From psc.dav at gmail.com  Sat Sep  2 16:45:47 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Sat, 2 Sep 2017 16:45:47 +0200
Subject: [FieldTrip] Fwd: single trials eLoreta
In-Reply-To: <CAK-BAC9v4U74suf_A4WNKKHRZtNM=zWWR7ZdpFSjQVzXrfp=uA@mail.gmail.com>
References: <CAK-BAC9v4U74suf_A4WNKKHRZtNM=zWWR7ZdpFSjQVzXrfp=uA@mail.gmail.com>
Message-ID: <CAK-BAC_sO4pJiSnNC1qY2EYdojO1XJgmmmhKmKGLLhF-F0QT_A@mail.gmail.com>

Dear fieldtrip experts,
I am posting again my questions, hoping that someone has experience with a
similar pipeline and can give some feedback.

I am trying to use the eLoreta method to get single-trial estimates of
source activity from specific ROIs, in the following way:

% eLORETA
cfg = [];
cfg.method                          = 'eloreta';
cfg.grid                                  = leadfield;
cfg.headmodel                       = headmodel;
cfg.eloreta.keepfilter              = 'yes';
cfg.eloreta.normalize               = 'yes';
cfg.eloreta.lambda                  = 0.05;
     *(1)
cfg.eloreta.projectnoise            = 'yes';
eLO_source                          = ft_sourceanalysis(cfg,data);

% in the above line, "data" is the results of ft_timelockanalysis
% with cfg.covariance = 'yes';
     *(2)

% then I put the source positions from the MNI template I
% used for the sourcemodel (see:
% http://www.fieldtriptoolbox.org/tutorial/sourcemodel#subject-
% specific_grids_that_are_equivalent_across_subjects_in_normalized_space
eLO_source.pos                      = template_grid.pos;
iPOS                                = eLO_source.pos;
iPOS(eLO_source.inside==0,:)        = NaN; % only points inside gray matter

% Then I select ROIs (here only one for simplicity) to extract single-trial
source activity:
[v,I]      = min(pdist2(iPOS, ROIs_mni , 'euclidean'));

% And I multiply the spatial filter for the EEG data in each trial
W            = eLO_source.avg.filter{I}; % filter at my ROI of interest
for tr = 1:size(data.trial,1)
       % loop over trials
     trials{tr} = W * squeeze(data.trial(tr,:,:));
   *(3)
end

Is this approach correct?
My main questions are:

*(1) Is there a way to select the best lambda parameter (e.g., selecting
the one that best approximates the activity at the EEG channels level)?

*(2) I am confused about the role of the covariance, since it doesn't seem
to be used when source activity is estimated using the set of spatial
filters at the single trial

*(3) Is the "trials{tr} = W * squeeze(data.trial(tr,:,:)); " approach
correct to get time-series of source activity in a ROI?

Looking forward for your feedback.

Best,
David


-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From juliacoopiza at gmail.com  Sun Sep  3 17:14:06 2017
From: juliacoopiza at gmail.com (Julia Coopi)
Date: Sun, 3 Sep 2017 09:14:06 -0600
Subject: [FieldTrip] Using PPC method
In-Reply-To: <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
Message-ID: <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have
any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu> wrote:

> Dear Tianyang,
>
> maybe it's a good idea to download the accompanying sample data from the
> tutorial and look if you can recreate the shown data structure. Then look
> closer into the values of the respective fields. That should give you a
> better grasp on what is required there.
>
> I have not fully looked into the code but my feeling is that
> spikeTrials.timestamp is not of any further use and is just carried from
> the data structure before (which was not cut into trials and where the raw
> timestamps were useful). The timing of spikes relative to the trial zero
> point is fully described in the fields ".time", ".trial" and ".trialtime".
> Best,
> Andreas
>
>
> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
> on behalf of 马天阳 [tianyangma2013 at gmail.com]
> *Sent:* Monday, August 28, 2017 5:31 PM
> *To:* FieldTrip discussion list
> *Subject:* Re: [FieldTrip] Using PPC method
>
> Dear Andreas,
>
> I still don't quite understand the tutorial.
>
> spikeTrials =
>          label: {'sig002a_wf'  'sig003a_wf'}
>      timestamp: {[1x83601 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
>       waveform: {[1x32x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>           unit: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>            hdr: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>         dimord: '{chan}_lead_time_spike'
>            cfg: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>           time: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>          trial: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>      trialtime: [600x2 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]
>
> Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?
>
> I feel I am quite lost.
>
> Best,
>
> Tianyang
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From k.lehongre-ihu at icm-institute.org  Mon Sep  4 11:26:52 2017
From: k.lehongre-ihu at icm-institute.org (Katia Lehongre)
Date: Mon, 4 Sep 2017 11:26:52 +0200
Subject: [FieldTrip] Workshop on intracranial recordings in human,
	October 3-4, Paris
Message-ID: <6eba6c50-169d-a0f0-ab1e-73d808e81d24@icm-institute.org>

Dear all,

The first*WIRED*(*W*orkshop on *I*ntracranial *R*ecordings in humans : 
*E*pilepsy, *D*BS), will be held in Paris, France, at the Brain and 
Spine Institute (ICM) on October 3 and 4.
Registration is opened and*free*.

Conferences, technical discussions, poster session (prize worth 800€ to 
be awarded), commercial solutions, wine and cheese…

  All information and registration to this event on our website: 
http://wired-icm.org <http://wired-icm.org/>

_Note that_ only *a few spots are left* for poster presentation and that 
we have reach *70% of full capacity* including people from 10 Parisian 
institutes, 6 major French cities and 9 countries.

We hope to see you there!
The organizers

_Organization_ : Katia Lehongre, Adrien Schramm –

_Scientific Committee_: Vincent Navarro, Katia Lehongre, Brian Lau, 
Nathalie George, Michel Le Van Quyen, Marie Laure Welter, Lionel Naccache



*_Program_*

*Tuesday October 3^rd *

*AM*

*Research Lecture Session*

**

9:00 *   – /Welcome breakfast/*

9:30*    –   Event Introduction* 
/V. Navarro,  K. Lehongre/

9:45*     –   Keynote : Epileptic ictal wavefront*

*/C. Schevon/ 
<http://columbianeurology.org/profile/caschevon>*/ (Columbia, USA)/

10:45 *– /Break/*

11:00*   –   DBS: Title to be determined*

/J. Bastin (Grenoble, France)/

11:45*   –   DBS: **Title to be determined*

12:30*    – /Lunch & Poster session/*

	

*PM*

*Methodological aspects*

**

14:00 *   –   Equipment : Focus on new electrodes* 
 /L. Valton/ 
<http://www.cerco.ups-tlse.fr/Luc-Valton>/ (Toulouse, France)/

15:00*    –   Analysis : Spike sorting techniques* 
 /F. Mormann/ 
<http://www.sfb1089.de/index.php/2014-01-16-17-35-38/principal-investigatiors/29-priv-doz-dr-dr-florian-mormann>/ (Bonn, 
Germany)/

16:00 *– /Coffe Break/*

16:30*   –   Imaging: Electrode localization*

*/S. Fernandez/ <http://www.cenir.org/>*/ (Paris, France/)

18:00*   –   Wine and Cheese session*

**

*Wednesday October 4^th *

*AM*

*Research Lecture Session*

8:45 *   – /Breakfast/*

9:00*    –   Keynote : Title to be determined*

*/P. Brown/ <https://www.ndcn.ox.ac.uk/team/peter-brown>*/ (Oxford, UK)/

10:00*   –   Memory encoding*

*/N. Axmacher/ 
<http://www.rd.ruhr-uni-bochum.de/neuro/wiss/pi/axmacher/index.html.en>*/ (Bochum, 
Germany)/

10:45 *–   Visual processing*

/L. Reddy / <http://cerco.ups-tlse.fr/%7Elreddy/Leila_Reddy/>/(Toulouse, 
France/)

11:30*   – /Coffee Break/*

11:45*   –   Focus: Imaging and electrophysiology*

/C. Ciumas-Gaumond (Lyon, France)/

12:45*    –  Conclusion & poster award*

	

*PM – Off event*

*Réunion du groupe Français Microelectrode*

*
*

*Note that *The *Blackrock Microsystems’ Clinical Electrophysiology 
Workshop* will occur on Monday 2nd. All info and registration on 
this satellite event click here 
<http://blackrockmicro.com/european-workshop>.

**

-- 
Katia Lehongre

Ingénieure de recherche IHU-A-ICM
PF STIM CENIR, bureau -1.041
ICM, UPMC/Inserm U1127/CNRS UMR7225
Institut du Cerveau et de la Moelle épinière
Hôpital Pitié-Salpêtrière
47 Boulevard de l'Hôpital
CS 21414
75646 PARIS CEDEX 13
tel: 01 57 27 47 14

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From linzhangysu at outlook.com  Mon Sep  4 13:09:45 2017
From: linzhangysu at outlook.com (linzhangysu at outlook.com)
Date: Mon, 4 Sep 2017 11:09:45 +0000
Subject: [FieldTrip] WPLI
Message-ID: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>


[cid:image002.png at 01D325AA.E537BE20]

I want to calculate the WPLI of 64 channels for one subject. But I met some questions.
Firstly, I didn’t understand the meaning of repetitions (just as the maker of the figure ). The dimension of repetitions was 1 in my MATLAB code , which resulted  in the WPLI result are NaN vectors. How can I solve the problem about ‘repetitions’?
I am looking forward to your reply very urgently, Thank you !

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From michelic72 at gmail.com  Mon Sep  4 13:19:27 2017
From: michelic72 at gmail.com (Cristiano Micheli)
Date: Mon, 4 Sep 2017 13:19:27 +0200
Subject: [FieldTrip] WPLI
In-Reply-To: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
References: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
Message-ID: <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>

Dear Linzhangysu

the wPLI metric requires you to have your experimental design matrix
organized in 'repetitions' or 'trials'.
This is typically the case (but not only) of an evoked related design,
where the repetitions dimension is used to calculate your 'average' wPLI
across trials, and this is what the FT code is doing for you in the
*ft_connectivity_wpli* function.
In summary, with this formula you will not be able to apply wPLI to a
single trial (e.g. like in resting state). If your experiment allows
organizing the experimental data into 'trials' (with the operation of
epoching) then you can solve your problem, otherwise you will have to use
other metrics of phase coupling.

IHTH
Cris Micheli



On Mon, Sep 4, 2017 at 1:09 PM, linzhangysu at outlook.com <
linzhangysu at outlook.com> wrote:

>
>
> [image: cid:image002.png at 01D325AA.E537BE20]
>
>
>
> I want to calculate the WPLI of 64 channels for one subject. But I met
> some questions.
>
> Firstly, I didn’t understand the meaning of repetitions (just as the
> maker of the figure ). The dimension of repetitions was 1 in my MATLAB code
> , which resulted  in the WPLI result are NaN vectors. How can I solve the
> problem about ‘repetitions’?
>
> I am looking forward to your reply very urgently, Thank you !
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From awutz at mit.edu  Mon Sep  4 14:24:47 2017
From: awutz at mit.edu (Andreas Wutz)
Date: Mon, 4 Sep 2017 12:24:47 +0000
Subject: [FieldTrip] Using PPC method
In-Reply-To: <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>,
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
Message-ID: <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>

Dear Julia,

I did not see your error message. Maybe, your lfp data structure is still in a continuous recording format without a trial definition?

________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Julia Coopi [juliacoopiza at gmail.com]
Sent: Sunday, September 03, 2017 11:14 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Tianyang,

maybe it's a good idea to download the accompanying sample data from the tutorial and look if you can recreate the shown data structure. Then look closer into the values of the respective fields. That should give you a better grasp on what is required there.

I have not fully looked into the code but my feeling is that spikeTrials.timestamp is not of any further use and is just carried from the data structure before (which was not cut into trials and where the raw timestamps were useful). The timing of spikes relative to the trial zero point is fully described in the fields ".time", ".trial" and ".trialtime".
Best,
Andreas


From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of 马天阳 [tianyangma2013 at gmail.com<mailto:tianyangma2013 at gmail.com>]
Sent: Monday, August 28, 2017 5:31 PM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

I still don't quite understand the tutorial.

spikeTrials =
         label: {'sig002a_wf'  'sig003a_wf'}
     timestamp: {[1x83601 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
      waveform: {[1x32x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
          unit: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
           hdr: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
        dimord: '{chan}_lead_time_spike'
           cfg: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
          time: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
         trial: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
     trialtime: [600x2 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]

Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?

I feel I am quite lost.

Best,

Tianyang

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From christine.blume at sbg.ac.at  Mon Sep  4 14:28:47 2017
From: christine.blume at sbg.ac.at (Blume Christine)
Date: Mon, 4 Sep 2017 12:28:47 +0000
Subject: [FieldTrip] Effect size measure for cluster-based
	permutation	tests
In-Reply-To: <CAGZVU0-MSDjvM3xZAAr9J5z25DD=ia+eFMAFPwnq30qGK-oeiQ@mail.gmail.com>
References: <E20FE863CD1C294AA55E415F4C446424C2B80B2D@xchmbx2.is.sbg.ac.at>
 <CAGZVU0-MSDjvM3xZAAr9J5z25DD=ia+eFMAFPwnq30qGK-oeiQ@mail.gmail.com>
Message-ID: <E20FE863CD1C294AA55E415F4C446424C2B8347B@xchmbx2.is.sbg.ac.at>

Hi Alik,

Thanks a lot for your suggestion, which I hoped would prompt more answers. Does anyone have suggestions on how exactly to implement the calculation of an effect size measure?

Best,
Christine

Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Alik Widge
Gesendet: Mittwoch, 23. August 2017 16:42
An: FieldTrip discussion list
Betreff: Re: [FieldTrip] Effect size measure for cluster-based permutation tests

My naive answer, which perhaps will provoke Eric to provide a better one: you have the actual  cluster statistic and its permutation distribution under the null hypothesis. It seems as though that distribution could be assumed Gaussian and something like Cohen's d calculated.

On Aug 23, 2017 9:35 AM, "Blume Christine" <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>> wrote:
Dear all,

I came across a question posted by someone about a year ago, which concerned effect size measures for cluster-based permutation tests. Unfortunately, the question does not seem to have been answered…

Q: I am using cluster-based permutation tests (depsamplesT, on time-frequency data) and am wondering how to best calculate an effect size from that.

Best,
Christine



_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From xiew1202 at gmail.com  Mon Sep  4 14:44:26 2017
From: xiew1202 at gmail.com (Xie Wanze)
Date: Mon, 04 Sep 2017 12:44:26 +0000
Subject: [FieldTrip] WPLI
In-Reply-To: <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>
References: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
 <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>
Message-ID: <CAA6G0Y87vgohT_W-vS-M4TUyoBdCPNBpokCVkUZy1XCt-Z5y1w@mail.gmail.com>

Dear Linzhang,
As Cris mentioned, you cannot calculate the WPLi value with one trial. The
WPLI toolbox calculates the CSD and PSD for each single trial, and then get
the "correlation" of the phase information across trials.  This apparently
could not be done with one trial.
If you have continuous data you may try to segment it into epochs.

Wanze


Cristiano Micheli <michelic72 at gmail.com>于2017年9月4日 周一上午7:35写道：

> Dear Linzhangysu
>
> the wPLI metric requires you to have your experimental design matrix
> organized in 'repetitions' or 'trials'.
> This is typically the case (but not only) of an evoked related design,
> where the repetitions dimension is used to calculate your 'average' wPLI
> across trials, and this is what the FT code is doing for you in the
> *ft_connectivity_wpli* function.
> In summary, with this formula you will not be able to apply wPLI to a
> single trial (e.g. like in resting state). If your experiment allows
> organizing the experimental data into 'trials' (with the operation of
> epoching) then you can solve your problem, otherwise you will have to use
> other metrics of phase coupling.
>
> IHTH
> Cris Micheli
>
>
>
> On Mon, Sep 4, 2017 at 1:09 PM, linzhangysu at outlook.com <
> linzhangysu at outlook.com> wrote:
>
>>
>>
>> [image: cid:image002.png at 01D325AA.E537BE20]
>>
>>
>>
>> I want to calculate the WPLI of 64 channels for one subject. But I met
>> some questions.
>>
>> Firstly, I didn’t understand the meaning of repetitions (just as the
>> maker of the figure ). The dimension of repetitions was 1 in my MATLAB code
>> , which resulted  in the WPLI result are NaN vectors. How can I solve the
>> problem about ‘repetitions’?
>>
>> I am looking forward to your reply very urgently, Thank you !
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From e.maris at donders.ru.nl  Tue Sep  5 14:12:52 2017
From: e.maris at donders.ru.nl (Maris, E.G.G. (Eric))
Date: Tue, 5 Sep 2017 12:12:52 +0000
Subject: [FieldTrip] Effect size measure for cluster-based	permutation
	tests
In-Reply-To: <mailman.12.1504605602.27164.fieldtrip@science.ru.nl>
References: <mailman.12.1504605602.27164.fieldtrip@science.ru.nl>
Message-ID: <59403DFC-9FBC-4585-928E-84787AE7E99F@donders.ru.nl>

Dear discussion list readers & contributors (especially Christine Blume),

There have been many questions (not only on the FT discussion list) about the calculation of effect size measures in the context of cluster-based permutation tests. I will continue my reply under the quotes below.


From: Blume Christine <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>>
Subject: Re: [FieldTrip] Effect size measure for cluster-based permutation tests
Date: 4 September 2017 at 14:28:47 GMT+2
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Reply-To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>


Hi Alik,

Thanks a lot for your suggestion, which I hoped would prompt more answers. Does anyone have suggestions on how exactly to implement the calculation of an effect size measure?

Best,
Christine

Von: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Alik Widge
Gesendet: Mittwoch, 23. August 2017 16:42
An: FieldTrip discussion list
Betreff: Re: [FieldTrip] Effect size measure for cluster-based permutation tests

My naive answer, which perhaps will provoke Eric to provide a better one: you have the actual  cluster statistic and its permutation distribution under the null hypothesis. It seems as though that distribution could be assumed Gaussian and something like Cohen's d calculated.

On Aug 23, 2017 9:35 AM, "Blume Christine" <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>> wrote:
Dear all,

I came across a question posted by someone about a year ago, which concerned effect size measures for cluster-based permutation tests. Unfortunately, the question does not seem to have been answered…

Q: I am using cluster-based permutation tests (depsamplesT, on time-frequency data) and am wondering how to best calculate an effect size from that.

Best,
Christine


Giving a useful answer to this question requires that one knows for what this effect size measure will be used. Typically, a standardised effect size measure is required to perform a power calculation. A power calculation is possible for a number of parametric statistical tests such as the T- and  the F-test. As input for this power calculation, Cohen’s d is required. A sensible value for Cohen’s d can sometimes be found in published studies (preferably with large sample sizes).

Cohen’s d can easily be obtained from the outcome of a cluster-based permutation test:

  1.  Calculate the non-standardised effect sizes by averaging the (sensor, frequency, time)-specific effects within the cluster of interest. Typically, the (sensor, frequency, time)-specific effects are raw differences between the subject averages for the experimental conditions that are being compared.
  2.  Calculate the standard deviation over the subjects of these non-standardised effect sizes.
  3.  Calculate Cohen’s d by dividing the grand average of the non-standardised effect sizes by the standard deviation obtained in 2.

Unfortunately, Cohen’s d calculated in this way, will be biased, and therefore cannot be used for a power calculation. This type of bias is sometimes denoted as “double dipping”.

In general, it is extremely challenging to perform a power calculations for statistical analyses that involve high-dimensional data. This does not only hold for electrophysiological, but also for fMRI data. To get idea about the difficulties that one encounters, have a look at this paper from the fMRI community: http://www.biorxiv.org/content/early/2016/04/20/049429. For the analysis of high-dimensional electrophysiological data, quite some statistical work still has to be done.

best,
Eric Maris












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From bioeng.yoosofzadeh at gmail.com  Wed Sep  6 04:22:10 2017
From: bioeng.yoosofzadeh at gmail.com (Vahab Yousofzadeh)
Date: Tue, 5 Sep 2017 22:22:10 -0400
Subject: [FieldTrip] Effect size measure for cluster-based
Message-ID: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>

Hi Christine,

Based on my understanding from the following link the effect size
(that correspond to the significant clusters) can not be derived from
p (or t)-values by
ft_sourcestatistics:
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test

Cheers,
Vahab


From christine.blume at sbg.ac.at  Wed Sep  6 09:10:25 2017
From: christine.blume at sbg.ac.at (Blume Christine)
Date: Wed, 6 Sep 2017 07:10:25 +0000
Subject: [FieldTrip] Effect size measure for cluster-based
In-Reply-To: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>
References: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>
Message-ID: <E20FE863CD1C294AA55E415F4C446424C2B83B02@xchmbx2.is.sbg.ac.at>

Dear all,

Thank you so much for all the suggestions and hints. I will look into them!

Best,
Christine


________________________________________
Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]&quot; im Auftrag von &quot;Vahab Yousofzadeh [bioeng.yoosofzadeh at gmail.com]
Gesendet: Mittwoch, 06. September 2017 04:22
An: fieldtrip at science.ru.nl
Betreff: Re: [FieldTrip] Effect size measure for cluster-based

Hi Christine,

Based on my understanding from the following link the effect size
(that correspond to the significant clusters) can not be derived from
p (or t)-values by
ft_sourcestatistics:
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test

Cheers,
Vahab
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip



From preted at mcmaster.ca  Wed Sep  6 16:48:26 2017
From: preted at mcmaster.ca (David)
Date: Wed, 6 Sep 2017 10:48:26 -0400
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>

Hello everyone, 

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I’ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I’m running into is stated below as well as my code and I’ve attached an image of what the data looks like. I’ve tried following the tutorials and searching through the mailing list and can’t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE: 
“Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);
  
Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);”



Thank you, 
David Prete
Ph.D. Candidate 
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour 
McMaster University 

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From jan.schoffelen at donders.ru.nl  Wed Sep  6 17:18:53 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Wed, 6 Sep 2017 15:18:53 +0000
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>
References: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570@donders.ru.nl>

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I’ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I’m running into is stated below as well as my code and I’ve attached an image of what the data looks like. I’ve tried following the tutorials and searching through the mailing list and can’t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
“Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);”



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From tokimoto at mejiro.ac.jp  Wed Sep  6 17:57:17 2017
From: tokimoto at mejiro.ac.jp (=?utf-8?B?5pmC5pys55yf5ZC+?=)
Date: Thu, 7 Sep 2017 00:57:17 +0900
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8@mejiro.ac.jp>

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp
******************************************

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From smoratti at psi.ucm.es  Wed Sep  6 18:25:54 2017
From: smoratti at psi.ucm.es (STEPHAN MORATTI)
Date: Wed, 6 Sep 2017 18:25:54 +0200
Subject: [FieldTrip] Learning agreementbuybr 8
In-Reply-To: <CAE20z9+OVvw9x6VbsgJzT4eQH9YNNxc09WzHMN3H522WadtHqg@mail.gmail.com>
References: <CAE20z9+OVvw9x6VbsgJzT4eQH9YNNxc09WzHMN3H522WadtHqg@mail.gmail.com>
Message-ID: <CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA@mail.gmail.com>

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es> escribió:

Buenos días Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
¿Serías tan amable de enviármelo firmado?

Gracias y perdón por las molestias,
Sara
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From preted at mcmaster.ca  Wed Sep  6 18:43:41 2017
From: preted at mcmaster.ca (David)
Date: Wed, 6 Sep 2017 12:43:41 -0400
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
References: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
Message-ID: <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>

Hi Jan-Mathijs, 

I’ve double checked and the label is written as ‘CZ’. So, it seems to be more than a typo, unfortunately. 

David 

From: fieldtrip-request at science.ru.nl
Sent: September 6, 2017 12:26 PM
To: fieldtrip at science.ru.nl
Subject: fieldtrip Digest, Vol 82, Issue 8

Send fieldtrip mailing list submissions to
	fieldtrip at science.ru.nl

To subscribe or unsubscribe via the World Wide Web, visit
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When replying, please edit your Subject line so it is more specific
than "Re: Contents of fieldtrip digest..."


Today's Topics:

   1. Re: Reshape Error Using ft_freqstatistics
      (Schoffelen, J.M. (Jan Mathijs))
   2. Cluster-based permutation tests for 3 conditions (????)
   3. Re: Learning agreementbuybr 8 (STEPHAN MORATTI)


----------------------------------------------------------------------

Message: 1
Date: Wed, 6 Sep 2017 15:18:53 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl>
Content-Type: text/plain; charset="utf-8"

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I?ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I?m running into is stated below as well as my code and I?ve attached an image of what the data looks like. I?ve tried following the tutorials and searching through the mailing list and can?t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
?Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);?



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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------------------------------

Message: 2
Date: Thu, 7 Sep 2017 00:57:17 +0900
From: ???? <tokimoto at mejiro.ac.jp>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp>
Content-Type: text/plain; charset="us-ascii"

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp
******************************************

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------------------------------

Message: 3
Date: Wed, 6 Sep 2017 18:25:54 +0200
From: STEPHAN MORATTI <smoratti at psi.ucm.es>
To: SARA RODRIGUEZ FREGENAL <srodri10 at ucm.es>, FieldTrip discussion
	list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] Learning agreementbuybr 8
Message-ID:
	<CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es> escribi?:

Buenos d?as Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
?Ser?as tan amable de envi?rmelo firmado?

Gracias y perd?n por las molestias,
Sara
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------------------------------

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

End of fieldtrip Digest, Vol 82, Issue 8
****************************************

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From jan.schoffelen at donders.ru.nl  Thu Sep  7 07:19:09 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 7 Sep 2017 05:19:09 +0000
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>
References: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
 <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>
Message-ID: <5038FBE1-5E7B-4F6B-B152-4A500879F002@donders.ru.nl>

In that case I recommend that you try and interpret the error message in a bit more detail. From the information you provide nobody can tell what’s going on, apart from the fact that it is a low-level matlab error. I suggest to use the matlab debugger to investigate the size of ‘meanreshapeddat', and the value of ‘nchan’ ‘nfreq’ ‘nrepl’ ‘ntime’ in this specific case.

And also think about your specification of cfg.latency. Note that you only include positive latencies to be tested, but you ask for actvsblT as a test statistic, which name suggests to use a baseline (i.e. require latencies of < 0 in the data).

JM


On 6 Sep 2017, at 18:43, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hi Jan-Mathijs,

I’ve double checked and the label is written as ‘CZ’. So, it seems to be more than a typo, unfortunately.

David

From: fieldtrip-request at science.ru.nl<mailto:fieldtrip-request at science.ru.nl>
Sent: September 6, 2017 12:26 PM
To: fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>
Subject: fieldtrip Digest, Vol 82, Issue 8

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Today's Topics:

   1. Re: Reshape Error Using ft_freqstatistics
      (Schoffelen, J.M. (Jan Mathijs))
   2. Cluster-based permutation tests for 3 conditions (????)
   3. Re: Learning agreementbuybr 8 (STEPHAN MORATTI)


----------------------------------------------------------------------

Message: 1
Date: Wed, 6 Sep 2017 15:18:53 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>>
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl<mailto:0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl>>
Content-Type: text/plain; charset="utf-8"

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca><mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I?ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I?m running into is stated below as well as my code and I?ve attached an image of what the data looks like. I?ve tried following the tutorials and searching through the mailing list and can?t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
?Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);?



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

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Message: 2
Date: Thu, 7 Sep 2017 00:57:17 +0900
From: ???? <tokimoto at mejiro.ac.jp<mailto:tokimoto at mejiro.ac.jp>>
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp<mailto:EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp>>
Content-Type: text/plain; charset="us-ascii"

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp<mailto:tokimoto at mejiro.ac.jp>
******************************************

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------------------------------

Message: 3
Date: Wed, 6 Sep 2017 18:25:54 +0200
From: STEPHAN MORATTI <smoratti at psi.ucm.es<mailto:smoratti at psi.ucm.es>>
To: SARA RODRIGUEZ FREGENAL <srodri10 at ucm.es<mailto:srodri10 at ucm.es>>, FieldTrip discussion
                list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Learning agreementbuybr 8
Message-ID:
                <CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com<mailto:CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com>>
Content-Type: text/plain; charset="utf-8"

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es<mailto:srodri10 at ucm.es>> escribi?:

Buenos d?as Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
?Ser?as tan amable de envi?rmelo firmado?

Gracias y perd?n por las molestias,
Sara
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------------------------------

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End of fieldtrip Digest, Vol 82, Issue 8
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From nugenta at mail.nih.gov  Thu Sep  7 03:02:10 2017
From: nugenta at mail.nih.gov (Nugent, Allison C. (NIH/NIMH) [E])
Date: Thu, 7 Sep 2017 01:02:10 +0000
Subject: [FieldTrip] 2nd MEG North America Meeting - Abstract Submission
	Reminder
Message-ID: <MWHPR09MB12162641EDE3ED43AEDC65DD80940@MWHPR09MB1216.namprd09.prod.outlook.com>

This is a reminder that we are currently accepting abstracts for the 2nd MEG-North America meeting, to be held in Bethesda, Maryland November 8th and 9th. Committee meetings will be held on November 8th, with the scientific session on November 9th.



There will be a poster session and oral sessions on November 9th.  We are attempting to secure funding to present several speaker honoraria for excellent abstracts submitted by early career investigators and trainees.  If you would like to be considered for this, please indicate your preference, along with your position, on your abstract submission.



Abstracts may be submitted any time up until Wednesday, September 13th at 11:59pm, directly to NIHMEGworkshop at gmail.com<mailto:NIHMEGworkshop at gmail.com> (note the deadline has been extended).



Please visit our website at https://megworkshop.nih.gov/MEGWorkshop/  - We have added additional information regarding the program!



Or register at: https://www.eventbrite.com/e/meg-north-america-2017-tickets-36315511673<https://www.eventbrite.com/e/meg-north-america-2017-tickets-36315511673?aff=utm_source%3Deb_email%26utm_medium%3Demail%26utm_campaign%3Dnew_event_email&utm_term=eventurl_text>



We hope to see you there!



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From alice.bollini at yahoo.com  Thu Sep  7 10:18:22 2017
From: alice.bollini at yahoo.com (Alice B)
Date: Thu, 7 Sep 2017 08:18:22 +0000 (UTC)
Subject: [FieldTrip] Source reconstruction issues
References: <283732377.7662318.1504772302305.ref@mail.yahoo.com>
Message-ID: <283732377.7662318.1504772302305@mail.yahoo.com>

Hello everyone,
I would like to use fieldtrip for extracting source activity from specific ROIs (using the eLoreta approach).
Here is my script, there are few things I am not sure in the pipeline (marked with numbers on the right)
% eLORETA cfg = []; cfg.method                          = 'eloreta';cfg.grid                                  = leadfield;cfg.headmodel                       = headmodel;cfg.eloreta.keepfilter              = 'yes';cfg.eloreta.normalize               = 'yes';cfg.eloreta.lambda                  = 0.05;                                      *(1)cfg.eloreta.projectnoise            = 'yes';eLO_source                          = ft_sourceanalysis(cfg,data);
% in the above line, "data" is the results of ft_timelockanalysis% with cfg.covariance = 'yes';                                                  *(2)
% then I put the source positions from the MNI template% used for the sourcemodel (http://www.fieldtriptoolbox.org/tutorial/sourcemodel#subject-specific_grids_that_are_equivalent_across_subjects_in_normalized_space)eLO_source.pos                      = template_grid.pos;iPOS                                        = eLO_source.pos;iPOS(eLO_source.inside==0,:)        = NaN; % only points inside gray matter
% Then I select ROIs (here only one for simplicity) to extract single-trial source activity:[v,I]       = min(pdist2(iPOS, ROIs_mni , 'euclidean'));
% And I multiply the spatial filter for the EEG data in each trialW            = eLO_source.avg.filter{I}; % filter at my ROI of interestfor tr = 1:size(data.trial,1)       % loop over trials         trials{tr} = W * squeeze(data.trial(tr,:,:));                            *(3)end
Is this approach correct?My main questions are:
*(1) Is there a way to select the best lambda parameter (e.g., selecting the one that best approximates the activity at the EEG channels level)?
*(2) I am confused about the role of the covariance, since it doesn't seem to be used when source activity is estimated using the set of spatial filters at the single trial
*(3) Is the "trials{tr} = W * squeeze(data.trial(tr,:,:)); " approach correct to get time-series of source activity in a ROI?

Best,Alice
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From da401 at kent.ac.uk  Thu Sep  7 13:53:20 2017
From: da401 at kent.ac.uk (D.Abdallah)
Date: Thu, 7 Sep 2017 11:53:20 +0000
Subject: [FieldTrip] Question about MVPA topographic map
Message-ID: <1504785200819.38370@kent.ac.uk>

Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.

I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING

%Reading the data

cfg = [];

cfg.dataset = filename1;

cfg.reref = 'yes';

cfg.channel = {'Cz', 'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'};

cfg.refchannel = 'Cz';

cfg.demean = 'yes';

data_eeg1 = ft_preprocessing(cfg);

%Segmenting data

cfg.trialdef.eventtype = '?';

Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;

cfg.trialdef.poststim = 0.6;

cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype = 'STATUS';

cfg.trialdef.eventvalue = [100];

stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];

FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];

VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers

stimulusTrigger = 100;

faceResponseTrigger = 1;

vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing

[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';

FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing

[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';

Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP

FaceTrials.reref = 'no';

FaceTrials.keeptrials = 'yes'; % classifiers operate on individual trials

FaceTrials.channel = {'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'}; % occipital channels only

FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP

VaseTrials.reref = 'no';

VaseTrials.keeptrials = 'yes'; % classifiers operate on individual trials

VaseTrials.channel = {'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'}; % occipital channels only

VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA

cfg.layout = 'biosemi64.lay';

cfg.method = 'crossvalidate';

cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];

cfg.nfolds = 4;

cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy' 'binomial' 'contingency'};

stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);

stat.statistic.contingency

%Plot MVPA Results

stat.mymodel = stat.model{2}.primal;

cfg.parameter = 'mymodel';

cfg.xlim = [-0.1 0.6];

cfg.comments = '';

cfg.colorbar = 'yes';

cfg.interplimits= 'electrodes';

ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.

We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).

Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,

Diane Abdallah


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From Patrick.Rollo at uth.tmc.edu  Fri Sep  8 20:38:22 2017
From: Patrick.Rollo at uth.tmc.edu (Rollo, Patrick)
Date: Fri, 8 Sep 2017 18:38:22 +0000
Subject: [FieldTrip] Job Posting on FieldTrip message board
Message-ID: <6484964699b24059ba1dd00807892d06@uth.tmc.edu>

FieldTrip Moderators,

I have a job posting that I would like to make on this message board, our lab, Tandon Lab, has posted in the past. The advert is attached here. Please let me know if you have any questions,


Thank you,


Patrick Rollo
Research Assistant
Department of Neurosurgery
UTHealth McGovern Medical School at Houston
6431 Fannin St MSB G.550G Houston TX 77030
phone: 713-500-5475


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From lxykh0700073 at outlook.com  Sun Sep 10 03:53:24 2017
From: lxykh0700073 at outlook.com (Xinyi Li)
Date: Sun, 10 Sep 2017 01:53:24 +0000
Subject: [FieldTrip] mixed design permutation tests on time-frequency data?
Message-ID: <HK2PR06MB070552220B0902408B2D56C8816B0@HK2PR06MB0705.apcprd06.prod.outlook.com>

Hi all,


I'm wondering how to do a mixed design permutation test on time-frequency data, specifically, how to specify the design matrix.

I followed the instructions in this post https://mailman.science.ru.nl/pipermail/fieldtrip/2008-March/001500.html for design matrix, but got an error 'the design matrix variables should be constant within a block'.


Any suggestions?


Thanks!

Xinyi
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From Miguel.Granjaespiritosanto at nottingham.ac.uk  Mon Sep 11 11:19:13 2017
From: Miguel.Granjaespiritosanto at nottingham.ac.uk (Miguel Granja Espirito Santo)
Date: Mon, 11 Sep 2017 09:19:13 +0000
Subject: [FieldTrip] Any update on the group-level source MNE stats?
Message-ID: <AM2PR06MB086699834D86F136DC514BFE83680@AM2PR06MB0866.eurprd06.prod.outlook.com>

Hi fieldtripers,


I was wondering if there is any update on doing group level stats after conducting an MNE source analysis. I found several threads on the mailings list which I have successfully replicated, but I was wondering if there is any official FT approved way.


At the end of the MNE page it says that this is under development, so is anyone privy to what the status of documentation is?

Just asking because of supervisor enquiry for publication of our results.


Best,

Miguel
PhD Student
School of Psychology
University of Nottingham





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From Manuel.Bange at unimedizin-mainz.de  Mon Sep 11 14:32:57 2017
From: Manuel.Bange at unimedizin-mainz.de (Bange, Manuel)
Date: Mon, 11 Sep 2017 12:32:57 +0000
Subject: [FieldTrip] Time normalisation for trials of different lenghts
Message-ID: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>

Dear fieldtrip community,

My name is Manuel Bange and I am working in the Movement Disorder and Neurostimulation Lab in Mainz, Germany. Currently I am working on a project where we recorded EEG and EMG data combined with simultaneously recorded ground reaction forces during walking on a treadmill. I intend to use complete gait cycles as trials in order to, for example, calculate the inter-trial coherence. The issue I now face is that every cycle lasts a different amount of time.
In a first step I have separated the continuous data into trials that correspond to whole gait cycles. One cycle starts with the onset of the right foot and ends one sample before the following onset of the same foot. This results in a number of trials (around 18 per subject) of different lengths.
Following this, I performed a time-frequency analysis for each individual trial by


1.       selecting a specific trial

cfg.trials      = 2;                                                               % select a a trial, here trial 2

trial2 = ft_selectdata(cfg, data)



2.       performing time-frequency analysis

cfg          = [];

cfg.output   = 'pow';

cfg.method   = 'mtmconvol';

cfg.taper    = 'hanning';

cfg.foi      = [1:1:40];

cfg.t_ftimwin    = ones(length(cfg.foi),1).*1;

cfg.toi          = trial2.time{1,1}(1,125):0.01:trial2.time{1,1}(1,end-125-1);

cfg.keeptrials = 'yes'

data_tfa1     = ft_freqanalysis(cfg, trial2);
resulting in
  data_tfa1 =
  struct with fields:
label: {257×1 cell}
dimord: 'rpt_chan_freq_time'
freq: [1×40 double]
time: [1×172 double]                                                                    % differs for each trial
powspctrm: [1×257×40×172 double]
cumtapcnt: [1×40 double]
cfg: [1×1 struct]

Now my question is:
Is there a way to normalise this data over the time-axis, so that all trials have the same length? This is important to calculate an average time-frequency representation, or in another step, to calculate the inter-trial coherence. I have thought of normalising the time axis and interpolating the corresponding power-values.

Thanks and best regards,

Manuel Bange
M.Sc. Sports Science


Johannes-Gutenberg-University Hospital
Movement Disorders and Neurostimulation
Department of Neurology
Langenbeckstr. 1
55131 Mainz, Germany
www.unimedizin-mainz.de

Email: manuel.bange at unimedizin-mainz.de

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From juliacoopiza at gmail.com  Mon Sep 11 15:24:35 2017
From: juliacoopiza at gmail.com (Julia Coopi)
Date: Mon, 11 Sep 2017 07:24:35 -0600
Subject: [FieldTrip] Using PPC method
In-Reply-To: <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>
Message-ID: <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>

Dear Andreas,

Finally, I managed to get PPC result from fliedtrip, now I have a problem:
I am using mtmfft as method I wnat to have fine frequency resolution
atleast I wan to have a point for each 1 hz. I have used
 cfg.foi       =2:1:80;

But it did't work, my output has a frequncy vector like this:[ 5 10 15 20
... 80];

do you have any suggestion for better frequency resolution.
If any body has a suggestion, that woulb be great to share it.

Thanks,
Julia

On Mon, Sep 4, 2017 at 6:24 AM, Andreas Wutz <awutz at mit.edu> wrote:

> Dear Julia,
>
> I did not see your error message. Maybe, your lfp data structure is still
> in a continuous recording format without a trial definition?
>
> ------------------------------
> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
> on behalf of Julia Coopi [juliacoopiza at gmail.com]
> *Sent:* Sunday, September 03, 2017 11:14 AM
>
> *To:* FieldTrip discussion list
> *Subject:* Re: [FieldTrip] Using PPC method
>
> Dear Andreas,
>
> Thanks for your response, I am going through your suggestion. did you have
> any problem regarding the appending spikes and lfp. I got this error:
>
> Error using ft_appendspike (line 112)
> could not find the trial information in the continuous data
>
> thanks.
> Julia
>
> On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu> wrote:
>
>> Dear Tianyang,
>>
>> maybe it's a good idea to download the accompanying sample data from the
>> tutorial and look if you can recreate the shown data structure. Then look
>> closer into the values of the respective fields. That should give you a
>> better grasp on what is required there.
>>
>> I have not fully looked into the code but my feeling is that
>> spikeTrials.timestamp is not of any further use and is just carried from
>> the data structure before (which was not cut into trials and where the raw
>> timestamps were useful). The timing of spikes relative to the trial zero
>> point is fully described in the fields ".time", ".trial" and ".trialtime".
>> Best,
>> Andreas
>>
>>
>> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
>> on behalf of 马天阳 [tianyangma2013 at gmail.com]
>> *Sent:* Monday, August 28, 2017 5:31 PM
>> *To:* FieldTrip discussion list
>> *Subject:* Re: [FieldTrip] Using PPC method
>>
>> Dear Andreas,
>>
>> I still don't quite understand the tutorial.
>>
>> spikeTrials =
>>          label: {'sig002a_wf'  'sig003a_wf'}
>>      timestamp: {[1x83601 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
>>       waveform: {[1x32x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>           unit: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>            hdr: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>>         dimord: '{chan}_lead_time_spike'
>>            cfg: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>>           time: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>          trial: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>      trialtime: [600x2 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]
>>
>> Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?
>>
>> I feel I am quite lost.
>>
>> Best,
>>
>> Tianyang
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From awutz at mit.edu  Mon Sep 11 15:57:02 2017
From: awutz at mit.edu (Andreas Wutz)
Date: Mon, 11 Sep 2017 13:57:02 +0000
Subject: [FieldTrip] Using PPC method
In-Reply-To: <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>,
 <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>
Message-ID: <E117808318BC9745BF72B56E5563172A44579AF3@OC11EXPO29.exchange.mit.edu>

Dear Julia,

your frequency resolution depends on the time window you give to the FFT (cfg.timwin). Increasing that window will increase your freq resolution.


________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Julia Coopi [juliacoopiza at gmail.com]
Sent: Monday, September 11, 2017 9:24 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Finally, I managed to get PPC result from fliedtrip, now I have a problem:  I am using mtmfft as method I wnat to have fine frequency resolution atleast I wan to have a point for each 1 hz. I have used
 cfg.foi       =2:1:80;

But it did't work, my output has a frequncy vector like this:[ 5 10 15 20 ... 80];

do you have any suggestion for better frequency resolution.
If any body has a suggestion, that woulb be great to share it.

Thanks,
Julia

On Mon, Sep 4, 2017 at 6:24 AM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Julia,

I did not see your error message. Maybe, your lfp data structure is still in a continuous recording format without a trial definition?

________________________________
From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of Julia Coopi [juliacoopiza at gmail.com<mailto:juliacoopiza at gmail.com>]
Sent: Sunday, September 03, 2017 11:14 AM

To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Tianyang,

maybe it's a good idea to download the accompanying sample data from the tutorial and look if you can recreate the shown data structure. Then look closer into the values of the respective fields. That should give you a better grasp on what is required there.

I have not fully looked into the code but my feeling is that spikeTrials.timestamp is not of any further use and is just carried from the data structure before (which was not cut into trials and where the raw timestamps were useful). The timing of spikes relative to the trial zero point is fully described in the fields ".time", ".trial" and ".trialtime".
Best,
Andreas


From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of 马天阳 [tianyangma2013 at gmail.com<mailto:tianyangma2013 at gmail.com>]
Sent: Monday, August 28, 2017 5:31 PM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

I still don't quite understand the tutorial.

spikeTrials =
         label: {'sig002a_wf'  'sig003a_wf'}
     timestamp: {[1x83601 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
      waveform: {[1x32x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
          unit: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
           hdr: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
        dimord: '{chan}_lead_time_spike'
           cfg: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
          time: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
         trial: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
     trialtime: [600x2 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]

Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?

I feel I am quite lost.

Best,

Tianyang

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From nasseroleslami at gmail.com  Mon Sep 11 19:09:52 2017
From: nasseroleslami at gmail.com (Bahman Nasseroleslami)
Date: Mon, 11 Sep 2017 18:09:52 +0100
Subject: [FieldTrip] Research Assistant in Position Neural Engineering
 Position - Trinity College Dublin, the University of Dublin, Dublin, Ireland
Message-ID: <CAGgfpUYU1EwV3zqdznbAg-LQeZmKR4iVR4P7sjs=fQELTGK+kQ@mail.gmail.com>

Dear all,

There is a research assistant position available in Academic Unit of
Neurology, Trinity College Dublin, the University of Dublin, Dublin,
Ireland.

---------------------------------------
Job ID : 032518
Post Title: Research Assistant in Neural Engineering
Post Status: 12 month contract, full-time
Research Group / Department / School Academic Unit of Neurology, School of
Medicine, Trinity College Dublin, the University of Dublin
Location: Trinity Biomedical Sciences Institute, Trinity College Dublin,
the University of Dublin, College Green, Dublin D02 R590, Ireland
Reports to: Professor Orla Hardiman/Dr Bahman Nasseroleslami
Salary: Research Assistant Level based on Irish Universities Association
(IUA) Guideline, Point 1: €21,459 per annum (or above commensurate with
experience).
Closing Date: 5pm on Wednesday 27th September 2017


Please note that Garda vetting will be sought in respect of individuals who
come under consideration for a post.

Post Summary

Applications are invited for a motivated and self-driven individual for the
position of research assistant with the Irish ALS Research Group, hosted in
the Trinity Biomedical Sciences Institute (TBSI)'s Academic Unit of
Neurology. The ideal candidate will have an undergraduate or master's
degree in engineering, bioengineering, mathematics, computational biology,
or a cognate area. Familiarity with and/or the ability to quickly acquire
skills in electrophysiological recordings and analysis (e.g. EEG/EMG),
would be highly desirable as would a knowledge of computer programming
(MATLAB).
---------------------------------------

The detailed job description file (PDF) and the application instructions
can be found online at http://jobs.tcd.ie.

It would be really appreciated if you could share this with those that may
be interested.

Sincerely
Bahman

––––––––––––
Bahman Nasseroleslami
Senior Research Fellow, IRC Postdoctoral Fellow
Academic Unit of Neurology, School of Medicine
Trinity College Dublin, the University of Dublin
Dublin 2, Ireland.

Room 5.43, Trinity Biomedical Sciences Institute
152-160 Pearse Street, Dublin D02 R590, Ireland.
nasserob at tcd.ie, nasseroleslami at gmail.com
www.tcd.ie

Trinity College Dublin, the University of Dublin is ranked 1st in Ireland
and in the top 100 world universities by the QS World University Rankings.
––––––––––––
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From michak at is.umk.pl  Mon Sep 11 23:16:54 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Mon, 11 Sep 2017 23:16:54 +0200
Subject: [FieldTrip] MRI low contrast
Message-ID: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>

Dear Fieldtrippers,

How to correct low contrast in MR image when using ft_mri_read and
ft_sourceplot to read and display MR image? (see attachment
mri00_lowctrst.png)

For comparison, same .nii opened with mricron software (
http://people.cas.sc.edu/rorden/mricron/install.html) displays with proper
contrast (see attachment mri00_hictrst.png)

Code:

ss = 'sub1';
f = ['../data/ind/', ss, '/mri/mri00.nii'];
mri00 = ft_read_mri(f)
ft_sourceplot([],mri00)


Thank you in advance !

Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics
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From a.stolk8 at gmail.com  Mon Sep 11 23:48:47 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Mon, 11 Sep 2017 14:48:47 -0700
Subject: [FieldTrip] MRI low contrast
In-Reply-To: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
References: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
Message-ID: <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>

Hi Michal,

There's a (undocumented) keyboard shortcut, shift+equal sign (numpad +) to
adjust the contrast scaling (use numpad - for the opposite direction).

Perhaps ft_sourceplot should additionally require the same cfg.lim option
that for instance ft_determine_coordsys uses. Will propose in a PR.

Best,
Arjen




On Mon, Sep 11, 2017 at 2:16 PM, Michał Komorowski <michak at is.umk.pl> wrote:

> Dear Fieldtrippers,
>
> How to correct low contrast in MR image when using ft_mri_read and
> ft_sourceplot to read and display MR image? (see attachment
> mri00_lowctrst.png)
>
> For comparison, same .nii opened with mricron software (
> http://people.cas.sc.edu/rorden/mricron/install.html) displays with
> proper contrast (see attachment mri00_hictrst.png)
>
> Code:
>
> ss = 'sub1';
> f = ['../data/ind/', ss, '/mri/mri00.nii'];
> mri00 = ft_read_mri(f)
> ft_sourceplot([],mri00)
>
>
> Thank you in advance !
>
> Michał Komorowski, MSc
> Nicolaus Copernicus University in Toruń
> Faculty of Physics, Astronomy and Informatics
> Department of Informatics
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From anne.urai at gmail.com  Tue Sep 12 09:01:03 2017
From: anne.urai at gmail.com (Anne Urai)
Date: Tue, 12 Sep 2017 09:01:03 +0200
Subject: [FieldTrip] BIDS data format survey
Message-ID: <CAFGbMjQ9T+yXuHSqW0AZ3vHQrX29pzuMS9OoEEp+hbT9Pu7Vzw@mail.gmail.com>

Dear FieldTrippers,

Recently, a number of people have been working on developing a common data
standard for MEG called BIDS (Brain Imaging Data Structure).

They are now requesting community feedback, so please have a look at the
brief survey here and help them out: https://t.co/BjAFmR7yhN

*Magnetoencephalography (MEG) studies produce enormous amounts of data that
need to be stored, organized and analyzed. However, naming conventions and
metadata are often incomplete or inexistent, which is an impediment to the
transfer of scientific data and knowledge, the reproducibility of research
results, and the curation of large data repositories with entries from
heterogenous origins. *


*Building on recent efforts from the MRI community, MEG-BIDS is a
proposition to standardize the arrangement of data structures in
MEG. Please refer to the MEG-BIDS manuscript and to the MEG-BIDS detailed
specifications for all details concerning the proposed structure:- MEG-BIDS
Manuscript:
<https://www.google.com/url?q=http://www.biorxiv.org/content/early/2017/08/08/172684&sa=D&ust=1505157728861000&usg=AFQjCNFOvjNkNYmsyA0_EBvZ0KyQ9n1mvA>http://www.biorxiv.org/content/early/2017/08/08/172684
<http://www.biorxiv.org/content/early/2017/08/08/172684>*
*- MEG-BIDS Specifications:
<https://www.google.com/url?q=http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf&sa=D&ust=1505157728861000&usg=AFQjCNHZY8Z-YO0UQu61iEnkxMFq7DQZUQ>http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf
<http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf>*

*We wish to survey the MEG community on its present needs with respect to
data management, and design the MEG-BIDS standard to best respond to these
presently unmet needs.*

*Your participation is truly appreciated.*

... <bloop://bloop_expand>

—
Anne E. Urai, MSc
PhD student | Institut für Neurophysiologie und Pathophysiologie
Universitätsklinikum Hamburg-Eppendorf | Martinistrasse 52, 20246 |
Hamburg, Germany
www.anneurai.net / @AnneEUrai
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From michak at is.umk.pl  Tue Sep 12 09:56:58 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Tue, 12 Sep 2017 09:56:58 +0200
Subject: [FieldTrip] MRI low contrast
In-Reply-To: <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>
References: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
 <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>
Message-ID: <CAEJSOqhuxSn73P1fuj9AmRXNXKR=h6T7q9zLfuK0T7abv+CLkw@mail.gmail.com>

Yaay !

+ and - works  ! :D


For determining coordsys one should type:

% clim adjusts contrast (default [0 1], the lower the brighter)
[dataout] = ft_determine_coordsys(mri00, 'clim', [0 0.25])


Thank you very much !

Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics

2017-09-11 23:48 GMT+02:00 Arjen Stolk <a.stolk8 at gmail.com>:

> Hi Michal,
>
> There's a (undocumented) keyboard shortcut, shift+equal sign (numpad +) to
> adjust the contrast scaling (use numpad - for the opposite direction).
>
> Perhaps ft_sourceplot should additionally require the same cfg.lim option
> that for instance ft_determine_coordsys uses. Will propose in a PR.
>
> Best,
> Arjen
>
>
>
>
> On Mon, Sep 11, 2017 at 2:16 PM, Michał Komorowski <michak at is.umk.pl>
> wrote:
>
>> Dear Fieldtrippers,
>>
>> How to correct low contrast in MR image when using ft_mri_read and
>> ft_sourceplot to read and display MR image? (see attachment
>> mri00_lowctrst.png)
>>
>> For comparison, same .nii opened with mricron software (
>> http://people.cas.sc.edu/rorden/mricron/install.html) displays with
>> proper contrast (see attachment mri00_hictrst.png)
>>
>> Code:
>>
>> ss = 'sub1';
>> f = ['../data/ind/', ss, '/mri/mri00.nii'];
>> mri00 = ft_read_mri(f)
>> ft_sourceplot([],mri00)
>>
>>
>> Thank you in advance !
>>
>> Michał Komorowski, MSc
>> Nicolaus Copernicus University in Toruń
>> Faculty of Physics, Astronomy and Informatics
>> Department of Informatics
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From behinger at uos.de  Tue Sep 12 20:14:22 2017
From: behinger at uos.de (Benedikt Ehinger)
Date: Tue, 12 Sep 2017 20:14:22 +0200
Subject: [FieldTrip] Time normalisation for trials of different lenghts
In-Reply-To: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>
References: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>
Message-ID: <1311033b-5017-443e-9cd9-0f4e4486f112@uos.de>

Dear Manuel,

first off, I do not know if or how you can do this in fieldtrip. But in
eeglab you can do something they call "timewarping". One calculates a
time-frequency (TF) decomposition for each trial and then
warps/interpolates the TF so that some given events align. This is very
similar (identical?) to what you describe and you might find more
information either in the papers or the eeglab implementation. The
method has been described in Gwin 2010 [1] and we also used it in on of
our own studies [2].

Whether you can do the same also for phase (=> coherence) I don't know.

I hope that helps in your analysis.
Best, Benedikt

[1] https://www.ncbi.nlm.nih.gov/pubmed/20410364
[2] https://www.ncbi.nlm.nih.gov/pubmed/24616681


Am 11.09.2017 um 14:32 schrieb Bange, Manuel:
> Dear fieldtrip community,
> 
>  
> 
> My name is Manuel Bange and I am working in the Movement Disorder and
> Neurostimulation Lab in Mainz, Germany. Currently I am working on a
> project where we recorded EEG and EMG data combined with simultaneously
> recorded ground reaction forces during walking on a treadmill. I intend
> to use complete gait cycles as trials in order to, for example,
> calculate the inter-trial coherence. The issue I now face is that every
> cycle lasts a different amount of time.
> 
> In a first step I have separated the continuous data into trials that
> correspond to whole gait cycles. One cycle starts with the onset of the
> right foot and ends one sample before the following onset of the same
> foot. This results in a number of trials (around 18 per subject) of
> different lengths.
> 
> Following this, I performed a time-frequency analysis for each
> individual trial by
> 
>  
> 
> 1.       selecting a specific trial
> 
> /cfg.trials      = 2;
>                                                               % select a
> a trial, here trial 2/
> 
> /trial2 = ft_selectdata(cfg, data)/
> 
> / /
> 
> 2.       performing time-frequency analysis
> 
> /cfg          = [];/
> 
> /cfg.output   = 'pow';/
> 
> /cfg.method   = 'mtmconvol';/
> 
> /cfg.taper    = 'hanning';/
> 
> /cfg.foi      = [1:1:40];/
> 
> /cfg.t_ftimwin    = ones(length(cfg.foi),1).*1;/
> 
> /cfg.toi          =
> trial2.time{1,1}(1,125):0.01:trial2.time{1,1}(1,end-125-1);            /
> 
> /cfg.keeptrials = 'yes'/
> 
> /data_tfa1     = ft_freqanalysis(cfg, trial2);/
> 
> /resulting in/
> 
> /  data_tfa1 = /
> 
> /  struct with fields:/
> 
> /label: {257×1 cell}/
> 
> /dimord: 'rpt_chan_freq_time'/
> 
> /freq: [1×40 double]/
> 
> /time: [1×172
> double]                                                                   
> % differs for each trial/
> 
> /powspctrm: [1×257×40×172 double]/
> 
> /cumtapcnt: [1×40 double]/
> 
> /cfg: [1×1 struct]/
> 
>  
> 
> Now my question is:
> 
> Is there a way to normalise this data over the time-axis, so that all
> trials have the same length? This is important to calculate an average
> time-frequency representation, or in another step, to calculate the
> inter-trial coherence. I have thought of normalising the time axis and
> interpolating the corresponding power-values.
> 
>  
> 
> Thanks and best regards,
> 
>  
> 
> Manuel Bange
> 
> M.Sc. Sports Science
> 
>  
> 
>  
> 
> Johannes-Gutenberg-University Hospital
> 
> Movement Disorders and Neurostimulation
> 
> Department of Neurology
> 
> Langenbeckstr. 1
> 
> 55131 Mainz, Germany
> 
> www.unimedizin-mainz.de
> 
>  
> 
> Email: manuel.bange at unimedizin-mainz.de
> 
>  
> 
> 
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 


From psc.dav at gmail.com  Tue Sep 12 20:47:15 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Tue, 12 Sep 2017 20:47:15 +0200
Subject: [FieldTrip] inverse imaging
Message-ID: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>

Dear fieldtrip experts,

I was wondering if anyone has experience with extracting single trials
estimates of source activity (using MNE or Loreta-based approaches) from
regions of interest, and what would be the best procedure…


Thanks in advance
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From julian.keil at gmail.com  Wed Sep 13 12:22:01 2017
From: julian.keil at gmail.com (Julian Keil)
Date: Wed, 13 Sep 2017 12:22:01 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
Message-ID: <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>

Hi David,

do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors <http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors>
In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.

Good luck,

Julian


> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
> 
> Dear fieldtrip experts,
> 
> I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…
> 
> 
> 
> Thanks in advance
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From psc.dav at gmail.com  Wed Sep 13 13:22:46 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Wed, 13 Sep 2017 13:22:46 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
Message-ID: <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>

Thaks Julian,
that is the approach I was using, with eLoreta.
I am not sure about two steps,though.
One is the estimate and use of the signal covariance to input for single-trial
activity in source space.
The other is the choice of the optimal lambda.

If you have some advice, that wold be very helpful.

Thanks,
David

2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com>:

> Hi David,
>
> do you want to obtain single-trial activity in source space? In that case,
> have you looked at the „virtual sensors“-tutorial? http://www.
> fieldtriptoolbox.org/tutorial/shared/virtual_sensors
> In the tutorial, LCMV is used for the source analysis, but it should also
> work with sloreta, as the output-structure of the source-analysis is
> identical. I’m not sure about MNE though.
>
> Good luck,
>
> Julian
>
>
> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
>
> Dear fieldtrip experts,
>
> I was wondering if anyone has experience with extracting single trials
> estimates of source activity (using MNE or Loreta-based approaches) from
> regions of interest, and what would be the best procedure…
>
>
> Thanks in advance
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From julian.keil at gmail.com  Wed Sep 13 13:41:22 2017
From: julian.keil at gmail.com (Julian Keil)
Date: Wed, 13 Sep 2017 13:41:22 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
 <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
Message-ID: <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>

Hi David,

regarding the lambda, I think there are different ideas floating around the fieldtrip discussion-list. I suggest searching for the term „lambda“ to get a rough idea. Personally, for our EEG-data I usually use 10%.

What is your question exactly regarding the covariance as input?

Cheers,

Julian

> Am 13.09.2017 um 13:22 schrieb David Pascucci <psc.dav at gmail.com>:
> 
> Thaks Julian,
> that is the approach I was using, with eLoreta.
> I am not sure about two steps,though.
> One is the estimate and use of the signal covariance to input for single-trial activity in source space.
> The other is the choice of the optimal lambda.
> 
> If you have some advice, that wold be very helpful.
> 
> Thanks,
> David
> 
> 2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com <mailto:julian.keil at gmail.com>>:
> Hi David,
> 
> do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors <http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors>
> In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.
> 
> Good luck,
> 
> Julian
> 
> 
>> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com <mailto:psc.dav at gmail.com>>:
>> 
>> Dear fieldtrip experts,
>> 
>> I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…
>> 
>> 
>> 
>> Thanks in advance
>> 
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip <https://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip <https://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
> 
> 
> 
> -- 
> --------------------- 
> David Pascucci
> 
> Postdoctoral Fellow
> University of Fribourg
> Department of Psychology
> Rue de Faucigny 2
> 1700 Fribourg
> Switzerland
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From evelyn.muschter at unitn.it  Wed Sep 13 13:50:06 2017
From: evelyn.muschter at unitn.it (Evelyn Muschter)
Date: Wed, 13 Sep 2017 13:50:06 +0200
Subject: [FieldTrip] Any update on the group-level source MNE stats?/
	Vol 82, Issue 13
In-Reply-To: <mailman.9.1505124001.16132.fieldtrip@science.ru.nl>
References: <mailman.9.1505124001.16132.fieldtrip@science.ru.nl>
Message-ID: <88486D75-ED4E-40E0-8EE1-95A1A21E60CB@unitn.it>

Hi Miguel and all, 

I have been wondering this myself! 
I have also followed various tutorial snippets here, but I am stuck with how to properly do group level stats. 
Any suggestions and input would be greatly appreciated! 

Best, 

Evelyn 

> On Sep 11, 2017, at 12:00 PM, fieldtrip-request at science.ru.nl wrote:
> 
> Send fieldtrip mailing list submissions to
> 	fieldtrip at science.ru.nl
> 
> To subscribe or unsubscribe via the World Wide Web, visit
> 	https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> or, via email, send a message with subject or body 'help' to
> 	fieldtrip-request at science.ru.nl
> 
> You can reach the person managing the list at
> 	fieldtrip-owner at science.ru.nl
> 
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of fieldtrip digest..."
> 
> 
> Today's Topics:
> 
>   1. Any update on the group-level source MNE stats?
>      (Miguel Granja Espirito Santo)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Mon, 11 Sep 2017 09:19:13 +0000
> From: Miguel Granja Espirito Santo
> 	<Miguel.Granjaespiritosanto at nottingham.ac.uk>
> To: "fieldtrip at science.ru.nl" <fieldtrip at science.ru.nl>
> Subject: [FieldTrip] Any update on the group-level source MNE stats?
> Message-ID:
> 	<AM2PR06MB086699834D86F136DC514BFE83680 at AM2PR06MB0866.eurprd06.prod.outlook.com>
> 	
> Content-Type: text/plain; charset="iso-8859-1"
> 
> Hi fieldtripers,
> 
> 
> I was wondering if there is any update on doing group level stats after conducting an MNE source analysis. I found several threads on the mailings list which I have successfully replicated, but I was wondering if there is any official FT approved way.
> 
> 
> At the end of the MNE page it says that this is under development, so is anyone privy to what the status of documentation is?
> 
> Just asking because of supervisor enquiry for publication of our results.
> 
> 
> Best,
> 
> Miguel
> PhD Student
> School of Psychology
> University of Nottingham
> 
> 
> 
> 
> 
> This message and any attachment are intended solely for the addressee
> and may contain confidential information. If you have received this
> message in error, please send it back to me, and immediately delete it. 
> 
> Please do not use, copy or disclose the information contained in this
> message or in any attachment.  Any views or opinions expressed by the
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> 
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> ------------------------------
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 
> End of fieldtrip Digest, Vol 82, Issue 13
> *****************************************




From psc.dav at gmail.com  Wed Sep 13 15:33:57 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Wed, 13 Sep 2017 15:33:57 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
 <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
 <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>
Message-ID: <CAK-BAC872zH8R=v4QBrfj0Draw2aygYsdqkVf+esZjDSY+s62w@mail.gmail.com>

Thanks again Julian,
About the covariance, I am not sure about its usage in the reconstruction
of single-trials activity.
According to the example, this is done by multiplying the spatial filters
with the EEG data.

Whereas the covariance (Cf) is used to compute the avg.pow and ori in
ft_eloreta (line 160-168)

% get the power
dip.pow = zeros(size(dip.pos,1),1);
dip.ori = cell(size(dip.pos,1),1);
for i=1:size(dip.pos,1)
  csd        = dip.filter{i}**Cf**dip.filter{i}';
  [u,s,vv]    = svd(real(csd));
  dip.pow(i) = s(1);
  dip.ori{i} = u(:,1);
end

It does not seem to be considered when creating and storing spatial filters
(later used for single-trials reconstruction)
(line 152-158, ft_eloreta)

% use existing filters, or compute them
if ~isfield(dip, 'filter')
  filt = mkfilt_eloreta_v2(leadfield, lambda);
  for i=1:size(dip.pos,1)
    dip.filter{i,1} = squeeze(filt(:,i,:))';
  end
end

My question is, am I getting this wrong?
and if not, should I ignore the covariance estimation in the case of
single-trials reconstructed via filters*data?


Cheers,
David


2017-09-13 13:41 GMT+02:00 Julian Keil <julian.keil at gmail.com>:

> Hi David,
>
> regarding the lambda, I think there are different ideas floating around
> the fieldtrip discussion-list. I suggest searching for the term „lambda“ to
> get a rough idea. Personally, for our EEG-data I usually use 10%.
>
> What is your question exactly regarding the covariance as input?
>
> Cheers,
>
> Julian
>
> Am 13.09.2017 um 13:22 schrieb David Pascucci <psc.dav at gmail.com>:
>
> Thaks Julian,
> that is the approach I was using, with eLoreta.
> I am not sure about two steps,though.
> One is the estimate and use of the signal covariance to input for single-trial
> activity in source space.
> The other is the choice of the optimal lambda.
>
> If you have some advice, that wold be very helpful.
>
> Thanks,
> David
>
> 2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com>:
>
>> Hi David,
>>
>> do you want to obtain single-trial activity in source space? In that
>> case, have you looked at the „virtual sensors“-tutorial? http://www.
>> fieldtriptoolbox.org/tutorial/shared/virtual_sensors
>> In the tutorial, LCMV is used for the source analysis, but it should also
>> work with sloreta, as the output-structure of the source-analysis is
>> identical. I’m not sure about MNE though.
>>
>> Good luck,
>>
>> Julian
>>
>>
>> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
>>
>> Dear fieldtrip experts,
>>
>> I was wondering if anyone has experience with extracting single trials
>> estimates of source activity (using MNE or Loreta-based approaches) from
>> regions of interest, and what would be the best procedure…
>>
>>
>> Thanks in advance
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
>
> --
> ---------------------
> David Pascucci
>
> Postdoctoral Fellow
> University of Fribourg
> Department of Psychology
> Rue de Faucigny 2
> 1700 Fribourg
> Switzerland
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From Adeen.Flinker at nyumc.org  Wed Sep 13 20:23:40 2017
From: Adeen.Flinker at nyumc.org (Flinker, Adeen)
Date: Wed, 13 Sep 2017 18:23:40 +0000
Subject: [FieldTrip] ECoG postdoc position
Message-ID: <99026305-0472-4915-871A-C55374055912@nyumc.org>

NYU School of Medicine is looking for candidates for two post-doctoral positions in Human Electrocortigoraphy (ECoG) research. Both positions will be under the supervision of Dr. Adeen Flinker, investigating speech processing and cortical network dynamics.

The research will be conducted at NYU Comprehensive Epilepsy Center working with a population of surgical patients undergoing treatment for refractory epilepsy. Research paradigms will be conducted in close collaboration with the clinical neurology team. The candidate will conduct neurophysiological research in patients implanted with intracranial electrodes (surface, depth, laminar, Utah arrays) and in intraoperative patients undergoing acute recording, stimulation or cooling.

The ideal applicant must have a Ph.D. in Neuroscience, Psychology, Biomedical Engineering or a related field. Proficiency in oral and written English is mandatory. A solid background in programming, statistics and scientific writing is required. The candidate is expected to be autonomous and to have a track-record of peer-reviewed publication. Previous experience with human electrophysiology or machine learning will be an asset.

One postdoctoral position is funded by a MURI grant investigating event segmentation and episodic memory. The candidate will have an opportunity to work closely with collaborators in Princeton (Dr. Hasson, Dr. Norman), Harvard (Dr. Gershman), UC Davis (Dr. Ranganath) and Washington University (Dr. Zacks).

Interested individuals should send an email to adeen.flinker at nyumc.org, including a cover letter describing research experience and qualifications, academic CV, and contact information of referees.


Adeen Flinker, PhD
Assistant Professor
Department of Neurology
NYU School of Medicine
145 East 32nd Street
New York, NY 10016
646-754-2228



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From C.Mazzetti at donders.ru.nl  Thu Sep 14 13:28:45 2017
From: C.Mazzetti at donders.ru.nl (Mazzetti, C. (Cecilia))
Date: Thu, 14 Sep 2017 11:28:45 +0000
Subject: [FieldTrip] ICA warning messages
Message-ID: <389DA1293690C94C93E3A53201F6C91E569EB8E6@exprd01.hosting.ru.nl>

Hi Evryone,
I was wondering why do i get this type of warnings when running ICA on my data.
this is the script I am using:


cfg.resamplefs = 300;
    cfg.detrend    = 'no';
    datads = ft_resampledata(cfg, data_clean);

    cfg=[];
    cfg.bpfilter='yes'; % bandpass , use low freqs for  alpha compponents
    cfg.bpfreq = [0.5 30];
    datatmp= ft_preprocessing(cfg, datads)


    cfg.method = 'runica';
    cfg.runica.maxsteps =30;
    comp_filt = ft_componentanalysis(cfg, datatmp);

    clear datads


    cfg           = [];
    cfg.unmixing  = comp_filt.unmixing;
    cfg.topolabel = comp_filt.topolabel;
    comp_origin = ft_componentanalysis(cfg, data_clean);

    clear comp

    cfg          = [];
      cfg.viewmode = 'component';
    cfg.layout   = 'CTF275.lay';
    ft_databrowser(cfg, comp_origin)


    cfg           = [];
    cfg.component = input('bad comps = ');
    meg_ica    = ft_rejectcomponent(cfg, comp_origin,data_clean);


later on after having selected the bad components i get these messages :

Warning: unexpected channel unit "unknown" in channel 158  (i get this for all the channels but i copy-pasted just one of them for obv reasons)
Warning: copying input chantype to montage
Warning: copying input chanunit to montage

Thanks in advance for your hints!

Cecilia

Cecilia Mazzetti - Ph.D. Candidate
Donders Centre for Cognitive Neuroimaging, room 1.170
Kapittelweg 29 | 6525 EN Nijmegen

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From nirofir2 at gmail.com  Thu Sep 14 14:10:46 2017
From: nirofir2 at gmail.com (Nir Ofir)
Date: Thu, 14 Sep 2017 15:10:46 +0300
Subject: [FieldTrip] Using ft_redefinetrial with minlength and
	begsample/endsample option
Message-ID: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>

Hi fieldtrip users,

I have a data structure containing MEG trials which are aligned to stimulus
onset. I now want to realign them to the target onset, as well as removing
trials which are too short. I thought the easiest way to do this would be
to use ft_redefinetrial in the following way:

offset = dat.trialinfo(:,5); % this column contains the duration of the
stimulus-target intervel
cfg = [];
cfg.minlength = -dat.time{1}(1)+cfgx.pretarget+0.5; % prestim defined by
dat sructure + 0.5 s ERF + cfgx.pretarget
cfg.begsample = round((offset - cfgx.pretarget)*1000);
cfg.endample = round(offset*1000);
dat = ft_redefinetrial(cfg, dat);

When I run this, I get the following error:

Index exceeds matrix dimensions.

Error in ft_redefinetrial (line 209)
    data.trial{i} = data.trial{i}(:, begsample(i):endsample(i));

So I looked into ft_redefinetrials a bit, and it seems like when minlength
is defined, the trials themselves are removed, but the begample/endsample
vector are not cut to contain only the relevant trials. For now I moved to
a 2-step solution (first removing trials, than realigning), but it seems
like this could have a relatively simple fix. Suggestions?

Thanks!
Nir Ofir
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From v.piai.research at gmail.com  Fri Sep 15 12:45:01 2017
From: v.piai.research at gmail.com (Vitoria Piai)
Date: Fri, 15 Sep 2017 12:45:01 +0200
Subject: [FieldTrip] Postdoc position: Magnetoencephalography and
 Tractography applied to Language in Neurological Populations
Message-ID: <CADAXq1RZuTfHM=9iQjWAU7HUSdM_WpnbNZ6OcVuXFEZ8_sXWTg@mail.gmail.com>

*3-year postdoctoral position on the topic of magnetoencephalography and
tractography applied to language in neurological populations*


We are looking for a postdoctoral candidate with demonstrable experience in
analysis of structural imaging and tractography to strengthen our research
group. Our group aims at integrating brain measures with high temporal
resolution, obtained using magnetoencephalography, with measures of
structural connectivity to better understand language function in healthy
and neurological populations. Ongoing projects include studying chronic
stroke and brain tumour patients.


More information on
https://www.languageininteraction.nl/jobs/postdoc-position-388.html
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From jan.schoffelen at donders.ru.nl  Fri Sep 15 15:46:58 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Fri, 15 Sep 2017 13:46:58 +0000
Subject: [FieldTrip] Fwd: Question for Fieldtrip
References: <4b2644a5.aff0.15e8581f6ac.Coremail.zhangwenjia2732@126.com>
Message-ID: <420905E5-98A4-4C9C-96C4-1C8A28325786@donders.ru.nl>



Begin forwarded message:

From: 张文嘉 <zhangwenjia2732 at 126.com<mailto:zhangwenjia2732 at 126.com>>
Subject: Question for Fieldtrip
Date: 15 September 2017 at 14:27:20 GMT+2
To: <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>>



Dear Fieldtrip expert,

I am Wenjia from NYU Shanghai. I am doing timefrequency analysis with frildtrip.
I have a question that I cannot solve. And, I am wondering whether you could help me.

Specifically, I want to import the EGI data that has been preprocessed (after segmentation but no average) into fieldtrip, and further do timefrequency analysis.
I donot know how to do this.

I have found a script like following:
    cfg = [];
    cfg.triggertype = 'Stimulus';
    cfg.prestim = 1.0; %1.0s before the onset
    cfg.poststim = 2.0;  %2.0s after the onset
    cfg.inputfile = sprintf('s02_32_tf');
    cfg.triggercode = 'S 32';
    data_32 = read_analyzer_data(cfg);
"s02_32_tf" is the name that I exported from EGI system, then included 3 files: asc, vhdr and vmrk.
However an error poped up: Undefined function 'read_analyzer_data' for input arguments of type 'struct'.
Any advice are appreciated. Thank you very much.

--
Wenjia
NYU Shanghai













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From m.chait at ucl.ac.uk  Mon Sep 18 13:01:41 2017
From: m.chait at ucl.ac.uk (Chait, Maria)
Date: Mon, 18 Sep 2017 11:01:41 +0000
Subject: [FieldTrip] Research Assistant Position at the UCL Ear Institute
Message-ID: <AM3PR01MB109105F3944F78ECA01E0AB4A8630@AM3PR01MB1091.eurprd01.prod.exchangelabs.com>

I would appreciate your help in forwarding the advert below to relevant members of your department.



We are seeking to appoint an enthusiastic and motivated Research Assistant and Laboratory Manager to provide essential support for ongoing research aimed at developing transformative treatments for deafness and hearing problems. The post is funded for 12 months (with a possibility of extension for up to 5 years). The post will involve collection and analysis of audiometry as well as behavioral (psychophysics), eye tracking and EEG data in humans. The postholder will also be expected to contribute to the induction and direction of other research staff and students.  The UCL Ear Institute, located in the heart of London,  provides state-of-the-art research facilities across a wide range of disciplines and is one of the foremost centres for hearing, speech and language-related research within Europe.



Applicants should hold a 1st class, or upper 2nd (or equivalent) BSc or MSc degree in an engineering or Neuroscience-related subject. Previous experience with neuroscience research, functional brain imaging and/or acoustics is desirable.

More information and a link to the application site are in the following link: http://www.jobs.ac.uk/job/BEH401/research-assistant-in-auditory-neuroscience



Closing Date: 15 October 2017


Maria Chait PhD
m.chait at ucl.ac.uk<mailto:m.chait at ucl.ac.uk>
Professor in Auditory Cognitive Neuroscience
Lab site: http://www.ucl.ac.uk/ear/research/chaitlab/
UCL Ear Institute
332 Gray's Inn Road
London WC1X 8EE
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From hweeling.lee at gmail.com  Mon Sep 18 13:57:51 2017
From: hweeling.lee at gmail.com (Hwee Ling Lee)
Date: Mon, 18 Sep 2017 13:57:51 +0200
Subject: [FieldTrip] Normalizing log-transformed EEG power
Message-ID: <CABG7z0Sqe5vdMU8X_Kjp7aQVRn-y-rRGxevpKzHEnpZLs00Grw@mail.gmail.com>

Dear all,


I have a question regarding how to compute z-scores for log-transformed EEG
power across all events separately for each electrode and frequency. I have
searched everywhere on how to implement this in the fieldrip environment,
however, I will be very grateful if someone can help me out on this.

Thanks!

Cheers,
Hweeling
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From lxykh0700073 at outlook.com  Tue Sep 19 03:51:59 2017
From: lxykh0700073 at outlook.com (Xinyi Li)
Date: Tue, 19 Sep 2017 01:51:59 +0000
Subject: [FieldTrip] simple main effects and permutation
Message-ID: <HK2PR06MB07057B48729B2D6F77CC0A0581600@HK2PR06MB0705.apcprd06.prod.outlook.com>

Hi all,


I have a mixed design and my hypothesis is about simple main effects. So for example, I have two groups of people, and each person experience the same 2x2 factorial design with conditions A1, A2, B1, B2. And my hypotheses are something like the simple main effect of A within group 1 and condition B1.


My questions:

1) Can I just subset the data to include only the group and condition I want (B1 & group 1) and do a t-test between condition A1 & A2 after subsetting? If I understand correctly, this approach will bias the standard error of the estimates? But I'm not sure if this matters in a permutation framework, and I also don't know if it's a common practice to do this in EEG analysis?

2) Alternatively, I can run a full mixed ANOVA model and then the simple main effect in R to get the test statistics I want. But for this approach I'm not sure how I should perform the permutation since fieldtrip doesn't support a mixed design. Should I only permute A1 & A2 within condition B1 and group 1? Or should I permute everything within both factors A & B? And what about the group labels?


Any suggestions? Thanks!


Xinyi
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From da401 at kent.ac.uk  Tue Sep 19 06:25:36 2017
From: da401 at kent.ac.uk (D.Abdallah)
Date: Tue, 19 Sep 2017 04:25:36 +0000
Subject: [FieldTrip] Question about MVPA topographic map
In-Reply-To: <1504785200819.38370@kent.ac.uk>
References: <1504785200819.38370@kent.ac.uk>
Message-ID: <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>


Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.

I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING

%Reading the data

cfg = [];

cfg.dataset = filename1;

cfg.reref ='yes';

cfg.channel = {'Cz','PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};

cfg.refchannel ='Cz';

cfg.demean ='yes';

data_eeg1 = ft_preprocessing(cfg);

%Segmenting data

cfg.trialdef.eventtype ='?';

Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;

cfg.trialdef.poststim = 0.6;

cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype ='STATUS';

cfg.trialdef.eventvalue = [100];

stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];

FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];

VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers

stimulusTrigger = 100;

faceResponseTrigger = 1;

vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing

[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';

FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing

[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';

Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP

FaceTrials.reref ='no';

FaceTrials.keeptrials ='yes';% classifiers operate on individual trials

FaceTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only

FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP

VaseTrials.reref ='no';

VaseTrials.keeptrials ='yes';% classifiers operate on individual trials

VaseTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only

VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA

cfg.layout ='biosemi64.lay';

cfg.method ='crossvalidate';

cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];

cfg.nfolds = 4;

cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy''binomial''contingency'};

stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);

stat.statistic.contingency

%Plot MVPA Results

stat.mymodel = stat.model{2}.primal;

cfg.parameter ='mymodel';

cfg.xlim = [-0.1 0.6];

cfg.comments ='';

cfg.colorbar ='yes';

cfg.interplimits='electrodes';

ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.

We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).

Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,

Diane Abdallah




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From jan.schoffelen at donders.ru.nl  Tue Sep 19 07:47:49 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 05:47:49 +0000
Subject: [FieldTrip] Question about MVPA topographic map
In-Reply-To: <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>
References: <1504785200819.38370@kent.ac.uk>
 <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>
Message-ID: <A9C685DD-18DD-4F2F-99F6-99BA89CA43A4@donders.ru.nl>

Hi Diane,

First of all, I would recommend to share figures not as a matlab-figure, but as a screenshot bitmap or so. This saves people who are reading your mail a lot of overhead if they want to look at it, because they don’t need to start a matlab session etc.

Your topographical image looks ‘different’ from the one on the wiki because the distribution of your electrodes is more around the whole ‘rim’ of the head. The colored plane that shows up within the circle is the consequence of a spatial interpolation (in 2D) of the data points represented at the locations of the electrodes. For this reason also, there’s no need to be very explicit about the meaning of the x and y axes: they represent space.

Best wishes and good luck,

Jan-Mathijs



On 19 Sep 2017, at 06:25, D.Abdallah <da401 at kent.ac.uk<mailto:da401 at kent.ac.uk>> wrote:


Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.
I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING
%Reading the data
cfg = [];
cfg.dataset = filename1;
cfg.reref ='yes';
cfg.channel = {'Cz','PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};
cfg.refchannel ='Cz';
cfg.demean ='yes';
data_eeg1 = ft_preprocessing(cfg);

%Segmenting data
cfg.trialdef.eventtype ='?';
Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;
cfg.trialdef.poststim = 0.6;
cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype ='STATUS';
cfg.trialdef.eventvalue = [100];
stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];
FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];
VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers
stimulusTrigger = 100;
faceResponseTrigger = 1;
vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing
[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);
hdr = ft_read_header(cfg.dataset);
event = ft_read_event(cfg.dataset);
FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';
FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);
Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing
[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);
hdr = ft_read_header(cfg.dataset);
event = ft_read_event(cfg.dataset);
VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';
Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);
Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP
FaceTrials.reref ='no';
FaceTrials.keeptrials ='yes';% classifiers operate on individual trials
FaceTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only
FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP
VaseTrials.reref ='no';
VaseTrials.keeptrials ='yes';% classifiers operate on individual trials
VaseTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only
VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA
cfg.layout ='biosemi64.lay';
cfg.method ='crossvalidate';
cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];
cfg.nfolds = 4;
cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy''binomial''contingency'};
stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);
stat.statistic.contingency

%Plot MVPA Results
stat.mymodel = stat.model{2}.primal;
cfg.parameter ='mymodel';
cfg.xlim = [-0.1 0.6];
cfg.comments ='';
cfg.colorbar ='yes';
cfg.interplimits='electrodes';
ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.
We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).
Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,
Diane Abdallah




<Imagesc Subject 8.fig><Subject 8 Topographical map.fig><tutorial topographic map.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From jan.schoffelen at donders.ru.nl  Tue Sep 19 08:02:53 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 06:02:53 +0000
Subject: [FieldTrip] Using ft_redefinetrial with minlength and
 begsample/endsample option
In-Reply-To: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>
References: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>
Message-ID: <6728EEE0-D0EF-4A9D-AAEA-50A61E765FC9@donders.ru.nl>

Dear Nir Ofir,

Thanks for reporting this. It seems that you have also identified a possible solution, which would be to do the ‘too short trial removal’, only after the realignment of the time axis of the trials.
I think the best way to proceed would be that you try to implement this fix in your own local version of the FieldTrip git repository, and initiate a pull request once you are sure it works well. We will then review the suggested fix, and merge it into  Fieldtrip’s code base, so that everyone can benefit from your efforts.

Best wishes,

Jan-Mathijs


> On 14 Sep 2017, at 14:10, Nir Ofir <nirofir2 at gmail.com> wrote:
> 
> Hi fieldtrip users,
> 
> I have a data structure containing MEG trials which are aligned to stimulus onset. I now want to realign them to the target onset, as well as removing trials which are too short. I thought the easiest way to do this would be to use ft_redefinetrial in the following way:
> 
> offset = dat.trialinfo(:,5); % this column contains the duration of the stimulus-target intervel
> cfg = [];
> cfg.minlength = -dat.time{1}(1)+cfgx.pretarget+0.5; % prestim defined by dat sructure + 0.5 s ERF + cfgx.pretarget
> cfg.begsample = round((offset - cfgx.pretarget)*1000);
> cfg.endample = round(offset*1000);
> dat = ft_redefinetrial(cfg, dat);
> 
> When I run this, I get the following error:
> 
> Index exceeds matrix dimensions.
> 
> Error in ft_redefinetrial (line 209)
>     data.trial{i} = data.trial{i}(:, begsample(i):endsample(i));
> 
> So I looked into ft_redefinetrials a bit, and it seems like when minlength is defined, the trials themselves are removed, but the begample/endsample vector are not cut to contain only the relevant trials. For now I moved to a 2-step solution (first removing trials, than realigning), but it seems like this could have a relatively simple fix. Suggestions?
> 
> Thanks!
> Nir Ofir
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip




From isac.sehlstedt at psy.gu.se  Tue Sep 19 08:07:41 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Tue, 19 Sep 2017 06:07:41 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
Message-ID: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,

I have conducted a EEG experiement and am currently in a wedge. The PCA-function used in matlab ( i.e. pca() )  gives me the latent and coeff values that I want to use for further analysis. Sadly, I have cannot figure out how to perform a group level analysis using the matlab function and later "unmix" the group analysis to the subject level.

The a group analysis ft_componentanalysis function is easier to "unmix" thanks to its description of how to do so. However, I have not found a way to extract the latent and coefficient variables from the variables included in the comp-structure.

My question is:
Can you extract the latent and coefficient variables from the ft_componentanalysis results?

Alternatively:
Is it possible to extract the subject level latent and coefficient variables using the matlab function pca() ?

Very best,
Isac
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From jan.schoffelen at donders.ru.nl  Tue Sep 19 08:57:52 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 06:57:52 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>
References: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>
Message-ID: <3197AF78-5B87-4F7C-A587-FFD8E8FF2071@donders.ru.nl>

Hi Isac,

My question is:
Can you extract the latent and coefficient variables from the ft_componentanalysis results?

I’d say that the ‘latent variables’ are in the comp.trial field, and the coefficients are in comp.topo


Alternatively:
Is it possible to extract the subject level latent and coefficient variables using the matlab function pca() ?

I don’t know.

Best wishes,
Jan-Mathijs



Very best,
Isac
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
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From jean-michel.badier at univ-amu.fr  Tue Sep 19 15:52:53 2017
From: jean-michel.badier at univ-amu.fr (Jean-Michel Badier)
Date: Tue, 19 Sep 2017 15:52:53 +0200
Subject: [FieldTrip] Open positions at INS, Marseille. France
Message-ID: <fc154009-2f7a-ab63-68de-9cb58d5d9617@univ-amu.fr>


  /Post-doctoral position in the Theoretical Neuroscience Group - INS,
  Marseille, France/

*Summary*

The Theoretical Neuroscience Group (Head: Viktor Jirsa) is seeking to 
fill a post-doctoral position in the context of the project EPINOV to 
work on statistical & dynamical modeling of seizure propagation using 
personalized brain modeling and neuroinformatics approaches on a cohort 
of hundreds of epilepsy patients. EPINOV is one of 10 large-scale 
projects selected in the 3rd round of French scientific excellence 
program «RHU» managed by the National Research Agency (ANR). The aim of 
the EPINOV project is to significantly improve presurgical 
interpretation, guide surgical strategies and translate computational 
tools into clinical routine of personalized medicine. We use individual 
MRI scans to reconstruct brain anatomy and connectivity, which are 
combined with a neural mass model and fit using the Bayesian modeling 
software Stan to individuals’ intracranial electrophysiology data 
(stereotactic EEG), validated by clinical data from other modalities, 
such as MEG, fMRI, and semiology.

*Responsibilities*

• Scale up statistical models “vertically” to handle more data and 
higher resolution anatomy, using model comparison techniques to evaluate 
the advantage of different model structures

• Scale out models “horizontally”, performing coherent, reliable 
inference across a large cohort of patients using dedicated, on-site HPC 
resources

• Develop routines to evaluate and visualize inference results, making 
them amenable to clinical interpretation

• Integrate developed code into existing code bases and pipelines

*Qualification *

• Highly motivated to work on an interdisciplinary project and 
collaborate with the various members of the consortium.

• PhD degree in computational neuroscience, mathematical or statistical 
modeling, machine learning or equivalent level of knowledge.

• Significant, demonstrable experience in data fitting (Bayesian 
modeling, Dynamical Causal Modeling (DCM), Monte Carlo, etc) will be 
highly appreciated.

• Experience with working in a Linux/HPC environment

• Programming in a numerically oriented language (R, Python, MATLAB)

• Familiarity with Git, unit testing, Docker/VMs is a plus

*The Theoretical Neuroscience group *

We are a multi-national team interested in understanding the mechanisms 
underlying the spatiotemporal organization of large-scale brain 
networks. Our work comprises mathematical and computational modeling of 
large-scale network dynamics and human brain imaging data, the 
development of neuroinformatics tools for studying large-scale brain 
networks applied to concrete functions, dysfunctions (epilepsy, 
dementia) and aging.

*Terms of salary and employment *

A 12-month renewable contract will be established. Salary will depend on 
the diploma and experience. Operating language in the laboratory is 
English. Applications including a cover letter, curriculum vitae and the 
names of two referees should be sent by September 30th 2017 to: Dr. 
Irene Yujnovsky at irene.yujnovsky at univ-amu.fr 
<mailto:irene.yujnovsky at univ-amu.fr>

More information about the INS and the Theoretical Neurosciences Group 
can be found at: http://ins.univ-amu.fr <http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>*Clinical data manager for national consortium 
on epilepsy surgery - **Aix-Marseille Université *

*Marseille, FRANCE*

**

**

A position for an experienced clinical data manager is open to create 
and maintain an epileptic patient database including registration, 
normalization and security issues and to ensure the communication with 
the key partners in the academic, clinical and industry sectors with the 
aim of generating individual Virtual Patient models using The Virtual 
Brain (TVB) platform as framework (see 
http://www.thevirtualbrain.org).//This database will be generated in the 
context of the EPINOV (/Improving EPilepsy surgery management and 
progNOsis using Virtual brain technology) /projectled by Professor 
Fabrice Bartolomei 
(http://fr.ap-hm.fr/service/neurophysiologie-clinique-hopital-timone) 
funded by the RHU programme.

*Qualification*

**

Candidates must be highly motivated to work on an interdisciplinary 
project and collaborate with the various members of the consortium. They 
should have a degree in biomedical engineering, medical informatics or 
equivalent level of knowledge. Candidates must possess a solid 
experience in management of clinical and/or research data and 
programming (C, MATLAB, Python). Experience with neuroimaging data 
(stereotactic EEG, MRI, DTI, MEG, EEG), clinical trials, 
neuroinformatics and its standard formats (for instance DICOM, XNAT, 
BIDS), machine learning and Big Data would be considered an advantage.

*The EPINOV project and consortium *

We are a national consortium composed of clinicians, researchers and 
industrial partners interested in improving epilepsy surgical prognosis 
using large–scale brain modelling based on individual epileptic patient 
data. A prospective, randomized multicenter trial will be conducted with 
subjects suffering from drug-resistant epilepsy. The clinical trial will 
systematically evaluate the added value of personalized brain modelling 
in the surgical decision making.

*Terms of salary and employment*

Salary will depend on the diploma and previous experience. Operating 
languages in the consortium are both French and English.

Applications including a cover letter, curriculum vitae and the names of 
two referees should be sent by October 31st 2017 to: *Dr. Irene 
Yujnovsky* at irene.yujnovsky at univ-amu.fr 
<mailto:irene.yujnovsky at univ-amu.fr>


-- 
Jean michel Badier /- UMR S 1106 Institut de Neurosciences des Systèmes/
Aix-Marseille Université - Laboratoire MEG - TIMONE - 27 Boulevard Jean 
Moulin - 13005 Marseille
Tél: +33(0)4 91 38 55 62 - Fax : +33(0)4 91 78 99 14
Site : http://www.univ-amu.fr - Email : jean-michel.badier at univ-amu.fr 
<mailto:jean-michel.badier at univ-amu.fr>
/Afin de respecter l'environnement, merci de n'imprimer cet email que si 
nécessaire./
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From johnnguyen.education at gmail.com  Tue Sep 19 18:41:34 2017
From: johnnguyen.education at gmail.com (John Nguyen)
Date: Tue, 19 Sep 2017 12:41:34 -0400
Subject: [FieldTrip] Source analysis and sensor space differences
Message-ID: <CACzaKkHpp+LMKBLdjcbFooe5w0tddrGVeUgR1AFSU5n8Ya5BRA@mail.gmail.com>

Hi All,

My Name is John Nguyen and I am working at the Reinhart Cognitive
Neuroscience Lab at Boston University. I have been using Fieldtrip (Version
4/10/17)​​ for several months now and have decided to tackle
SourceAnalysis. After a few weeks of struggling with it, I find myself
still far from the goal post.

A milestone I'm trying to achieve is plotting activity in the visual cortex
due to a visual stimulus onset. This activity is easily reflected in my
sensor-level plots but, in the source-level, it's projection is more than a
bit wonky [image link, Negative sensor potential ,1.09-1.23s, following
visual stimulus followed by positive sensor potential at 1.22-1.48s neither
of which are present in source space.]
[https://drive.google.com/file/d/0B2UdTHvTeS9NNWV6dGJKZ2JWbk0/
view?usp=sharing]

In my current code I am using Fieldtrip templates to minimize error on my
end as much as possible.
"elec = ft_read_sens('standard_1020.elc');
load('standard_bem.mat','vol')
load('standard_sourcemodel3d8mm.mat','sourcemodel')
mri = ft_read_mri('single_subj_T1_1mm.nii');"

My Sourceanalysis is timelocked LCMV with a relative baseline change at
times -0.2 to 0.0s. I also preformed a relative baseline change on my
sensor-level data because I was wondering if the disparity was a baseline
issue. I've reached a dead-end and would appreciate any help. I've attached
links to my script
[https://drive.google.com/file/d/0B2UdTHvTeS9NT05uWUk4T0hDUjQ/
view?usp=sharing]

and data (no rereference, no artifact reject)
[https://drive.google.com/file/d/0B2UdTHvTeS9NSl8yaW9fcXZWSjQ/
view?usp=sharing]

and sensor cap layout.
[https://drive.google.com/file/d/0B2UdTHvTeS9NSGJXZUszOHBUbGM/
view?usp=sharing]


Best regards,
John Nguyen
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From isac.sehlstedt at psy.gu.se  Wed Sep 20 09:15:56 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Wed, 20 Sep 2017 07:15:56 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
Message-ID: <D5E7D932.19177%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,


This is a follow-up question to a previous question with the same mail-topic.


I have included my code below to show what I am doing (in case I have made errors) and print screens (which are also attached) of the variables I get after the ft_componentanalysis that I get.


Sadly, I cannot see any variable named comp.trial (see Unknown.tiff, or Unknown-1.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see Unknown.tiff-2) . Why am I not getting that in ft? Is it possible to get that using ft?


Very Best,

Isac



----------------- The code -----------------


clear all; close all;


%% Load

load('averages_for_ft.mat')




%% define layout

cfg = [];

cfg.elec=PreOdd_ft{1, 1}.elec;

cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)

layout = ft_prepare_layout(cfg)




%% Make the computations


% Dummy varibles

Cond1 = [];

Cond2 = [];

theDiff = [];

theDiff_ft = {};



%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions

    curr_Cond2 = Cond2_ft{i}.avg;

    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions

    cfg = [];

    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});

    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math

    cfg  = [];

    cfg.operation = 'subtract';

    cfg.parameter = 'avg';

    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);

    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions

    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.

    Cond2 = [Cond2 curr_Cond2];

    Cond1 = [Cond1 curr_Cond1];

    theDiff = [theDiff curr_difference_avg];

end




%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};

dummy_Cond2.avg = Cond2;

dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};

dummy_Cond1.avg = Cond1;

dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};

dummy_theDiff.avg = theDiff;

dummy_theDiff.time = 1:1:size(theDiff,2);




%% Run the PCA



cfg = [];

cfg.method  = 'pca';

cfg.layout = layout;


Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);

Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);

theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);




%% Revert back to subject level


cfgCond2 = [];

cfgCond2.unmixing = Cond2_comp.unmixing;

cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];

cfgCond1.unmixing = Cond1_comp.unmixing;

cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];

cfgtheDiff.unmixing = theDiff_comp.unmixing;

cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)

    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});

    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});

    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );

end



----------------- The variables/results  -----------------




[X]



[X]


[X]
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From litvak.vladimir at gmail.com  Wed Sep 20 12:56:01 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Wed, 20 Sep 2017 11:56:01 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
Message-ID: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results
when doing TF decomposition compared to computing a spectrum for the same
time window. I'm not sure I got to the bottom of it yet but one thing I
found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
differently by increasing padding. For short padding the results are
similar but with increasing padding there are differences both in offset of
the spectrum and its overall shape. See attached images where the top one
shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as
well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper',
'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
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From r.oostenveld at donders.ru.nl  Wed Sep 20 16:26:37 2017
From: r.oostenveld at donders.ru.nl (Robert Oostenveld)
Date: Wed, 20 Sep 2017 16:26:37 +0200
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
Message-ID: <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com> wrote:
> 
> Dear Fieldtrippers,
> 
> I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero. 
> 
> Is this a bug or a feature?
> 
> Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.
> 
> Thanks,
> 
> Vladimir
> 
> 
> -------------------------------------
> 
> pad = 0.5;%1%10
> 
> 
> freqoi = 5:45;
> timwin = 0.4+0*freqoi;
> 
> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
> 
> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
> 
> figure;
> subplot(2,1,1)
> plot(freqoi, s1);
> subplot(2,1,2);
> plot(freqoi, s1-s1(1));
> %%
> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
> 
> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
> 
> subplot(2,1,1)
> hold on
> plot(freqoi, s2, 'r');
> subplot(2,1,2)
> hold on
> plot(freqoi, s2-s2(1), 'r');
> <pad0_5.png><pad1.png><pad10.png>_______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Wed Sep 20 17:03:52 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Wed, 20 Sep 2017 17:03:52 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
Message-ID: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper
segmentation for which changing individual brain/scalp thresholds works in
many cases but not all, e.g. when the scalp 'bleeds' into some noise
outside of the head. Also, changing parameters in spm8 for normalization,
such as number of iterations (directly in in spm_normalize, since FT does
not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from
outsides of the head 'bleed' onto the scalp, thereby preventing optimal
scalp-segmentation and thereby normalization. Others have an inappropriate
contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved
preprocessing procedures. However, it does not seem supported in FT yet.
Does anyone have experience with this, and can perhaps share how they
extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
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From hamedtaheri at yahoo.com  Wed Sep 20 20:00:47 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Wed, 20 Sep 2017 18:00:47 +0000 (UTC)
Subject: [FieldTrip] Splitting EEG
References: <1779089334.6211483.1505930447659.ref@mail.yahoo.com>
Message-ID: <1779089334.6211483.1505930447659@mail.yahoo.com>

Hello Fieldtrip users,
I have a continues EEG data ( 80 seconds) and I would like to analyze some part of it.For instance, I want to analyse seconds 20 to 25 ( 5 seconds).Would you please let me know how can I select my times of interest.I've written a simple code but I don't know how can I split the data. 
cfg    = [];
cfg.dataset = '........  .eeg';
data_org                = ft_preprocessing(cfg); %Original Data

% Step1:  Filtering Row Data
cfg                        = [];
cfg.bpfilter            = 'yes';
cfg.bpfreq             = [1 30];
data_Filtered        = ft_preprocessing(cfg,data_org); 
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From sarang at cfin.au.dk  Wed Sep 20 22:22:51 2017
From: sarang at cfin.au.dk (Sarang S. Dalal)
Date: Wed, 20 Sep 2017 20:22:51 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
Message-ID: <0A0286C7-606F-4B30-B8F4-6689EAAD9620@cfin.au.dk>

Hi Stephen,

We have a pipeline that can use either SPM8 or SPM12 to perform both segmentation and normalization, though perhaps in a way that’s different from the official FieldTrip tutorials. Have a look at:
https://github.com/meeg-cfin/nemolab/blob/master/basics/nemo_mriproc.m

ft_volumesegment should use whichever SPM is in your path (be careful about fieldtrip/external/spm8!), and (according to my memory) SPM12 sometimes can succeed where SPM8 doesn’t provide good segmentations.

Note that for the normalization in SPM12, our script defines “/OldNorm/T1.nii” as the template, which indeed seems to give results equivalent to SPM8. I think you could change this to SPM12’s default template if you prefer.

NB: we use MRI coordinates as the base coordinate system in our pipelines, so MEG/EEG is transformed to MRI, rather than MRI to MEG/EEG.

Cheers,
Sarang


On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at GMAIL.COM<mailto:stephen.whitmarsh at GMAIL.COM>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From cornelia.quaedflieg at uni-hamburg.de  Wed Sep 20 22:51:57 2017
From: cornelia.quaedflieg at uni-hamburg.de (Conny Quaedflieg)
Date: Wed, 20 Sep 2017 22:51:57 +0200
Subject: [FieldTrip] Splitting EEG
In-Reply-To: <1779089334.6211483.1505930447659@mail.yahoo.com>
References: <1779089334.6211483.1505930447659.ref@mail.yahoo.com>
 <1779089334.6211483.1505930447659@mail.yahoo.com>
Message-ID: <20170920205155.9C930B5309@mailhost.uni-hamburg.de>

Dear Hamed, 

You can use ft_select data with cfg.latency 
See
http://www.fieldtriptoolbox.org/reference/ft_selectdata 

best 
Conny

Van: Hamed Taheri
Verzonden: woensdag 20 september 2017 20:12
Aan: fieldtrip at science.ru.nl
Onderwerp: [FieldTrip] Splitting EEG

Hello Fieldtrip users,

I have a continues EEG data ( 80 seconds) and I would like to analyze some part of it.
For instance, I want to analyse seconds 20 to 25 ( 5 seconds).
Would you please let me know how can I select my times of interest.
I've written a simple code but I don't know how can I split the data. 

cfg    = [];
cfg.dataset = '........  .eeg';
data_org                = ft_preprocessing(cfg); %Original Data

% Step1:  Filtering Row Data
cfg                        = [];
cfg.bpfilter            = 'yes';
cfg.bpfreq             = [1 30];
data_Filtered        = ft_preprocessing(cfg,data_org); 

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From jan.schoffelen at donders.ru.nl  Thu Sep 21 09:09:06 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 07:09:06 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
Message-ID: <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>

Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).

Best,
Jan-Mathijs

On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From litvak.vladimir at gmail.com  Thu Sep 21 11:17:11 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 10:17:11 +0100
Subject: [FieldTrip] MEG technician post at UCL
Message-ID: <CACq1Bh+rMEWNX-LiMp6gBWeCiQB9dkyXAmsw5=Jn9STB3nHrTA@mail.gmail.com>

*Senior MEG Research Technician*



Applications are invited for a Senior Research Technician in the Wellcome
Centre for Human Neuroimaging, UCL Institute of Neurology.



The Centre houses an Electroencephalography (EEG) system, two
Magnetoencephalography (MEG) systems - a CTF 275 channel Omega System and
an Optically Pumped Magnetometer (OPM) System -  and Magnetic Resonance
Imaging (MRI) facilities - two 3T Siemens Prisma scanners and a 3T Siemens
Trio.  The successful candidate will be responsible for coordinating and
maintaining an efficient MEG and EEG service for a range of different
research projects. This role will be constantly evolving as new and
alternative technologies are incorporated into the functional imaging
department.





*Applicants are required to have:*

·         Experience of EEG/MEG or similar electrophysiological recording
methods (e.g. cardiology/audiology) within a clinical or research
environment.

·         A commitment to academic research.

·         MEG experience is not essential as training will be provided.



*Salary - UCL Grade 7:*

£34,653 to £41,864 inclusive of London Allowance.

The post is available immediately and is funded until Nov 2021 in the first
instance.



Applications through UCL's online recruitment – www.ucl.ac.uk/hr/jobs where
you can download a job description and person specification using ref:
1671016.



Informal enquiries to Elaine Williams:  elaine.williams at ucl.ac.uk . If you
have any queries regarding the application process, please contact Samantha
Robinson, HR Officer, Institute of Neurology, 23 Queen Square, London, WC1N
3BG
<https://maps.google.com/?q=23+Queen+Square,+London,+WC1N+3BG&entry=gmail&source=g>
(email: ion.hradmin at ucl.ac.uk).



*Closing date:  26th September 2017*

*Taking Action for Equality*
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From jan.schoffelen at donders.ru.nl  Thu Sep 21 11:53:05 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 09:53:05 +0000
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
Message-ID: <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>

Hi to all who’s reading along,

Perhaps the two cases will become more similar once the ‘timwin’ is increased in length for the mtmconvol case….

Best wishes,

JM

On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl<mailto:r.oostenveld at donders.ru.nl>> wrote:

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
<pad0_5.png><pad1.png><pad10.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From litvak.vladimir at gmail.com  Thu Sep 21 12:29:12 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 11:29:12 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
Message-ID: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>

Hi Jan-Mathijs,

Yes, you are right about Robert's example. But if you do:

pad = 10;

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
'hanning', 'pad', pad);

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');


You will see the problem that I'm talking about. We discussed with Robert
yesterday and this is indeed 'a feature' which has to do with the fact that
the outputs of mtmfft and mtmconvol have different units. The former is
spectral density whereas the latter is spectral power.

Here is what Robert wrote me:


the units of computations (also here) are a known and long-standing issue.
I know for a long time that the two have different scaling, but did not
think about it for a long time. I recall something like this: To compare
TFRs over frequencies, you don't want the bandwidth to affect the estimate.
Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect
those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with
conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral
resolution over which the signal(and noise) spreads is different. If you
were to compute the TFR in V^2/Hz, the same V at 20Hz would have a
different value, because the length of the wavelet affects the 1/Hz.
something related (but nevertheless different) applies to the mtmfft: if
you want to estimate broadband activity in a window of 1 second or a window
of 2 seconds, you would get different spectral resolutions. The nyquist is
the same, but the power gets distributed over more bins between 0 and
Fnyquist/2. That would cause the values to appear smaller in the 2-s case.
Hence we compute spectral density, which somehow normalizes for this. I
never found a really clear explanation, but google got me this
https://dsp.stackexchange.com/questions/33957/what-is-the-difference-between-the-psd-and-the-power-spectrum
what confuses me is that power (or variance) is already normalized, i.e.
sum of squared values divided by N. So we have energy (which increases with
length), power (which does not increase with length), and power density


So one issue is that most people don't know about this including me and
possibly you. I think a good solution would be to add an option to specify
the output units for all the methods as there might be quite subtle
considerations for choosing one over the other as Robert suggests.

Vladimir

On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi to all who’s reading along,
>
> Perhaps the two cases will become more similar once the ‘timwin’ is
> increased in length for the mtmconvol case….
>
> Best wishes,
>
> JM
>
> On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl>
> wrote:
>
> Hi Vladimir,
>
> I suggest that you first start with a simpler case, like this
>
> fsample = 1000;
> time = (1:1000)/fsample;
> dat = randn(size(time));
>
> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
> 'hanning');
>
> power1 = abs(spectrum1).^2;
> power1 = squeeze(power1);
>
> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);
>
> power2 = abs(spectrum2).^2;
> power2 = squeeze(power2);
>
> figure
> plot(freqoi1, power1);
> hold on
> plot(freqoi2, power2, 'r');
>
> Note that these are not the same (albeit similar), which I had expected…
>
> best
> Robert
>
>
>
> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com>
> wrote:
>
> Dear Fieldtrippers,
>
> I'm looking into an issue of one of SPM users who gets different results
> when doing TF decomposition compared to computing a spectrum for the same
> time window. I'm not sure I got to the bottom of it yet but one thing I
> found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
> differently by increasing padding. For short padding the results are
> similar but with increasing padding there are differences both in offset of
> the spectrum and its overall shape. See attached images where the top one
> shows original spectra and the bottom one aligns the lowermost bin to zero.
>
> Is this a bug or a feature?
>
> Below is the script that produces these plots. I could provide the data as
> well but this could probably be reproduced with any data.
>
> Thanks,
>
> Vladimir
>
>
> -------------------------------------
>
> pad = 0.5;%1%10
>
>
> freqoi = 5:45;
> timwin = 0.4+0*freqoi;
>
> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time,
> 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>
> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
>
> figure;
> subplot(2,1,1)
> plot(freqoi, s1);
> subplot(2,1,2);
> plot(freqoi, s1-s1(1));
> %%
> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
> 'hanning', 'freqoi', freqoi,...
>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>
> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
>
> subplot(2,1,1)
> hold on
> plot(freqoi, s2, 'r');
> subplot(2,1,2)
> hold on
> plot(freqoi, s2-s2(1), 'r');
> <pad0_5.png><pad1.png><pad10.png>__________________________
> _____________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From litvak.vladimir at gmail.com  Thu Sep 21 12:34:23 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 11:34:23 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
 <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
Message-ID: <CACq1BhKab0TFFr=eX1peo_w2PPBuq5tGuCzBh9muB7Dyvm=R_A@mail.gmail.com>

Another thing that I noticed is that in the mtmconvol case padding is added
to the entire trial, not to the short window over which FFT is actually
computed. This might be because you actually use a wavelet which moves
along the data (I didn't check that). Anyhow right now this doesn't make
much difference because padding doesn't affect mtmconvol in such a dramatic
way as mtmfft. However, if you do allow specifying the units as power
rather than density then the way things are now mtmconvol and mtmfft with
the same padding would not be equivalent.

Vladimir

On Thu, Sep 21, 2017 at 11:29 AM, Vladimir Litvak <litvak.vladimir at gmail.com
> wrote:

> Hi Jan-Mathijs,
>
> Yes, you are right about Robert's example. But if you do:
>
> pad = 10;
>
> fsample = 1000;
> time = (1:1000)/fsample;
> dat = randn(size(time));
>
> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
> 'hanning', 'pad', pad);
>
> power1 = abs(spectrum1).^2;
> power1 = squeeze(power1);
>
> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);
>
> power2 = abs(spectrum2).^2;
> power2 = squeeze(power2);
>
> figure
> plot(freqoi1, power1);
> hold on
> plot(freqoi2, power2, 'r');
>
>
> You will see the problem that I'm talking about. We discussed with Robert
> yesterday and this is indeed 'a feature' which has to do with the fact that
> the outputs of mtmfft and mtmconvol have different units. The former is
> spectral density whereas the latter is spectral power.
>
> Here is what Robert wrote me:
>
>
> the units of computations (also here) are a known and long-standing issue.
> I know for a long time that the two have different scaling, but did not
> think about it for a long time. I recall something like this: To compare
> TFRs over frequencies, you don't want the bandwidth to affect the estimate.
> Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect
> those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with
> conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral
> resolution over which the signal(and noise) spreads is different. If you
> were to compute the TFR in V^2/Hz, the same V at 20Hz would have a
> different value, because the length of the wavelet affects the 1/Hz.
> something related (but nevertheless different) applies to the mtmfft: if
> you want to estimate broadband activity in a window of 1 second or a window
> of 2 seconds, you would get different spectral resolutions. The nyquist is
> the same, but the power gets distributed over more bins between 0 and
> Fnyquist/2. That would cause the values to appear smaller in the 2-s case.
> Hence we compute spectral density, which somehow normalizes for this. I
> never found a really clear explanation, but google got me this
> https://dsp.stackexchange.com/questions/33957/what-is-the-
> difference-between-the-psd-and-the-power-spectrum
> what confuses me is that power (or variance) is already normalized, i.e.
> sum of squared values divided by N. So we have energy (which increases with
> length), power (which does not increase with length), and power density
>
>
> So one issue is that most people don't know about this including me and
> possibly you. I think a good solution would be to add an option to specify
> the output units for all the methods as there might be quite subtle
> considerations for choosing one over the other as Robert suggests.
>
> Vladimir
>
> On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi to all who’s reading along,
>>
>> Perhaps the two cases will become more similar once the ‘timwin’ is
>> increased in length for the mtmconvol case….
>>
>> Best wishes,
>>
>> JM
>>
>> On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl>
>> wrote:
>>
>> Hi Vladimir,
>>
>> I suggest that you first start with a simpler case, like this
>>
>> fsample = 1000;
>> time = (1:1000)/fsample;
>> dat = randn(size(time));
>>
>> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
>> 'hanning');
>>
>> power1 = abs(spectrum1).^2;
>> power1 = squeeze(power1);
>>
>> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
>> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);
>>
>> power2 = abs(spectrum2).^2;
>> power2 = squeeze(power2);
>>
>> figure
>> plot(freqoi1, power1);
>> hold on
>> plot(freqoi2, power2, 'r');
>>
>> Note that these are not the same (albeit similar), which I had expected…
>>
>> best
>> Robert
>>
>>
>>
>> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com>
>> wrote:
>>
>> Dear Fieldtrippers,
>>
>> I'm looking into an issue of one of SPM users who gets different results
>> when doing TF decomposition compared to computing a spectrum for the same
>> time window. I'm not sure I got to the bottom of it yet but one thing I
>> found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
>> differently by increasing padding. For short padding the results are
>> similar but with increasing padding there are differences both in offset of
>> the spectrum and its overall shape. See attached images where the top one
>> shows original spectra and the bottom one aligns the lowermost bin to zero.
>>
>> Is this a bug or a feature?
>>
>> Below is the script that produces these plots. I could provide the data
>> as well but this could probably be reproduced with any data.
>>
>> Thanks,
>>
>> Vladimir
>>
>>
>> -------------------------------------
>>
>> pad = 0.5;%1%10
>>
>>
>> freqoi = 5:45;
>> timwin = 0.4+0*freqoi;
>>
>> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time,
>> 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>>
>> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
>>
>> figure;
>> subplot(2,1,1)
>> plot(freqoi, s1);
>> subplot(2,1,2);
>> plot(freqoi, s1-s1(1));
>> %%
>> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
>> 'hanning', 'freqoi', freqoi,...
>>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>>
>> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
>>
>> subplot(2,1,1)
>> hold on
>> plot(freqoi, s2, 'r');
>> subplot(2,1,2)
>> hold on
>> plot(freqoi, s2-s2(1), 'r');
>> <pad0_5.png><pad1.png><pad10.png>___________________________
>> ____________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
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From jan.schoffelen at donders.ru.nl  Thu Sep 21 12:36:30 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 10:36:30 +0000
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
 <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
Message-ID: <612489C6-6EFD-4D56-A9FC-189A180CC961@donders.ru.nl>

Don’t worry Vladimir,

Robert and I have discussed these issues at length on several occasions in the past ;)
Best wishes,
JM

On 21 Sep 2017, at 12:29, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Hi Jan-Mathijs,

Yes, you are right about Robert's example. But if you do:

pad = 10;

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning', 'pad', pad);

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');


You will see the problem that I'm talking about. We discussed with Robert yesterday and this is indeed 'a feature' which has to do with the fact that the outputs of mtmfft and mtmconvol have different units. The former is spectral density whereas the latter is spectral power.

Here is what Robert wrote me:

the units of computations (also here) are a known and long-standing issue. I know for a long time that the two have different scaling, but did not think about it for a long time. I recall something like this: To compare TFRs over frequencies, you don't want the bandwidth to affect the estimate. Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral resolution over which the signal(and noise) spreads is different. If you were to compute the TFR in V^2/Hz, the same V at 20Hz would have a different value, because the length of the wavelet affects the 1/Hz.
something related (but nevertheless different) applies to the mtmfft: if you want to estimate broadband activity in a window of 1 second or a window of 2 seconds, you would get different spectral resolutions. The nyquist is the same, but the power gets distributed over more bins between 0 and Fnyquist/2. That would cause the values to appear smaller in the 2-s case. Hence we compute spectral density, which somehow normalizes for this. I never found a really clear explanation, but google got me this https://dsp.stackexchange.com/questions/33957/what-is-the-difference-between-the-psd-and-the-power-spectrum
what confuses me is that power (or variance) is already normalized, i.e. sum of squared values divided by N. So we have energy (which increases with length), power (which does not increase with length), and power density


So one issue is that most people don't know about this including me and possibly you. I think a good solution would be to add an option to specify the output units for all the methods as there might be quite subtle considerations for choosing one over the other as Robert suggests.

Vladimir

On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi to all who’s reading along,

Perhaps the two cases will become more similar once the ‘timwin’ is increased in length for the mtmconvol case….

Best wishes,

JM

On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl<mailto:r.oostenveld at donders.ru.nl>> wrote:

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
<pad0_5.png><pad1.png><pad10.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Thu Sep 21 14:36:43 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 14:36:43 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
Message-ID: <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in
/external), to use SPM12 for segmentation of my template and my subject
MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
improvement over 8 when it comes to brain-segmentation, which now does not
require individual treatments anymore. It also outputs more compartments
which gives me a little bit more to work with when dealing with scans that
have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to
spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in
ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!
Stephen



On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi Stephen,
>
> Please note that FT now has full support for SPM12, both using the
> old-style segmentation, and the new one (the latter yielding 6 tissue
> types).
>
> Best,
> Jan-Mathijs
>
> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
> wrote:
>
> Dear all,
>
> I having some problems in normalizing MRIs for my study. Some have
> improper segmentation for which changing individual brain/scalp thresholds
> works in many cases but not all, e.g. when the scalp 'bleeds' into some
> noise outside of the head. Also, changing parameters in spm8 for
> normalization, such as number of iterations (directly in in spm_normalize,
> since FT does not pass these parameters) improves the transformation.
>
> However, some scans I cannot deal with, either because they have noise
> from outsides of the head 'bleed' onto the scalp, thereby preventing
> optimal scalp-segmentation and thereby normalization. Others have an
> inappropriate contrast MRI sequence.
>
> Some fMRI researchers advised me to use SPM12, because of its improved
> preprocessing procedures. However, it does not seem supported in FT yet.
> Does anyone have experience with this, and can perhaps share how they
> extracted the transformation matrix from the resulting nifti's?
>
> Thanks,
> Stephen
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From hgould at memphis.edu  Thu Sep 21 15:56:38 2017
From: hgould at memphis.edu (Herbert J Gould (hgould))
Date: Thu, 21 Sep 2017 13:56:38 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>,
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
Message-ID: <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>

I have retired please remove me from the mail list

Herbert Jay Gould
Professor Emeritus
The University of Memphis



Sent from my Verizon Wireless 4G LTE smartphone


-------- Original message --------
From: Stephen Whitmarsh
Date:09/21/2017 7:43 AM (GMT-06:00)
To: FieldTrip discussion list
Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!
Stephen



On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).

Best,
Jan-Mathijs

On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From a.stolk8 at gmail.com  Thu Sep 21 17:00:20 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Thu, 21 Sep 2017 08:00:20 -0700
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
Message-ID: <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>

Hey Stephen,

Look for discussions regarding spm12 and also dartel on bugzilla. It's been a while but as far as I can remember ft_volumenormalize is the only function now that has not been integrated. Reason being that it wasnt straightforward to house the dartel procedure under a single function, so this is ongoing work still. You can however use spm12's coregistration function with ft_volumerealign (for rigid body transformations), which Im using quite a bit and never let me down (and is much faster than before). But that wouldnt work for normalization to template space though (use spm8).

Best

> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu> wrote:
> 
> I have retired please remove me from the mail list 
> 
> Herbert Jay Gould
> Professor Emeritus
> The University of Memphis
> 
> 
> 
> Sent from my Verizon Wireless 4G LTE smartphone
> 
> 
> -------- Original message --------
> From: Stephen Whitmarsh
> Date:09/21/2017 7:43 AM (GMT-06:00)
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization
> 
> Dear Sarang and Jan-Mathijs,
> 
> Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.
> 
> Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12. 
> 
> In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.
> 
> Best wishes and thanks again!
> Stephen
> 
> 
> 
>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl> wrote:
>> Hi Stephen,
>> 
>> Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).
>> 
>> Best,
>> Jan-Mathijs
>> 
>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com> wrote:
>>> 
>>> Dear all,
>>> 
>>> I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.
>>> 
>>> However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.
>>> 
>>> Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's? 
>>> 
>>> Thanks,
>>> Stephen
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> 
>> 
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From zhangwenjia2732 at 126.com  Thu Sep 21 17:30:13 2017
From: zhangwenjia2732 at 126.com (=?GBK?B?1cXOxLzO?=)
Date: Thu, 21 Sep 2017 23:30:13 +0800 (CST)
Subject: [FieldTrip] Reading data too slow
Message-ID: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>





Dear all,


I have some problems in reading data into fieldtrip.
Specifically, I used EGI system to record EEG data and preprocessed them with Brainvison analyzer 
Then, I exported the preprocessed data into generic data format, making 3 files: .eeg, .vhdr and vmrk.
Last, I used ft_definetrial and ft_preprocessing to read these data into FieldTrip.
However, the reading is very very slow.


I tried to make only 2 channels left and tried methods as follow:
http://www.fieldtriptoolbox.org/faq/reading_is_slow_can_i_write_my_raw_data_to_a_more_efficient_file_format
But, they all did not work.


Does anyone know what I am doing wrong? Any advice very appreciated. 
Thank you


--

Wenjia
NYU Shanghai
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From stephen.whitmarsh at gmail.com  Thu Sep 21 17:52:31 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 17:52:31 +0200
Subject: [FieldTrip] Reading data too slow
In-Reply-To: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>
References: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>
Message-ID: <CAFrxm=yXm=k-AAaNjKgz0pGABc6pnYyP8hCgPQFy0j=e_3BUyg@mail.gmail.com>

Hi Wenjia,

It's impossible to give specific advice with no extra information.

How slow?
Is the data read at all?
Any error messages?
What script are you exactly running and what is the output?
See:
http://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community

I would also check your computer resources (CPU and memory) during loading
to see if you are running into a memory/CPU problem specific for your
system.
Finally, I would start with no filters. They sometimes take a while.

Cheers,
Stephen



On 21 September 2017 at 17:30, 张文嘉 <zhangwenjia2732 at 126.com> wrote:

>
>
> Dear all,
>
> I have some problems in reading data into fieldtrip.
> Specifically, I used EGI system to record EEG data and preprocessed them
> with Brainvison analyzer
> Then, I exported the preprocessed data into generic data format, making 3
> files: .eeg, .vhdr and vmrk.
> Last, I used ft_definetrial and ft_preprocessing to read these data into
> FieldTrip.
> However, the reading is very very slow.
>
> I tried to make only 2 channels left and tried methods as follow:
> http://www.fieldtriptoolbox.org/faq/reading_is_slow_can_i_
> write_my_raw_data_to_a_more_efficient_file_format
> But, they all did not work.
>
> Does anyone know what I am doing wrong? Any advice very appreciated.
> Thank you
>
> --
> Wenjia
> NYU Shanghai
>
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From stephen.whitmarsh at gmail.com  Thu Sep 21 18:20:47 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 18:20:47 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
Message-ID: <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>

Hi Arjen,

Thanks, and good to hear you've not been let down yet. It might be the fact
that I have some bad quality MRIs to deal with. However... does this
problem (see attached) ring a bell for anyone?:

Brain segmentation is proper, and co-registration with polhemus  head-shape
as well, but inverse warp to MNI result in a tilted grid. Linear vs.
non-linear transformation gives the same result. Other subjects going
through the same procedure work fine, except two others wherein I
identified it as a problem in segmenting the scalp and therefor the first
step of the normalization. This one looks absolutely fine in every other
regard, however.

I'm stumped...

    cfg                        = [];
    cfg.spmversion          = 'spm12';
    cfg.grid.warpmni        = 'yes';
    cfg.grid.template       = template_grid;
    cfg.grid.nonlinear      = 'yes';
    cfg.mri                 = mri_realigned;
    cfg.grid.unit           = 'mm';
    subject_grid            = ft_prepare_sourcemodel(cfg);

Cheers,
Stephen


On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> Hey Stephen,
>
> Look for discussions regarding spm12 and also dartel on bugzilla. It's
> been a while but as far as I can remember ft_volumenormalize is the only
> function now that has not been integrated. Reason being that it wasnt
> straightforward to house the dartel procedure under a single function, so
> this is ongoing work still. You can however use spm12's coregistration
> function with ft_volumerealign (for rigid body transformations), which Im
> using quite a bit and never let me down (and is much faster than before).
> But that wouldnt work for normalization to template space though (use spm8).
>
> Best
>
> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu>
> wrote:
>
> I have retired please remove me from the mail list
>
> Herbert Jay Gould
> Professor Emeritus
> The University of Memphis
>
>
>
> Sent from my Verizon Wireless 4G LTE smartphone
>
>
> -------- Original message --------
> From: Stephen Whitmarsh
> Date:09/21/2017 7:43 AM (GMT-06:00)
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
> normalization
>
> Dear Sarang and Jan-Mathijs,
>
> Thanks a lot. I am now able (after updating FT, which now includes SPM12
> in /external), to use SPM12 for segmentation of my template and my subject
> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
> improvement over 8 when it comes to brain-segmentation, which now does not
> require individual treatments anymore. It also outputs more compartments
> which gives me a little bit more to work with when dealing with scans that
> have bad delineation of the scalp for normalization.
>
> Pleas note that defaults seems to differ - some FT functions default to
> spm8, others to spm12.
>
> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
> 'spm12'. In other words the cfg.spmversion is not passed along.
>
> Best wishes and thanks again!
> Stephen
>
>
>
> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi Stephen,
>>
>> Please note that FT now has full support for SPM12, both using the
>> old-style segmentation, and the new one (the latter yielding 6 tissue
>> types).
>>
>> Best,
>> Jan-Mathijs
>>
>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
>> wrote:
>>
>> Dear all,
>>
>> I having some problems in normalizing MRIs for my study. Some have
>> improper segmentation for which changing individual brain/scalp thresholds
>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>> noise outside of the head. Also, changing parameters in spm8 for
>> normalization, such as number of iterations (directly in in spm_normalize,
>> since FT does not pass these parameters) improves the transformation.
>>
>> However, some scans I cannot deal with, either because they have noise
>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>> optimal scalp-segmentation and thereby normalization. Others have an
>> inappropriate contrast MRI sequence.
>>
>> Some fMRI researchers advised me to use SPM12, because of its improved
>> preprocessing procedures. However, it does not seem supported in FT yet.
>> Does anyone have experience with this, and can perhaps share how they
>> extracted the transformation matrix from the resulting nifti's?
>>
>> Thanks,
>> Stephen
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From a.stolk8 at gmail.com  Thu Sep 21 18:45:46 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Thu, 21 Sep 2017 09:45:46 -0700
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
Message-ID: <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>

First thought is a registration of brain outline to skull (instead of
brain), although at closer inspection the shift seems overall just a bit
too large for that. You could try calculating the normalization parameters
on skullstripped volumes (unless you want to keep non-brain tissue).

On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
stephen.whitmarsh at gmail.com> wrote:

> Hi Arjen,
>
> Thanks, and good to hear you've not been let down yet. It might be the
> fact that I have some bad quality MRIs to deal with. However... does this
> problem (see attached) ring a bell for anyone?:
>
> Brain segmentation is proper, and co-registration with polhemus
>  head-shape as well, but inverse warp to MNI result in a tilted grid.
> Linear vs. non-linear transformation gives the same result. Other subjects
> going through the same procedure work fine, except two others wherein I
> identified it as a problem in segmenting the scalp and therefor the first
> step of the normalization. This one looks absolutely fine in every other
> regard, however.
>
> I'm stumped...
>
>     cfg                        = [];
>     cfg.spmversion          = 'spm12';
>     cfg.grid.warpmni        = 'yes';
>     cfg.grid.template       = template_grid;
>     cfg.grid.nonlinear      = 'yes';
>     cfg.mri                 = mri_realigned;
>     cfg.grid.unit           = 'mm';
>     subject_grid            = ft_prepare_sourcemodel(cfg);
>
> Cheers,
> Stephen
>
>
> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>
>> Hey Stephen,
>>
>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>> been a while but as far as I can remember ft_volumenormalize is the only
>> function now that has not been integrated. Reason being that it wasnt
>> straightforward to house the dartel procedure under a single function, so
>> this is ongoing work still. You can however use spm12's coregistration
>> function with ft_volumerealign (for rigid body transformations), which Im
>> using quite a bit and never let me down (and is much faster than before).
>> But that wouldnt work for normalization to template space though (use spm8).
>>
>> Best
>>
>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu>
>> wrote:
>>
>> I have retired please remove me from the mail list
>>
>> Herbert Jay Gould
>> Professor Emeritus
>> The University of Memphis
>>
>>
>>
>> Sent from my Verizon Wireless 4G LTE smartphone
>>
>>
>> -------- Original message --------
>> From: Stephen Whitmarsh
>> Date:09/21/2017 7:43 AM (GMT-06:00)
>> To: FieldTrip discussion list
>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>> normalization
>>
>> Dear Sarang and Jan-Mathijs,
>>
>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>> in /external), to use SPM12 for segmentation of my template and my subject
>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>> improvement over 8 when it comes to brain-segmentation, which now does not
>> require individual treatments anymore. It also outputs more compartments
>> which gives me a little bit more to work with when dealing with scans that
>> have bad delineation of the scalp for normalization.
>>
>> Pleas note that defaults seems to differ - some FT functions default to
>> spm8, others to spm12.
>>
>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>
>> Best wishes and thanks again!
>> Stephen
>>
>>
>>
>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>> jan.schoffelen at donders.ru.nl> wrote:
>>
>>> Hi Stephen,
>>>
>>> Please note that FT now has full support for SPM12, both using the
>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>> types).
>>>
>>> Best,
>>> Jan-Mathijs
>>>
>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
>>> wrote:
>>>
>>> Dear all,
>>>
>>> I having some problems in normalizing MRIs for my study. Some have
>>> improper segmentation for which changing individual brain/scalp thresholds
>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>> noise outside of the head. Also, changing parameters in spm8 for
>>> normalization, such as number of iterations (directly in in spm_normalize,
>>> since FT does not pass these parameters) improves the transformation.
>>>
>>> However, some scans I cannot deal with, either because they have noise
>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>> optimal scalp-segmentation and thereby normalization. Others have an
>>> inappropriate contrast MRI sequence.
>>>
>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>> Does anyone have experience with this, and can perhaps share how they
>>> extracted the transformation matrix from the resulting nifti's?
>>>
>>> Thanks,
>>> Stephen
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From sarang at cfin.au.dk  Thu Sep 21 19:30:25 2017
From: sarang at cfin.au.dk (Sarang S. Dalal)
Date: Thu, 21 Sep 2017 17:30:25 +0000
Subject: [FieldTrip] Using fixed orientations for men-source estimation
In-Reply-To: <71D8A67A81D69A4CB5BE2B979021C26ECAFFE748@esen3.imed.uni-magdeburg.de>
References: <71D8A67A81D69A4CB5BE2B979021C26ECAFFE748@esen3.imed.uni-magdeburg.de>
Message-ID: <1506015025.9072.22.camel@cfin.au.dk>

Hi Christian,

I had flagged your email to follow-up later but lost track of it -- it seems you didn't get a response yet, but I hope mine is still useful!

The strategy that the 'fixedori' implements for the beamformer variants (and sLORETA) are not based on the anatomical normal, but rather the direction that maximizes the theoretical SNR (adaptively determined from the signal characteristics). This optimal direction is dependent on the particular weight calculation formula for each source localization variant.

Therefore, a similar SNR optimization strategy for minimum norm would actually require a different formula than you see used for the others. It's simple enough to implement if you know what that formula is. :-) It is likely to be contained in the book by Sekihara & Nagarajan (2008), if you'd like to have a go at it yourself.

That said, min-norm is often (or perhaps usually) performed with the solution space constrained to gray matter voxels, and the orientations defined to be normal to the cortical surface. If you independently have a way to obtain anatomically derived orientations, then you can manually provide them in lf.ori. (Or maybe there is a FieldTrip function that could obtain these normals from the MRI segmentation procedure?)

Cheers,
Sarang



On Tue, 2017-08-01 at 11:11 +0000, christian.merkel at med.ovgu.de wrote:

Hello,


I am running ft_sourceanalysis and am wondering why I can restrict the parameter-estimation in LCMV and sLORETA by setting the parameter 'fixedori' but not when using MNE. Shouldn't one be able to also just use the normal direction of each source position here as well? Can I just apply the same logic in the script 'minimumnormestimate' to change the field 'lf.ori' as, for example, in 'ft_sloreta' or would this be problematic down the line?

Thank You,

Christian

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Fri Sep 22 10:09:11 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 10:09:11 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
 <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
Message-ID: <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>

Hi Arjen,

Indeed, I do not think there is a problem with segmentation of brain/skull,
as can be seen on the image. Stripping some skin of two subjects
(thresholding the 'soft_tissue' probability output of SPM12, then removing
it) with a similar problem of solved the rotation, but resulted in too
small grids... On this subject I attached this procedure has no effect.

However, at least for those other subjects normalization on skullstripped,
or rather, scalpstripped MRIs might do the trick. As I understand it,
however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
with (affine) transformation based on the scalp first, after which it
optimizes it based on brain segmentation. I would not know how to therefor
do normalization without scalp. In fact, it expects a full volumetric
image, not a (pre-)segmented one.

Cheers,
Stephen

On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> First thought is a registration of brain outline to skull (instead of
> brain), although at closer inspection the shift seems overall just a bit
> too large for that. You could try calculating the normalization parameters
> on skullstripped volumes (unless you want to keep non-brain tissue).
>
> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com> wrote:
>
>> Hi Arjen,
>>
>> Thanks, and good to hear you've not been let down yet. It might be the
>> fact that I have some bad quality MRIs to deal with. However... does this
>> problem (see attached) ring a bell for anyone?:
>>
>> Brain segmentation is proper, and co-registration with polhemus
>>  head-shape as well, but inverse warp to MNI result in a tilted grid.
>> Linear vs. non-linear transformation gives the same result. Other subjects
>> going through the same procedure work fine, except two others wherein I
>> identified it as a problem in segmenting the scalp and therefor the first
>> step of the normalization. This one looks absolutely fine in every other
>> regard, however.
>>
>> I'm stumped...
>>
>>     cfg                        = [];
>>     cfg.spmversion          = 'spm12';
>>     cfg.grid.warpmni        = 'yes';
>>     cfg.grid.template       = template_grid;
>>     cfg.grid.nonlinear      = 'yes';
>>     cfg.mri                 = mri_realigned;
>>     cfg.grid.unit           = 'mm';
>>     subject_grid            = ft_prepare_sourcemodel(cfg);
>>
>> Cheers,
>> Stephen
>>
>>
>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>
>>> Hey Stephen,
>>>
>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>> been a while but as far as I can remember ft_volumenormalize is the only
>>> function now that has not been integrated. Reason being that it wasnt
>>> straightforward to house the dartel procedure under a single function, so
>>> this is ongoing work still. You can however use spm12's coregistration
>>> function with ft_volumerealign (for rigid body transformations), which Im
>>> using quite a bit and never let me down (and is much faster than before).
>>> But that wouldnt work for normalization to template space though (use spm8).
>>>
>>> Best
>>>
>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>> hgould at memphis.edu> wrote:
>>>
>>> I have retired please remove me from the mail list
>>>
>>> Herbert Jay Gould
>>> Professor Emeritus
>>> The University of Memphis
>>>
>>>
>>>
>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>
>>>
>>> -------- Original message --------
>>> From: Stephen Whitmarsh
>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>> To: FieldTrip discussion list
>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>> normalization
>>>
>>> Dear Sarang and Jan-Mathijs,
>>>
>>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>>> in /external), to use SPM12 for segmentation of my template and my subject
>>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>> require individual treatments anymore. It also outputs more compartments
>>> which gives me a little bit more to work with when dealing with scans that
>>> have bad delineation of the scalp for normalization.
>>>
>>> Pleas note that defaults seems to differ - some FT functions default to
>>> spm8, others to spm12.
>>>
>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>
>>> Best wishes and thanks again!
>>> Stephen
>>>
>>>
>>>
>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>> jan.schoffelen at donders.ru.nl> wrote:
>>>
>>>> Hi Stephen,
>>>>
>>>> Please note that FT now has full support for SPM12, both using the
>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>> types).
>>>>
>>>> Best,
>>>> Jan-Mathijs
>>>>
>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>
>>>> Dear all,
>>>>
>>>> I having some problems in normalizing MRIs for my study. Some have
>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>> since FT does not pass these parameters) improves the transformation.
>>>>
>>>> However, some scans I cannot deal with, either because they have noise
>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>> inappropriate contrast MRI sequence.
>>>>
>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>> Does anyone have experience with this, and can perhaps share how they
>>>> extracted the transformation matrix from the resulting nifti's?
>>>>
>>>> Thanks,
>>>> Stephen
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From stephen.whitmarsh at gmail.com  Fri Sep 22 13:01:25 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 13:01:25 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
 <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
 <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>
Message-ID: <CAFrxm=yCDxWGxk6tusWb8aN09i8yujxpq8BgkVUcE9n0peSGJQ@mail.gmail.com>

Hi Arjen, Jan-Mathijs, et. al.,

It seems the rotation was caused by a bug my side. The segmentation using
SPM12 solved problems caused by low quality MRIS.
Thanks!
Stephen


On 22 September 2017 at 10:09, Stephen Whitmarsh <
stephen.whitmarsh at gmail.com> wrote:

> Hi Arjen,
>
> Indeed, I do not think there is a problem with segmentation of
> brain/skull, as can be seen on the image. Stripping some skin of two
> subjects (thresholding the 'soft_tissue' probability output of SPM12, then
> removing it) with a similar problem of solved the rotation, but resulted in
> too small grids... On this subject I attached this procedure has no effect.
>
> However, at least for those other subjects normalization on skullstripped,
> or rather, scalpstripped MRIs might do the trick. As I understand it,
> however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
> with (affine) transformation based on the scalp first, after which it
> optimizes it based on brain segmentation. I would not know how to therefor
> do normalization without scalp. In fact, it expects a full volumetric
> image, not a (pre-)segmented one.
>
> Cheers,
> Stephen
>
> On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>
>> First thought is a registration of brain outline to skull (instead of
>> brain), although at closer inspection the shift seems overall just a bit
>> too large for that. You could try calculating the normalization parameters
>> on skullstripped volumes (unless you want to keep non-brain tissue).
>>
>> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
>> stephen.whitmarsh at gmail.com> wrote:
>>
>>> Hi Arjen,
>>>
>>> Thanks, and good to hear you've not been let down yet. It might be the
>>> fact that I have some bad quality MRIs to deal with. However... does this
>>> problem (see attached) ring a bell for anyone?:
>>>
>>> Brain segmentation is proper, and co-registration with polhemus
>>>  head-shape as well, but inverse warp to MNI result in a tilted grid.
>>> Linear vs. non-linear transformation gives the same result. Other subjects
>>> going through the same procedure work fine, except two others wherein I
>>> identified it as a problem in segmenting the scalp and therefor the first
>>> step of the normalization. This one looks absolutely fine in every other
>>> regard, however.
>>>
>>> I'm stumped...
>>>
>>>     cfg                        = [];
>>>     cfg.spmversion          = 'spm12';
>>>     cfg.grid.warpmni        = 'yes';
>>>     cfg.grid.template       = template_grid;
>>>     cfg.grid.nonlinear      = 'yes';
>>>     cfg.mri                 = mri_realigned;
>>>     cfg.grid.unit           = 'mm';
>>>     subject_grid            = ft_prepare_sourcemodel(cfg);
>>>
>>> Cheers,
>>> Stephen
>>>
>>>
>>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>>
>>>> Hey Stephen,
>>>>
>>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>>> been a while but as far as I can remember ft_volumenormalize is the only
>>>> function now that has not been integrated. Reason being that it wasnt
>>>> straightforward to house the dartel procedure under a single function, so
>>>> this is ongoing work still. You can however use spm12's coregistration
>>>> function with ft_volumerealign (for rigid body transformations), which Im
>>>> using quite a bit and never let me down (and is much faster than before).
>>>> But that wouldnt work for normalization to template space though (use spm8).
>>>>
>>>> Best
>>>>
>>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>>> hgould at memphis.edu> wrote:
>>>>
>>>> I have retired please remove me from the mail list
>>>>
>>>> Herbert Jay Gould
>>>> Professor Emeritus
>>>> The University of Memphis
>>>>
>>>>
>>>>
>>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>>
>>>>
>>>> -------- Original message --------
>>>> From: Stephen Whitmarsh
>>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>>> To: FieldTrip discussion list
>>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>>> normalization
>>>>
>>>> Dear Sarang and Jan-Mathijs,
>>>>
>>>> Thanks a lot. I am now able (after updating FT, which now includes
>>>> SPM12 in /external), to use SPM12 for segmentation of my template and my
>>>> subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>>> require individual treatments anymore. It also outputs more compartments
>>>> which gives me a little bit more to work with when dealing with scans that
>>>> have bad delineation of the scalp for normalization.
>>>>
>>>> Pleas note that defaults seems to differ - some FT functions default to
>>>> spm8, others to spm12.
>>>>
>>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>>
>>>> Best wishes and thanks again!
>>>> Stephen
>>>>
>>>>
>>>>
>>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>>> jan.schoffelen at donders.ru.nl> wrote:
>>>>
>>>>> Hi Stephen,
>>>>>
>>>>> Please note that FT now has full support for SPM12, both using the
>>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>>> types).
>>>>>
>>>>> Best,
>>>>> Jan-Mathijs
>>>>>
>>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>>
>>>>> Dear all,
>>>>>
>>>>> I having some problems in normalizing MRIs for my study. Some have
>>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>>> since FT does not pass these parameters) improves the transformation.
>>>>>
>>>>> However, some scans I cannot deal with, either because they have noise
>>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>>> inappropriate contrast MRI sequence.
>>>>>
>>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>>> Does anyone have experience with this, and can perhaps share how they
>>>>> extracted the transformation matrix from the resulting nifti's?
>>>>>
>>>>> Thanks,
>>>>> Stephen
>>>>> _______________________________________________
>>>>> fieldtrip mailing list
>>>>> fieldtrip at donders.ru.nl
>>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>>
>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> fieldtrip mailing list
>>>>> fieldtrip at donders.ru.nl
>>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
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From hamedtaheri at yahoo.com  Fri Sep 22 13:53:44 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Fri, 22 Sep 2017 11:53:44 +0000 (UTC)
Subject: [FieldTrip] Artifact Rejection Problem
References: <258456736.7597235.1506081224221.ref@mail.yahoo.com>
Message-ID: <258456736.7597235.1506081224221@mail.yahoo.com>

Hello Dear Fieldtrip users,
I have an EEG signal which I want to do artifact rejection on it.I've recorded the EEG during watching a video clip. 
My signal is 100 seconds and I've selected 50 seconds.I can find EOG artifact but I can't reject it. Would you please let me know how can I do it.


cfg  = [];
cfg.dataset   = 'myfile.eeg';  %BrainVision Recoreder EEG

cfg.trialdef.triallength   = inf;
cfg.trialdef.ntrials         = inf;
cfg   = ft_definetrial(cfg); trl     = cfg.trl;
data_org = ft_preprocessing(cfg);

%Select 30sec of data
cfg.latency        = 'all';
cfg.latency     = [0 30]; %start point and end point
cfg.avgovertime = 'no';
cfg.nanmean     = 'no';
data_s = ft_selectdata(cfg, data_org);
[cfg, artifact] = ft_artifact_eog(cfg,data_s);clean_data = ft_rejectartifact(cfg,data_s);
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From stephen.whitmarsh at gmail.com  Fri Sep 22 14:12:11 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 14:12:11 +0200
Subject: [FieldTrip] Warnings on CentOS break code?
Message-ID: <CAFrxm=yerQMk1i4DMm1XJp9JEGyO6Q5hf7_qBLLqQcRZmrym1w@mail.gmail.com>

Hi there,

Since upgrading to the latest FT version, some warnings throw an error
because FT cannot determine it's in a CentOS distro. At least, that's what
I think it is?

Am I missing something?

Best,
Stephen

cfg = [];
cfg.artfctdef               = artdef_MEG{ipart};
cfg.artfctdef.reject        = 'partial';
cfg.artfctdef.minaccepttim  = 3;
data{ipart}                 = ft_rejectartifact(cfg,data{ipart});

results in:

Error using ft_platform_supports (line 134)
unsupported value for first argument: html

Error in ft_notification (line 376)
          if ft_platform_supports('html')

Error in ft_warning (line 63)
  ft_notification(varargin{:});

Error in getdimord>warning_dimord_could_not_be_determined (line 621)
  ft_warning('%s\n\n%s', msg,content);

Error in getdimord (line 572)
  warning_dimord_could_not_be_determined(field,data);

Error in ft_selectdata (line 201)
  dimord{i} = getdimord(varargin{1}, datfield{i});

Error in WANDER_common_filter_DICS (line 85)
    hdr = ft_selectdata(cfg,hdr);

134     error('unsupported value for first argument: %s', what);
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From hamedtaheri at yahoo.com  Fri Sep 22 19:06:53 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Fri, 22 Sep 2017 17:06:53 +0000 (UTC)
Subject: [FieldTrip] Artifact Removing Problem
References: <28989140.7825054.1506100013966.ref@mail.yahoo.com>
Message-ID: <28989140.7825054.1506100013966@mail.yahoo.com>

Hi all
I've tried to remove EOG, jump and muscle artifact from my EEG.I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts. ( No filter, remove) . I want to filter my signal no remove some part of mt signal. 
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.Could you please help me what is my wrong?
Best Regards,Hamed
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From hamedtaheri at yahoo.com  Sun Sep 24 17:37:43 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Sun, 24 Sep 2017 15:37:43 +0000 (UTC)
Subject: [FieldTrip] Artifact Rejection Problem
References: <383387083.4716793.1506267463295.ref@mail.yahoo.com>
Message-ID: <383387083.4716793.1506267463295@mail.yahoo.com>

Hi all
I've tried to remove EOG, jump and muscle artifact from my EEG.I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts. I want to filter my signal no remove some part of my signal. 
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.Could you please help me what is my wrong?
cfg.artfctdef.eog.artifact = artifact_EOG; 
cfg.artfctdef.jump.artifact = artifact_jump;
cfg.artfctdef.muscle.artifact = artifact_muscle;
cfg.artfctdef.reject = 'complet' ;
data_no_artifacts = ft_rejectartifact(cfg,data_int);


Best Regards,Hamed

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From mailtome.2113 at gmail.com  Mon Sep 25 03:23:35 2017
From: mailtome.2113 at gmail.com (Arti Abhishek)
Date: Mon, 25 Sep 2017 11:23:35 +1000
Subject: [FieldTrip] Question regarding clusterplot
Message-ID: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>

Dear list,

I am trying to plot significant clusters from the cluster based permutation
test on the ERPs. I want to plot the p values on a binary fashion (p<.05).
I just don't want to highlight the electrodes, but I want to interpolate
the p values and plot topography (just like ERP topography, but in abinary
fashion). I was wondering whether there is a way to do it?

Thanks,
Arti
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From tzvetan.popov at uni-konstanz.de  Mon Sep 25 06:29:05 2017
From: tzvetan.popov at uni-konstanz.de (Tzvetan Popov)
Date: Mon, 25 Sep 2017 06:29:05 +0200
Subject: [FieldTrip] Question regarding clusterplot
In-Reply-To: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>
References: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>
Message-ID: <BCD13C5C-6A8F-4931-BF0A-4BC1E4ABCF05@uni-konstanz.de>

Hi Arti,

you could specify cfg.parameter = ‘mask’; instead of ‘avg’ which is the default.

best
tzvetan

> Am 25.09.2017 um 03:23 schrieb Arti Abhishek <mailtome.2113 at gmail.com>:
> 
> Dear list,
> 
> I am trying to plot significant clusters from the cluster based permutation test on the ERPs. I want to plot the p values on a binary fashion (p<.05). I just don't want to highlight the electrodes, but I want to interpolate the p values and plot topography (just like ERP topography, but in abinary fashion). I was wondering whether there is a way to do it?
> 
> Thanks,
> Arti
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip




From simeon.wong at sickkids.ca  Mon Sep 25 16:45:04 2017
From: simeon.wong at sickkids.ca (Simeon Wong)
Date: Mon, 25 Sep 2017 14:45:04 +0000
Subject: [FieldTrip] Artifact Rejection Problem
In-Reply-To: <383387083.4716793.1506267463295@mail.yahoo.com>
References: <383387083.4716793.1506267463295.ref@mail.yahoo.com>,
 <383387083.4716793.1506267463295@mail.yahoo.com>
Message-ID: <YTXPR0101MB1456A96D8CE2B437CDEFE9A3FD7A0@YTXPR0101MB1456.CANPRD01.PROD.OUTLOOK.COM>

Hi Hamed,

I believe ft_rejectartefact does not offer an option to simply remove any artefact. Removing artefacts is actually a non-trivial process that you can approach from several different ways.

Try using ft_componentanalysis to apply ICA to remove eye blinks and some eye movement. I'm not too sure about muscle artefact in EEG but perhaps setting a bandpass filter from 1-30 Hz may help attenuate most of it. You probably don't need to worry about jump artifacts since that typically applies only to MEG.


Regards,
Simeon Wong
________________________________
From: fieldtrip-bounces at science.ru.nl <fieldtrip-bounces at science.ru.nl> on behalf of Hamed Taheri <hamedtaheri at yahoo.com>
Sent: September 24, 2017 11:37:43 AM
To: FieldTrip Discussion List
Subject: [FieldTrip] Artifact Rejection Problem

Hi all

I've tried to remove EOG, jump and muscle artifact from my EEG.
I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts.
I want to filter my signal no remove some part of my signal.
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.
Could you please help me what is my wrong?

cfg.artfctdef.eog.artifact = artifact_EOG;
cfg.artfctdef.jump.artifact = artifact_jump;
cfg.artfctdef.muscle.artifact = artifact_muscle;
cfg.artfctdef.reject = 'complet' ;
data_no_artifacts = ft_rejectartifact(cfg,data_int);


Best Regards,
Hamed


________________________________

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From bqrosen at ucsd.edu  Mon Sep 25 22:05:39 2017
From: bqrosen at ucsd.edu (Burke Rosen)
Date: Mon, 25 Sep 2017 13:05:39 -0700
Subject: [FieldTrip] ft_combineplanar 'svd' method
Message-ID: <C10A6011-0A3E-419C-8396-E238EFA66428@ucsd.edu>

Hello,

What is the principle behind the ‘svd’ and ‘absvd’ methods for ft_combineplanar?
And/or is there a citation which introduces these methods?

Thank you,

Burke Rosen







From michak at is.umk.pl  Mon Sep 25 23:44:00 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Mon, 25 Sep 2017 23:44:00 +0200
Subject: [FieldTrip] AAL Surface plot - weird black spots
In-Reply-To: <CAEJSOqhQCVyPfR1woRC69a8s1cgJimyfe3GBEuc38Uds9cVRbg@mail.gmail.com>
References: <CAEJSOqhQCVyPfR1woRC69a8s1cgJimyfe3GBEuc38Uds9cVRbg@mail.gmail.com>
Message-ID: <CAEJSOqg7wr4xn1b19sr1TKbOLMvpKAj+nFnZR4QONf_=_rFZHw@mail.gmail.com>

Dear Fieldtrippers,

I found the solution and it is simple. Just make sure that you have in your
config follwing lines:

cfg.projmethod = 'project'
cfg.projvec = [0 5]

Have a nice plots !


Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics

2017-07-31 14:15 GMT+02:00 Michał Komorowski <michak at is.umk.pl>:

> Dear Fieldtrippers,
>
> I am trying to reproduce brain surface pictures from this paper (Fig.5) :
> http://journals.plos.org/plosbiology/article?id=10.
> 1371/journal.pbio.1002498
>
> I wonder why I get weid black spots in surface plot (e.g. occipital area).
> What should I do to get those nice picures from link above?
> What I am doing wrong (code below)?
>
> Code for generating erroneous pictures (see attachment):
>
> mrifile             = [FieldtripPath 'template/anatomy/single_subj_
> T1.nii']
>
> mri = ft_read_mri(mrifile)
> mri.coordsys = 'mni'; % to prevent manual fixing of coordsys
>
> atlaspath       = [FieldtripPath 'template/atlas/aal/ROI_MNI_V4.nii'];
> atlas           = ft_read_atlas(atlaspath)
> atlas.anatomy   = mri.anatomy;
>
> cfg                     = [];
> cfg.method              = 'surface';
> cfg.projmethod          = 'project';
> cfg.camlight            = 'yes';
> %cfg.surffile = [FieldtripPath 'template/anatomy/surface_pial_left.mat'];
> % uncomment to project half brain
> cfg.locationcoordinates = 'voxel';
> cfg.cmap                = jet(116);
> cfg.cmap                = [[0,0,0]; cfg.cmap]
> cfg.funcolormap         = cfg.cmap;
> cfg.funparameter        = 'tissue';
> cfg.atlas               = atlaspath;
> ft_sourceplot(cfg, atlas)
>
>
> % check fit anatomy to atlas
> cfg                     = [];
> cfg.method              = 'ortho';
> cfg.locationcoordinates = 'voxel';
> cfg.cmap                = jet(116);
> cfg.cmap                = [[0,0,0]; cfg.cmap] % color map
> cfg.funcolormap         = cfg.cmap;
> cfg.funparameter        = 'tissue';
> ft_sourceplot(cfg, atlas)
>
> Best wishes.
>
> Michał Komorowski
>
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From nirofir2 at gmail.com  Tue Sep 26 15:12:30 2017
From: nirofir2 at gmail.com (Nir Ofir)
Date: Tue, 26 Sep 2017 16:12:30 +0300
Subject: [FieldTrip] Variable Number of Tapers in 'mtmfft' Frequency Analysis
Message-ID: <CAKiBOH=U2m3JYMrpitfbLi-bagc+yu0fw1yK2tJ7Lj8YoLZuuA@mail.gmail.com>

Hi Fieldtrip users,

ft_freqanalysis (FT version 20170404) does not allow using a variable
number of tapers in 'mtmfft' mode (lines 462-465 display a warning and keep
only the first element of cfg.tapsmofrq), but it seems like
ft_specest_mtmfft does have an implementation of a variable number of
tapers (lines 286-348). It also seems like mtm_specest_mtmconvol, which
allows variable number of tapers, calls ft_specest_mtmfft. So 2 questions:
1. Is the variable number of tapers option used in mtmfft in some other way?
2. What is the reason for not allowing a variable number of tapers in
mtmfft generally?

Thanks!
Nir Ofir
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From bog.louisa at gmail.com  Wed Sep 27 22:01:53 2017
From: bog.louisa at gmail.com (Louisa Bogaerts)
Date: Wed, 27 Sep 2017 23:01:53 +0300
Subject: [FieldTrip] reading in and preprocessing gtec_mat data
Message-ID: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>

Hello everyone,



In the lab or Dr. Landau we recently started using a *g.tech EEG setup* and
*Simulink* record the data. We used the newest version of Fieldtrip to try
analyze the data.



Simulink outputs the data as a .mat file (an example here:
https://www.dropbox.com/s/6xgio9w81qx94bq/example.mat?dl=0), and according
to the fieldtrip documentation this data format should now be supported: e.g.,
https://github.com/fieldtrip/fieldtrip/blob/master/fileio/ft_read_data.m,
lines 274-276:

if any(strcmp(dataformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat',

'gtec_hdf5', 'mega_neurone'}))



However, it seems that multiple Fieldtrip functions are “looking” for a
header file that is not found.

-   When reading in the data with ft_read_data() we get the following error
messages (whereas simply loading them with load() works fine):

Error using ft_notification (line 340)

unsupported header format "matlab"



Error in ft_error (line 39)

  ft_notification(varargin{:});



Error in ft_read_header (line 2325)

        ft_error('unsupported header format "%s"', headerformat);



Error in ft_read_data (line 200)

  hdr = ft_read_header(filename, 'headerformat', headerformat, 'chanindx',

  chanindx, 'checkmaxfilter', checkmaxfilter);



- The same error messages show when using ft_preprocessing().



Does anyone have experience reading in and preprocessing gtech_mat data and
can he/she help us understand how to save the header info so that fieldtrip
can read it and recognise the data as gtec_mat?



Any help will be very much appreciated.



Louisa, Omri & Flor
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From isac.sehlstedt at psy.gu.se  Fri Sep 29 12:19:51 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Fri, 29 Sep 2017 10:19:51 +0000
Subject: [FieldTrip] Follow up question: Computing pca variables (i.e.
 Latent, and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
References: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
Message-ID: <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,


This is a kind reminder of a follow-up question to a previous question with the same mail-topic.


I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.


Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?


Pictures are found using this link:

https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0


Very Best,

Isac



————————————————— The code —————————————————


clear all; close all;


%% Load

load('averages_for_ft.mat')




%% define layout

cfg = [];

cfg.elec=PreOdd_ft{1, 1}.elec;

cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)

layout = ft_prepare_layout(cfg)




%% Make the computations


% Dummy varibles

Cond1 = [];

Cond2 = [];

theDiff = [];

theDiff_ft = {};



%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions

    curr_Cond2 = Cond2_ft{i}.avg;

    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions

    cfg = [];

    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});

    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math

    cfg  = [];

    cfg.operation = 'subtract';

    cfg.parameter = 'avg';

    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);

    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions

    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.

    Cond2 = [Cond2 curr_Cond2];

    Cond1 = [Cond1 curr_Cond1];

    theDiff = [theDiff curr_difference_avg];

end




%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};

dummy_Cond2.avg = Cond2;

dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};

dummy_Cond1.avg = Cond1;

dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};

dummy_theDiff.avg = theDiff;

dummy_theDiff.time = 1:1:size(theDiff,2);




%% Run the PCA



cfg = [];

cfg.method  = 'pca';

cfg.layout = layout;


Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);

Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);

theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);




%% Revert back to subject level


cfgCond2 = [];

cfgCond2.unmixing = Cond2_comp.unmixing;

cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];

cfgCond1.unmixing = Cond1_comp.unmixing;

cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];

cfgtheDiff.unmixing = theDiff_comp.unmixing;

cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)

    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});

    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});

    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );

end
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From jan.schoffelen at donders.ru.nl  Fri Sep 29 12:36:30 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Fri, 29 Sep 2017 10:36:30 +0000
Subject: [FieldTrip] Follow up question: Computing pca variables (i.e.
 Latent, and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>
References: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
 <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>
Message-ID: <97B82971-ABD0-49F2-81C4-235AE4D09162@donders.ru.nl>

perhaps you may want to check the ‘avg’ field.

JM

On 29 Sep 2017, at 12:19, Isac Sehlstedt <isac.sehlstedt at psy.gu.se<mailto:isac.sehlstedt at psy.gu.se>> wrote:

Dear fieldtripers,

This is a kind reminder of a follow-up question to a previous question with the same mail-topic.

I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.

Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?

Pictures are found using this link:
https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0

Very Best,
Isac


————————————————— The code —————————————————

clear all; close all;

%% Load
load('averages_for_ft.mat')



%% define layout
cfg = [];
cfg.elec=PreOdd_ft{1, 1}.elec;
cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)
layout = ft_prepare_layout(cfg)



%% Make the computations

% Dummy varibles
Cond1 = [];
Cond2 = [];
theDiff = [];
theDiff_ft = {};


%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions
    curr_Cond2 = Cond2_ft{i}.avg;
    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions
    cfg = [];
    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});
    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math
    cfg  = [];
    cfg.operation = 'subtract';
    cfg.parameter = 'avg';
    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);
    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions
    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.
    Cond2 = [Cond2 curr_Cond2];
    Cond1 = [Cond1 curr_Cond1];
    theDiff = [theDiff curr_difference_avg];
end



%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};
dummy_Cond2.avg = Cond2;
dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};
dummy_Cond1.avg = Cond1;
dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};
dummy_theDiff.avg = theDiff;
dummy_theDiff.time = 1:1:size(theDiff,2);



%% Run the PCA



cfg = [];
cfg.method  = 'pca';
cfg.layout = layout;

Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);
Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);
theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);



%% Revert back to subject level

cfgCond2 = [];
cfgCond2.unmixing = Cond2_comp.unmixing;
cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];
cfgCond1.unmixing = Cond1_comp.unmixing;
cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];
cfgtheDiff.unmixing = theDiff_comp.unmixing;
cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)
    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});
    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});
    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );
end
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Fri Sep 29 12:42:41 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 29 Sep 2017 12:42:41 +0200
Subject: [FieldTrip] reading in and preprocessing gtec_mat data
In-Reply-To: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>
References: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>
Message-ID: <CAFrxm=w7zoX0bberQ2t-tzAmCCRjy9YXM3noDsaapZ7OozM8WQ@mail.gmail.com>

Hi Louisa et al,

It seems that you are actually not trying to read data in the Gtec data
format, but that of simulink (which was saved as a mat file, as a matlab
file).
So, you should be able to just read your mat file and then put your data in
a fieldtrip data structure.

See: http://www.fieldtriptoolbox.org/faq/how_can_i_import_my_own_dataformat

Cheers,
Stephen










On 27 September 2017 at 22:01, Louisa Bogaerts <bog.louisa at gmail.com> wrote:

> Hello everyone,
>
>
>
> In the lab or Dr. Landau we recently started using a *g.tech EEG setup*
> and *Simulink* record the data. We used the newest version of Fieldtrip
> to try analyze the data.
>
>
>
> Simulink outputs the data as a .mat file (an example here:
> https://www.dropbox.com/s/6xgio9w81qx94bq/example.mat?dl=0), and
> according to the fieldtrip documentation this data format should now be
> supported: e.g., https://github.com/fieldtrip/f
> ieldtrip/blob/master/fileio/ft_read_data.m, lines 274-276:
>
> if any(strcmp(dataformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat',
>
> 'gtec_hdf5', 'mega_neurone'}))
>
>
>
> However, it seems that multiple Fieldtrip functions are “looking” for a
> header file that is not found.
>
> -   When reading in the data with ft_read_data() we get the following
> error messages (whereas simply loading them with load() works fine):
>
> Error using ft_notification (line 340)
>
> unsupported header format "matlab"
>
>
>
> Error in ft_error (line 39)
>
>   ft_notification(varargin{:});
>
>
>
> Error in ft_read_header (line 2325)
>
>         ft_error('unsupported header format "%s"', headerformat);
>
>
>
> Error in ft_read_data (line 200)
>
>   hdr = ft_read_header(filename, 'headerformat', headerformat, 'chanindx',
>
>   chanindx, 'checkmaxfilter', checkmaxfilter);
>
>
>
> - The same error messages show when using ft_preprocessing().
>
>
>
> Does anyone have experience reading in and preprocessing gtech_mat data
> and can he/she help us understand how to save the header info so that
> fieldtrip can read it and recognise the data as gtec_mat?
>
>
>
> Any help will be very much appreciated.
>
>
>
> Louisa, Omri & Flor
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From psc.dav at gmail.com  Sat Sep  2 16:45:47 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Sat, 2 Sep 2017 16:45:47 +0200
Subject: [FieldTrip] Fwd: single trials eLoreta
In-Reply-To: <CAK-BAC9v4U74suf_A4WNKKHRZtNM=zWWR7ZdpFSjQVzXrfp=uA@mail.gmail.com>
References: <CAK-BAC9v4U74suf_A4WNKKHRZtNM=zWWR7ZdpFSjQVzXrfp=uA@mail.gmail.com>
Message-ID: <CAK-BAC_sO4pJiSnNC1qY2EYdojO1XJgmmmhKmKGLLhF-F0QT_A@mail.gmail.com>

Dear fieldtrip experts,
I am posting again my questions, hoping that someone has experience with a
similar pipeline and can give some feedback.

I am trying to use the eLoreta method to get single-trial estimates of
source activity from specific ROIs, in the following way:

% eLORETA
cfg = [];
cfg.method                          = 'eloreta';
cfg.grid                                  = leadfield;
cfg.headmodel                       = headmodel;
cfg.eloreta.keepfilter              = 'yes';
cfg.eloreta.normalize               = 'yes';
cfg.eloreta.lambda                  = 0.05;
     *(1)
cfg.eloreta.projectnoise            = 'yes';
eLO_source                          = ft_sourceanalysis(cfg,data);

% in the above line, "data" is the results of ft_timelockanalysis
% with cfg.covariance = 'yes';
     *(2)

% then I put the source positions from the MNI template I
% used for the sourcemodel (see:
% http://www.fieldtriptoolbox.org/tutorial/sourcemodel#subject-
% specific_grids_that_are_equivalent_across_subjects_in_normalized_space
eLO_source.pos                      = template_grid.pos;
iPOS                                = eLO_source.pos;
iPOS(eLO_source.inside==0,:)        = NaN; % only points inside gray matter

% Then I select ROIs (here only one for simplicity) to extract single-trial
source activity:
[v,I]      = min(pdist2(iPOS, ROIs_mni , 'euclidean'));

% And I multiply the spatial filter for the EEG data in each trial
W            = eLO_source.avg.filter{I}; % filter at my ROI of interest
for tr = 1:size(data.trial,1)
       % loop over trials
     trials{tr} = W * squeeze(data.trial(tr,:,:));
   *(3)
end

Is this approach correct?
My main questions are:

*(1) Is there a way to select the best lambda parameter (e.g., selecting
the one that best approximates the activity at the EEG channels level)?

*(2) I am confused about the role of the covariance, since it doesn't seem
to be used when source activity is estimated using the set of spatial
filters at the single trial

*(3) Is the "trials{tr} = W * squeeze(data.trial(tr,:,:)); " approach
correct to get time-series of source activity in a ROI?

Looking forward for your feedback.

Best,
David


-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From juliacoopiza at gmail.com  Sun Sep  3 17:14:06 2017
From: juliacoopiza at gmail.com (Julia Coopi)
Date: Sun, 3 Sep 2017 09:14:06 -0600
Subject: [FieldTrip] Using PPC method
In-Reply-To: <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
Message-ID: <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have
any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu> wrote:

> Dear Tianyang,
>
> maybe it's a good idea to download the accompanying sample data from the
> tutorial and look if you can recreate the shown data structure. Then look
> closer into the values of the respective fields. That should give you a
> better grasp on what is required there.
>
> I have not fully looked into the code but my feeling is that
> spikeTrials.timestamp is not of any further use and is just carried from
> the data structure before (which was not cut into trials and where the raw
> timestamps were useful). The timing of spikes relative to the trial zero
> point is fully described in the fields ".time", ".trial" and ".trialtime".
> Best,
> Andreas
>
>
> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
> on behalf of 马天阳 [tianyangma2013 at gmail.com]
> *Sent:* Monday, August 28, 2017 5:31 PM
> *To:* FieldTrip discussion list
> *Subject:* Re: [FieldTrip] Using PPC method
>
> Dear Andreas,
>
> I still don't quite understand the tutorial.
>
> spikeTrials =
>          label: {'sig002a_wf'  'sig003a_wf'}
>      timestamp: {[1x83601 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
>       waveform: {[1x32x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>           unit: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>            hdr: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>         dimord: '{chan}_lead_time_spike'
>            cfg: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>           time: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>          trial: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>      trialtime: [600x2 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]
>
> Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?
>
> I feel I am quite lost.
>
> Best,
>
> Tianyang
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From k.lehongre-ihu at icm-institute.org  Mon Sep  4 11:26:52 2017
From: k.lehongre-ihu at icm-institute.org (Katia Lehongre)
Date: Mon, 4 Sep 2017 11:26:52 +0200
Subject: [FieldTrip] Workshop on intracranial recordings in human,
	October 3-4, Paris
Message-ID: <6eba6c50-169d-a0f0-ab1e-73d808e81d24@icm-institute.org>

Dear all,

The first*WIRED*(*W*orkshop on *I*ntracranial *R*ecordings in humans : 
*E*pilepsy, *D*BS), will be held in Paris, France, at the Brain and 
Spine Institute (ICM) on October 3 and 4.
Registration is opened and*free*.

Conferences, technical discussions, poster session (prize worth 800€ to 
be awarded), commercial solutions, wine and cheese…

  All information and registration to this event on our website: 
http://wired-icm.org <http://wired-icm.org/>

_Note that_ only *a few spots are left* for poster presentation and that 
we have reach *70% of full capacity* including people from 10 Parisian 
institutes, 6 major French cities and 9 countries.

We hope to see you there!
The organizers

_Organization_ : Katia Lehongre, Adrien Schramm –

_Scientific Committee_: Vincent Navarro, Katia Lehongre, Brian Lau, 
Nathalie George, Michel Le Van Quyen, Marie Laure Welter, Lionel Naccache



*_Program_*

*Tuesday October 3^rd *

*AM*

*Research Lecture Session*

**

9:00 *   – /Welcome breakfast/*

9:30*    –   Event Introduction* 
/V. Navarro,  K. Lehongre/

9:45*     –   Keynote : Epileptic ictal wavefront*

*/C. Schevon/ 
<http://columbianeurology.org/profile/caschevon>*/ (Columbia, USA)/

10:45 *– /Break/*

11:00*   –   DBS: Title to be determined*

/J. Bastin (Grenoble, France)/

11:45*   –   DBS: **Title to be determined*

12:30*    – /Lunch & Poster session/*

	

*PM*

*Methodological aspects*

**

14:00 *   –   Equipment : Focus on new electrodes* 
 /L. Valton/ 
<http://www.cerco.ups-tlse.fr/Luc-Valton>/ (Toulouse, France)/

15:00*    –   Analysis : Spike sorting techniques* 
 /F. Mormann/ 
<http://www.sfb1089.de/index.php/2014-01-16-17-35-38/principal-investigatiors/29-priv-doz-dr-dr-florian-mormann>/ (Bonn, 
Germany)/

16:00 *– /Coffe Break/*

16:30*   –   Imaging: Electrode localization*

*/S. Fernandez/ <http://www.cenir.org/>*/ (Paris, France/)

18:00*   –   Wine and Cheese session*

**

*Wednesday October 4^th *

*AM*

*Research Lecture Session*

8:45 *   – /Breakfast/*

9:00*    –   Keynote : Title to be determined*

*/P. Brown/ <https://www.ndcn.ox.ac.uk/team/peter-brown>*/ (Oxford, UK)/

10:00*   –   Memory encoding*

*/N. Axmacher/ 
<http://www.rd.ruhr-uni-bochum.de/neuro/wiss/pi/axmacher/index.html.en>*/ (Bochum, 
Germany)/

10:45 *–   Visual processing*

/L. Reddy / <http://cerco.ups-tlse.fr/%7Elreddy/Leila_Reddy/>/(Toulouse, 
France/)

11:30*   – /Coffee Break/*

11:45*   –   Focus: Imaging and electrophysiology*

/C. Ciumas-Gaumond (Lyon, France)/

12:45*    –  Conclusion & poster award*

	

*PM – Off event*

*Réunion du groupe Français Microelectrode*

*
*

*Note that *The *Blackrock Microsystems’ Clinical Electrophysiology 
Workshop* will occur on Monday 2nd. All info and registration on 
this satellite event click here 
<http://blackrockmicro.com/european-workshop>.

**

-- 
Katia Lehongre

Ingénieure de recherche IHU-A-ICM
PF STIM CENIR, bureau -1.041
ICM, UPMC/Inserm U1127/CNRS UMR7225
Institut du Cerveau et de la Moelle épinière
Hôpital Pitié-Salpêtrière
47 Boulevard de l'Hôpital
CS 21414
75646 PARIS CEDEX 13
tel: 01 57 27 47 14

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From linzhangysu at outlook.com  Mon Sep  4 13:09:45 2017
From: linzhangysu at outlook.com (linzhangysu at outlook.com)
Date: Mon, 4 Sep 2017 11:09:45 +0000
Subject: [FieldTrip] WPLI
Message-ID: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>


[cid:image002.png at 01D325AA.E537BE20]

I want to calculate the WPLI of 64 channels for one subject. But I met some questions.
Firstly, I didn’t understand the meaning of repetitions (just as the maker of the figure ). The dimension of repetitions was 1 in my MATLAB code , which resulted  in the WPLI result are NaN vectors. How can I solve the problem about ‘repetitions’?
I am looking forward to your reply very urgently, Thank you !

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From michelic72 at gmail.com  Mon Sep  4 13:19:27 2017
From: michelic72 at gmail.com (Cristiano Micheli)
Date: Mon, 4 Sep 2017 13:19:27 +0200
Subject: [FieldTrip] WPLI
In-Reply-To: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
References: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
Message-ID: <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>

Dear Linzhangysu

the wPLI metric requires you to have your experimental design matrix
organized in 'repetitions' or 'trials'.
This is typically the case (but not only) of an evoked related design,
where the repetitions dimension is used to calculate your 'average' wPLI
across trials, and this is what the FT code is doing for you in the
*ft_connectivity_wpli* function.
In summary, with this formula you will not be able to apply wPLI to a
single trial (e.g. like in resting state). If your experiment allows
organizing the experimental data into 'trials' (with the operation of
epoching) then you can solve your problem, otherwise you will have to use
other metrics of phase coupling.

IHTH
Cris Micheli



On Mon, Sep 4, 2017 at 1:09 PM, linzhangysu at outlook.com <
linzhangysu at outlook.com> wrote:

>
>
> [image: cid:image002.png at 01D325AA.E537BE20]
>
>
>
> I want to calculate the WPLI of 64 channels for one subject. But I met
> some questions.
>
> Firstly, I didn’t understand the meaning of repetitions (just as the
> maker of the figure ). The dimension of repetitions was 1 in my MATLAB code
> , which resulted  in the WPLI result are NaN vectors. How can I solve the
> problem about ‘repetitions’?
>
> I am looking forward to your reply very urgently, Thank you !
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From awutz at mit.edu  Mon Sep  4 14:24:47 2017
From: awutz at mit.edu (Andreas Wutz)
Date: Mon, 4 Sep 2017 12:24:47 +0000
Subject: [FieldTrip] Using PPC method
In-Reply-To: <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>,
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
Message-ID: <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>

Dear Julia,

I did not see your error message. Maybe, your lfp data structure is still in a continuous recording format without a trial definition?

________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Julia Coopi [juliacoopiza at gmail.com]
Sent: Sunday, September 03, 2017 11:14 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Tianyang,

maybe it's a good idea to download the accompanying sample data from the tutorial and look if you can recreate the shown data structure. Then look closer into the values of the respective fields. That should give you a better grasp on what is required there.

I have not fully looked into the code but my feeling is that spikeTrials.timestamp is not of any further use and is just carried from the data structure before (which was not cut into trials and where the raw timestamps were useful). The timing of spikes relative to the trial zero point is fully described in the fields ".time", ".trial" and ".trialtime".
Best,
Andreas


From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of 马天阳 [tianyangma2013 at gmail.com<mailto:tianyangma2013 at gmail.com>]
Sent: Monday, August 28, 2017 5:31 PM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

I still don't quite understand the tutorial.

spikeTrials =
         label: {'sig002a_wf'  'sig003a_wf'}
     timestamp: {[1x83601 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
      waveform: {[1x32x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
          unit: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
           hdr: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
        dimord: '{chan}_lead_time_spike'
           cfg: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
          time: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
         trial: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
     trialtime: [600x2 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]

Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?

I feel I am quite lost.

Best,

Tianyang

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From christine.blume at sbg.ac.at  Mon Sep  4 14:28:47 2017
From: christine.blume at sbg.ac.at (Blume Christine)
Date: Mon, 4 Sep 2017 12:28:47 +0000
Subject: [FieldTrip] Effect size measure for cluster-based
	permutation	tests
In-Reply-To: <CAGZVU0-MSDjvM3xZAAr9J5z25DD=ia+eFMAFPwnq30qGK-oeiQ@mail.gmail.com>
References: <E20FE863CD1C294AA55E415F4C446424C2B80B2D@xchmbx2.is.sbg.ac.at>
 <CAGZVU0-MSDjvM3xZAAr9J5z25DD=ia+eFMAFPwnq30qGK-oeiQ@mail.gmail.com>
Message-ID: <E20FE863CD1C294AA55E415F4C446424C2B8347B@xchmbx2.is.sbg.ac.at>

Hi Alik,

Thanks a lot for your suggestion, which I hoped would prompt more answers. Does anyone have suggestions on how exactly to implement the calculation of an effect size measure?

Best,
Christine

Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Alik Widge
Gesendet: Mittwoch, 23. August 2017 16:42
An: FieldTrip discussion list
Betreff: Re: [FieldTrip] Effect size measure for cluster-based permutation tests

My naive answer, which perhaps will provoke Eric to provide a better one: you have the actual  cluster statistic and its permutation distribution under the null hypothesis. It seems as though that distribution could be assumed Gaussian and something like Cohen's d calculated.

On Aug 23, 2017 9:35 AM, "Blume Christine" <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>> wrote:
Dear all,

I came across a question posted by someone about a year ago, which concerned effect size measures for cluster-based permutation tests. Unfortunately, the question does not seem to have been answered…

Q: I am using cluster-based permutation tests (depsamplesT, on time-frequency data) and am wondering how to best calculate an effect size from that.

Best,
Christine



_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From xiew1202 at gmail.com  Mon Sep  4 14:44:26 2017
From: xiew1202 at gmail.com (Xie Wanze)
Date: Mon, 04 Sep 2017 12:44:26 +0000
Subject: [FieldTrip] WPLI
In-Reply-To: <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>
References: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
 <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>
Message-ID: <CAA6G0Y87vgohT_W-vS-M4TUyoBdCPNBpokCVkUZy1XCt-Z5y1w@mail.gmail.com>

Dear Linzhang,
As Cris mentioned, you cannot calculate the WPLi value with one trial. The
WPLI toolbox calculates the CSD and PSD for each single trial, and then get
the "correlation" of the phase information across trials.  This apparently
could not be done with one trial.
If you have continuous data you may try to segment it into epochs.

Wanze


Cristiano Micheli <michelic72 at gmail.com>于2017年9月4日 周一上午7:35写道：

> Dear Linzhangysu
>
> the wPLI metric requires you to have your experimental design matrix
> organized in 'repetitions' or 'trials'.
> This is typically the case (but not only) of an evoked related design,
> where the repetitions dimension is used to calculate your 'average' wPLI
> across trials, and this is what the FT code is doing for you in the
> *ft_connectivity_wpli* function.
> In summary, with this formula you will not be able to apply wPLI to a
> single trial (e.g. like in resting state). If your experiment allows
> organizing the experimental data into 'trials' (with the operation of
> epoching) then you can solve your problem, otherwise you will have to use
> other metrics of phase coupling.
>
> IHTH
> Cris Micheli
>
>
>
> On Mon, Sep 4, 2017 at 1:09 PM, linzhangysu at outlook.com <
> linzhangysu at outlook.com> wrote:
>
>>
>>
>> [image: cid:image002.png at 01D325AA.E537BE20]
>>
>>
>>
>> I want to calculate the WPLI of 64 channels for one subject. But I met
>> some questions.
>>
>> Firstly, I didn’t understand the meaning of repetitions (just as the
>> maker of the figure ). The dimension of repetitions was 1 in my MATLAB code
>> , which resulted  in the WPLI result are NaN vectors. How can I solve the
>> problem about ‘repetitions’?
>>
>> I am looking forward to your reply very urgently, Thank you !
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From e.maris at donders.ru.nl  Tue Sep  5 14:12:52 2017
From: e.maris at donders.ru.nl (Maris, E.G.G. (Eric))
Date: Tue, 5 Sep 2017 12:12:52 +0000
Subject: [FieldTrip] Effect size measure for cluster-based	permutation
	tests
In-Reply-To: <mailman.12.1504605602.27164.fieldtrip@science.ru.nl>
References: <mailman.12.1504605602.27164.fieldtrip@science.ru.nl>
Message-ID: <59403DFC-9FBC-4585-928E-84787AE7E99F@donders.ru.nl>

Dear discussion list readers & contributors (especially Christine Blume),

There have been many questions (not only on the FT discussion list) about the calculation of effect size measures in the context of cluster-based permutation tests. I will continue my reply under the quotes below.


From: Blume Christine <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>>
Subject: Re: [FieldTrip] Effect size measure for cluster-based permutation tests
Date: 4 September 2017 at 14:28:47 GMT+2
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Reply-To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>


Hi Alik,

Thanks a lot for your suggestion, which I hoped would prompt more answers. Does anyone have suggestions on how exactly to implement the calculation of an effect size measure?

Best,
Christine

Von: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Alik Widge
Gesendet: Mittwoch, 23. August 2017 16:42
An: FieldTrip discussion list
Betreff: Re: [FieldTrip] Effect size measure for cluster-based permutation tests

My naive answer, which perhaps will provoke Eric to provide a better one: you have the actual  cluster statistic and its permutation distribution under the null hypothesis. It seems as though that distribution could be assumed Gaussian and something like Cohen's d calculated.

On Aug 23, 2017 9:35 AM, "Blume Christine" <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>> wrote:
Dear all,

I came across a question posted by someone about a year ago, which concerned effect size measures for cluster-based permutation tests. Unfortunately, the question does not seem to have been answered…

Q: I am using cluster-based permutation tests (depsamplesT, on time-frequency data) and am wondering how to best calculate an effect size from that.

Best,
Christine


Giving a useful answer to this question requires that one knows for what this effect size measure will be used. Typically, a standardised effect size measure is required to perform a power calculation. A power calculation is possible for a number of parametric statistical tests such as the T- and  the F-test. As input for this power calculation, Cohen’s d is required. A sensible value for Cohen’s d can sometimes be found in published studies (preferably with large sample sizes).

Cohen’s d can easily be obtained from the outcome of a cluster-based permutation test:

  1.  Calculate the non-standardised effect sizes by averaging the (sensor, frequency, time)-specific effects within the cluster of interest. Typically, the (sensor, frequency, time)-specific effects are raw differences between the subject averages for the experimental conditions that are being compared.
  2.  Calculate the standard deviation over the subjects of these non-standardised effect sizes.
  3.  Calculate Cohen’s d by dividing the grand average of the non-standardised effect sizes by the standard deviation obtained in 2.

Unfortunately, Cohen’s d calculated in this way, will be biased, and therefore cannot be used for a power calculation. This type of bias is sometimes denoted as “double dipping”.

In general, it is extremely challenging to perform a power calculations for statistical analyses that involve high-dimensional data. This does not only hold for electrophysiological, but also for fMRI data. To get idea about the difficulties that one encounters, have a look at this paper from the fMRI community: http://www.biorxiv.org/content/early/2016/04/20/049429. For the analysis of high-dimensional electrophysiological data, quite some statistical work still has to be done.

best,
Eric Maris












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From bioeng.yoosofzadeh at gmail.com  Wed Sep  6 04:22:10 2017
From: bioeng.yoosofzadeh at gmail.com (Vahab Yousofzadeh)
Date: Tue, 5 Sep 2017 22:22:10 -0400
Subject: [FieldTrip] Effect size measure for cluster-based
Message-ID: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>

Hi Christine,

Based on my understanding from the following link the effect size
(that correspond to the significant clusters) can not be derived from
p (or t)-values by
ft_sourcestatistics:
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test

Cheers,
Vahab


From christine.blume at sbg.ac.at  Wed Sep  6 09:10:25 2017
From: christine.blume at sbg.ac.at (Blume Christine)
Date: Wed, 6 Sep 2017 07:10:25 +0000
Subject: [FieldTrip] Effect size measure for cluster-based
In-Reply-To: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>
References: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>
Message-ID: <E20FE863CD1C294AA55E415F4C446424C2B83B02@xchmbx2.is.sbg.ac.at>

Dear all,

Thank you so much for all the suggestions and hints. I will look into them!

Best,
Christine


________________________________________
Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]&quot; im Auftrag von &quot;Vahab Yousofzadeh [bioeng.yoosofzadeh at gmail.com]
Gesendet: Mittwoch, 06. September 2017 04:22
An: fieldtrip at science.ru.nl
Betreff: Re: [FieldTrip] Effect size measure for cluster-based

Hi Christine,

Based on my understanding from the following link the effect size
(that correspond to the significant clusters) can not be derived from
p (or t)-values by
ft_sourcestatistics:
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test

Cheers,
Vahab
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip



From preted at mcmaster.ca  Wed Sep  6 16:48:26 2017
From: preted at mcmaster.ca (David)
Date: Wed, 6 Sep 2017 10:48:26 -0400
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>

Hello everyone, 

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I’ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I’m running into is stated below as well as my code and I’ve attached an image of what the data looks like. I’ve tried following the tutorials and searching through the mailing list and can’t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE: 
“Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);
  
Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);”



Thank you, 
David Prete
Ph.D. Candidate 
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour 
McMaster University 

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From jan.schoffelen at donders.ru.nl  Wed Sep  6 17:18:53 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Wed, 6 Sep 2017 15:18:53 +0000
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>
References: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570@donders.ru.nl>

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I’ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I’m running into is stated below as well as my code and I’ve attached an image of what the data looks like. I’ve tried following the tutorials and searching through the mailing list and can’t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
“Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);”



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From tokimoto at mejiro.ac.jp  Wed Sep  6 17:57:17 2017
From: tokimoto at mejiro.ac.jp (=?utf-8?B?5pmC5pys55yf5ZC+?=)
Date: Thu, 7 Sep 2017 00:57:17 +0900
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8@mejiro.ac.jp>

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp
******************************************

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From smoratti at psi.ucm.es  Wed Sep  6 18:25:54 2017
From: smoratti at psi.ucm.es (STEPHAN MORATTI)
Date: Wed, 6 Sep 2017 18:25:54 +0200
Subject: [FieldTrip] Learning agreementbuybr 8
In-Reply-To: <CAE20z9+OVvw9x6VbsgJzT4eQH9YNNxc09WzHMN3H522WadtHqg@mail.gmail.com>
References: <CAE20z9+OVvw9x6VbsgJzT4eQH9YNNxc09WzHMN3H522WadtHqg@mail.gmail.com>
Message-ID: <CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA@mail.gmail.com>

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es> escribió:

Buenos días Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
¿Serías tan amable de enviármelo firmado?

Gracias y perdón por las molestias,
Sara
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From preted at mcmaster.ca  Wed Sep  6 18:43:41 2017
From: preted at mcmaster.ca (David)
Date: Wed, 6 Sep 2017 12:43:41 -0400
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
References: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
Message-ID: <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>

Hi Jan-Mathijs, 

I’ve double checked and the label is written as ‘CZ’. So, it seems to be more than a typo, unfortunately. 

David 

From: fieldtrip-request at science.ru.nl
Sent: September 6, 2017 12:26 PM
To: fieldtrip at science.ru.nl
Subject: fieldtrip Digest, Vol 82, Issue 8

Send fieldtrip mailing list submissions to
	fieldtrip at science.ru.nl

To subscribe or unsubscribe via the World Wide Web, visit
	https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
or, via email, send a message with subject or body 'help' to
	fieldtrip-request at science.ru.nl

You can reach the person managing the list at
	fieldtrip-owner at science.ru.nl

When replying, please edit your Subject line so it is more specific
than "Re: Contents of fieldtrip digest..."


Today's Topics:

   1. Re: Reshape Error Using ft_freqstatistics
      (Schoffelen, J.M. (Jan Mathijs))
   2. Cluster-based permutation tests for 3 conditions (????)
   3. Re: Learning agreementbuybr 8 (STEPHAN MORATTI)


----------------------------------------------------------------------

Message: 1
Date: Wed, 6 Sep 2017 15:18:53 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl>
Content-Type: text/plain; charset="utf-8"

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I?ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I?m running into is stated below as well as my code and I?ve attached an image of what the data looks like. I?ve tried following the tutorials and searching through the mailing list and can?t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
?Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);?



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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------------------------------

Message: 2
Date: Thu, 7 Sep 2017 00:57:17 +0900
From: ???? <tokimoto at mejiro.ac.jp>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp>
Content-Type: text/plain; charset="us-ascii"

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp
******************************************

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------------------------------

Message: 3
Date: Wed, 6 Sep 2017 18:25:54 +0200
From: STEPHAN MORATTI <smoratti at psi.ucm.es>
To: SARA RODRIGUEZ FREGENAL <srodri10 at ucm.es>, FieldTrip discussion
	list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] Learning agreementbuybr 8
Message-ID:
	<CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es> escribi?:

Buenos d?as Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
?Ser?as tan amable de envi?rmelo firmado?

Gracias y perd?n por las molestias,
Sara
-------------- next part --------------
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------------------------------

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

End of fieldtrip Digest, Vol 82, Issue 8
****************************************

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From jan.schoffelen at donders.ru.nl  Thu Sep  7 07:19:09 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 7 Sep 2017 05:19:09 +0000
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>
References: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
 <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>
Message-ID: <5038FBE1-5E7B-4F6B-B152-4A500879F002@donders.ru.nl>

In that case I recommend that you try and interpret the error message in a bit more detail. From the information you provide nobody can tell what’s going on, apart from the fact that it is a low-level matlab error. I suggest to use the matlab debugger to investigate the size of ‘meanreshapeddat', and the value of ‘nchan’ ‘nfreq’ ‘nrepl’ ‘ntime’ in this specific case.

And also think about your specification of cfg.latency. Note that you only include positive latencies to be tested, but you ask for actvsblT as a test statistic, which name suggests to use a baseline (i.e. require latencies of < 0 in the data).

JM


On 6 Sep 2017, at 18:43, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hi Jan-Mathijs,

I’ve double checked and the label is written as ‘CZ’. So, it seems to be more than a typo, unfortunately.

David

From: fieldtrip-request at science.ru.nl<mailto:fieldtrip-request at science.ru.nl>
Sent: September 6, 2017 12:26 PM
To: fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>
Subject: fieldtrip Digest, Vol 82, Issue 8

Send fieldtrip mailing list submissions to
                fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>

To subscribe or unsubscribe via the World Wide Web, visit
                https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
or, via email, send a message with subject or body 'help' to
                fieldtrip-request at science.ru.nl<mailto:fieldtrip-request at science.ru.nl>

You can reach the person managing the list at
                fieldtrip-owner at science.ru.nl<mailto:fieldtrip-owner at science.ru.nl>

When replying, please edit your Subject line so it is more specific
than "Re: Contents of fieldtrip digest..."


Today's Topics:

   1. Re: Reshape Error Using ft_freqstatistics
      (Schoffelen, J.M. (Jan Mathijs))
   2. Cluster-based permutation tests for 3 conditions (????)
   3. Re: Learning agreementbuybr 8 (STEPHAN MORATTI)


----------------------------------------------------------------------

Message: 1
Date: Wed, 6 Sep 2017 15:18:53 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>>
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl<mailto:0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl>>
Content-Type: text/plain; charset="utf-8"

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca><mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I?ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I?m running into is stated below as well as my code and I?ve attached an image of what the data looks like. I?ve tried following the tutorials and searching through the mailing list and can?t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
?Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);?



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl><mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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------------------------------

Message: 2
Date: Thu, 7 Sep 2017 00:57:17 +0900
From: ???? <tokimoto at mejiro.ac.jp<mailto:tokimoto at mejiro.ac.jp>>
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp<mailto:EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp>>
Content-Type: text/plain; charset="us-ascii"

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp<mailto:tokimoto at mejiro.ac.jp>
******************************************

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------------------------------

Message: 3
Date: Wed, 6 Sep 2017 18:25:54 +0200
From: STEPHAN MORATTI <smoratti at psi.ucm.es<mailto:smoratti at psi.ucm.es>>
To: SARA RODRIGUEZ FREGENAL <srodri10 at ucm.es<mailto:srodri10 at ucm.es>>, FieldTrip discussion
                list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Learning agreementbuybr 8
Message-ID:
                <CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com<mailto:CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com>>
Content-Type: text/plain; charset="utf-8"

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es<mailto:srodri10 at ucm.es>> escribi?:

Buenos d?as Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
?Ser?as tan amable de envi?rmelo firmado?

Gracias y perd?n por las molestias,
Sara
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------------------------------

_______________________________________________
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https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

End of fieldtrip Digest, Vol 82, Issue 8
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From nugenta at mail.nih.gov  Thu Sep  7 03:02:10 2017
From: nugenta at mail.nih.gov (Nugent, Allison C. (NIH/NIMH) [E])
Date: Thu, 7 Sep 2017 01:02:10 +0000
Subject: [FieldTrip] 2nd MEG North America Meeting - Abstract Submission
	Reminder
Message-ID: <MWHPR09MB12162641EDE3ED43AEDC65DD80940@MWHPR09MB1216.namprd09.prod.outlook.com>

This is a reminder that we are currently accepting abstracts for the 2nd MEG-North America meeting, to be held in Bethesda, Maryland November 8th and 9th. Committee meetings will be held on November 8th, with the scientific session on November 9th.



There will be a poster session and oral sessions on November 9th.  We are attempting to secure funding to present several speaker honoraria for excellent abstracts submitted by early career investigators and trainees.  If you would like to be considered for this, please indicate your preference, along with your position, on your abstract submission.



Abstracts may be submitted any time up until Wednesday, September 13th at 11:59pm, directly to NIHMEGworkshop at gmail.com<mailto:NIHMEGworkshop at gmail.com> (note the deadline has been extended).



Please visit our website at https://megworkshop.nih.gov/MEGWorkshop/  - We have added additional information regarding the program!



Or register at: https://www.eventbrite.com/e/meg-north-america-2017-tickets-36315511673<https://www.eventbrite.com/e/meg-north-america-2017-tickets-36315511673?aff=utm_source%3Deb_email%26utm_medium%3Demail%26utm_campaign%3Dnew_event_email&utm_term=eventurl_text>



We hope to see you there!



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From alice.bollini at yahoo.com  Thu Sep  7 10:18:22 2017
From: alice.bollini at yahoo.com (Alice B)
Date: Thu, 7 Sep 2017 08:18:22 +0000 (UTC)
Subject: [FieldTrip] Source reconstruction issues
References: <283732377.7662318.1504772302305.ref@mail.yahoo.com>
Message-ID: <283732377.7662318.1504772302305@mail.yahoo.com>

Hello everyone,
I would like to use fieldtrip for extracting source activity from specific ROIs (using the eLoreta approach).
Here is my script, there are few things I am not sure in the pipeline (marked with numbers on the right)
% eLORETA cfg = []; cfg.method                          = 'eloreta';cfg.grid                                  = leadfield;cfg.headmodel                       = headmodel;cfg.eloreta.keepfilter              = 'yes';cfg.eloreta.normalize               = 'yes';cfg.eloreta.lambda                  = 0.05;                                      *(1)cfg.eloreta.projectnoise            = 'yes';eLO_source                          = ft_sourceanalysis(cfg,data);
% in the above line, "data" is the results of ft_timelockanalysis% with cfg.covariance = 'yes';                                                  *(2)
% then I put the source positions from the MNI template% used for the sourcemodel (http://www.fieldtriptoolbox.org/tutorial/sourcemodel#subject-specific_grids_that_are_equivalent_across_subjects_in_normalized_space)eLO_source.pos                      = template_grid.pos;iPOS                                        = eLO_source.pos;iPOS(eLO_source.inside==0,:)        = NaN; % only points inside gray matter
% Then I select ROIs (here only one for simplicity) to extract single-trial source activity:[v,I]       = min(pdist2(iPOS, ROIs_mni , 'euclidean'));
% And I multiply the spatial filter for the EEG data in each trialW            = eLO_source.avg.filter{I}; % filter at my ROI of interestfor tr = 1:size(data.trial,1)       % loop over trials         trials{tr} = W * squeeze(data.trial(tr,:,:));                            *(3)end
Is this approach correct?My main questions are:
*(1) Is there a way to select the best lambda parameter (e.g., selecting the one that best approximates the activity at the EEG channels level)?
*(2) I am confused about the role of the covariance, since it doesn't seem to be used when source activity is estimated using the set of spatial filters at the single trial
*(3) Is the "trials{tr} = W * squeeze(data.trial(tr,:,:)); " approach correct to get time-series of source activity in a ROI?

Best,Alice
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From da401 at kent.ac.uk  Thu Sep  7 13:53:20 2017
From: da401 at kent.ac.uk (D.Abdallah)
Date: Thu, 7 Sep 2017 11:53:20 +0000
Subject: [FieldTrip] Question about MVPA topographic map
Message-ID: <1504785200819.38370@kent.ac.uk>

Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.

I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING

%Reading the data

cfg = [];

cfg.dataset = filename1;

cfg.reref = 'yes';

cfg.channel = {'Cz', 'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'};

cfg.refchannel = 'Cz';

cfg.demean = 'yes';

data_eeg1 = ft_preprocessing(cfg);

%Segmenting data

cfg.trialdef.eventtype = '?';

Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;

cfg.trialdef.poststim = 0.6;

cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype = 'STATUS';

cfg.trialdef.eventvalue = [100];

stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];

FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];

VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers

stimulusTrigger = 100;

faceResponseTrigger = 1;

vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing

[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';

FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing

[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';

Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP

FaceTrials.reref = 'no';

FaceTrials.keeptrials = 'yes'; % classifiers operate on individual trials

FaceTrials.channel = {'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'}; % occipital channels only

FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP

VaseTrials.reref = 'no';

VaseTrials.keeptrials = 'yes'; % classifiers operate on individual trials

VaseTrials.channel = {'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'}; % occipital channels only

VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA

cfg.layout = 'biosemi64.lay';

cfg.method = 'crossvalidate';

cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];

cfg.nfolds = 4;

cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy' 'binomial' 'contingency'};

stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);

stat.statistic.contingency

%Plot MVPA Results

stat.mymodel = stat.model{2}.primal;

cfg.parameter = 'mymodel';

cfg.xlim = [-0.1 0.6];

cfg.comments = '';

cfg.colorbar = 'yes';

cfg.interplimits= 'electrodes';

ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.

We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).

Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,

Diane Abdallah


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From Patrick.Rollo at uth.tmc.edu  Fri Sep  8 20:38:22 2017
From: Patrick.Rollo at uth.tmc.edu (Rollo, Patrick)
Date: Fri, 8 Sep 2017 18:38:22 +0000
Subject: [FieldTrip] Job Posting on FieldTrip message board
Message-ID: <6484964699b24059ba1dd00807892d06@uth.tmc.edu>

FieldTrip Moderators,

I have a job posting that I would like to make on this message board, our lab, Tandon Lab, has posted in the past. The advert is attached here. Please let me know if you have any questions,


Thank you,


Patrick Rollo
Research Assistant
Department of Neurosurgery
UTHealth McGovern Medical School at Houston
6431 Fannin St MSB G.550G Houston TX 77030
phone: 713-500-5475


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From lxykh0700073 at outlook.com  Sun Sep 10 03:53:24 2017
From: lxykh0700073 at outlook.com (Xinyi Li)
Date: Sun, 10 Sep 2017 01:53:24 +0000
Subject: [FieldTrip] mixed design permutation tests on time-frequency data?
Message-ID: <HK2PR06MB070552220B0902408B2D56C8816B0@HK2PR06MB0705.apcprd06.prod.outlook.com>

Hi all,


I'm wondering how to do a mixed design permutation test on time-frequency data, specifically, how to specify the design matrix.

I followed the instructions in this post https://mailman.science.ru.nl/pipermail/fieldtrip/2008-March/001500.html for design matrix, but got an error 'the design matrix variables should be constant within a block'.


Any suggestions?


Thanks!

Xinyi
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From Miguel.Granjaespiritosanto at nottingham.ac.uk  Mon Sep 11 11:19:13 2017
From: Miguel.Granjaespiritosanto at nottingham.ac.uk (Miguel Granja Espirito Santo)
Date: Mon, 11 Sep 2017 09:19:13 +0000
Subject: [FieldTrip] Any update on the group-level source MNE stats?
Message-ID: <AM2PR06MB086699834D86F136DC514BFE83680@AM2PR06MB0866.eurprd06.prod.outlook.com>

Hi fieldtripers,


I was wondering if there is any update on doing group level stats after conducting an MNE source analysis. I found several threads on the mailings list which I have successfully replicated, but I was wondering if there is any official FT approved way.


At the end of the MNE page it says that this is under development, so is anyone privy to what the status of documentation is?

Just asking because of supervisor enquiry for publication of our results.


Best,

Miguel
PhD Student
School of Psychology
University of Nottingham





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From Manuel.Bange at unimedizin-mainz.de  Mon Sep 11 14:32:57 2017
From: Manuel.Bange at unimedizin-mainz.de (Bange, Manuel)
Date: Mon, 11 Sep 2017 12:32:57 +0000
Subject: [FieldTrip] Time normalisation for trials of different lenghts
Message-ID: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>

Dear fieldtrip community,

My name is Manuel Bange and I am working in the Movement Disorder and Neurostimulation Lab in Mainz, Germany. Currently I am working on a project where we recorded EEG and EMG data combined with simultaneously recorded ground reaction forces during walking on a treadmill. I intend to use complete gait cycles as trials in order to, for example, calculate the inter-trial coherence. The issue I now face is that every cycle lasts a different amount of time.
In a first step I have separated the continuous data into trials that correspond to whole gait cycles. One cycle starts with the onset of the right foot and ends one sample before the following onset of the same foot. This results in a number of trials (around 18 per subject) of different lengths.
Following this, I performed a time-frequency analysis for each individual trial by


1.       selecting a specific trial

cfg.trials      = 2;                                                               % select a a trial, here trial 2

trial2 = ft_selectdata(cfg, data)



2.       performing time-frequency analysis

cfg          = [];

cfg.output   = 'pow';

cfg.method   = 'mtmconvol';

cfg.taper    = 'hanning';

cfg.foi      = [1:1:40];

cfg.t_ftimwin    = ones(length(cfg.foi),1).*1;

cfg.toi          = trial2.time{1,1}(1,125):0.01:trial2.time{1,1}(1,end-125-1);

cfg.keeptrials = 'yes'

data_tfa1     = ft_freqanalysis(cfg, trial2);
resulting in
  data_tfa1 =
  struct with fields:
label: {257×1 cell}
dimord: 'rpt_chan_freq_time'
freq: [1×40 double]
time: [1×172 double]                                                                    % differs for each trial
powspctrm: [1×257×40×172 double]
cumtapcnt: [1×40 double]
cfg: [1×1 struct]

Now my question is:
Is there a way to normalise this data over the time-axis, so that all trials have the same length? This is important to calculate an average time-frequency representation, or in another step, to calculate the inter-trial coherence. I have thought of normalising the time axis and interpolating the corresponding power-values.

Thanks and best regards,

Manuel Bange
M.Sc. Sports Science


Johannes-Gutenberg-University Hospital
Movement Disorders and Neurostimulation
Department of Neurology
Langenbeckstr. 1
55131 Mainz, Germany
www.unimedizin-mainz.de

Email: manuel.bange at unimedizin-mainz.de

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From juliacoopiza at gmail.com  Mon Sep 11 15:24:35 2017
From: juliacoopiza at gmail.com (Julia Coopi)
Date: Mon, 11 Sep 2017 07:24:35 -0600
Subject: [FieldTrip] Using PPC method
In-Reply-To: <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>
Message-ID: <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>

Dear Andreas,

Finally, I managed to get PPC result from fliedtrip, now I have a problem:
I am using mtmfft as method I wnat to have fine frequency resolution
atleast I wan to have a point for each 1 hz. I have used
 cfg.foi       =2:1:80;

But it did't work, my output has a frequncy vector like this:[ 5 10 15 20
... 80];

do you have any suggestion for better frequency resolution.
If any body has a suggestion, that woulb be great to share it.

Thanks,
Julia

On Mon, Sep 4, 2017 at 6:24 AM, Andreas Wutz <awutz at mit.edu> wrote:

> Dear Julia,
>
> I did not see your error message. Maybe, your lfp data structure is still
> in a continuous recording format without a trial definition?
>
> ------------------------------
> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
> on behalf of Julia Coopi [juliacoopiza at gmail.com]
> *Sent:* Sunday, September 03, 2017 11:14 AM
>
> *To:* FieldTrip discussion list
> *Subject:* Re: [FieldTrip] Using PPC method
>
> Dear Andreas,
>
> Thanks for your response, I am going through your suggestion. did you have
> any problem regarding the appending spikes and lfp. I got this error:
>
> Error using ft_appendspike (line 112)
> could not find the trial information in the continuous data
>
> thanks.
> Julia
>
> On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu> wrote:
>
>> Dear Tianyang,
>>
>> maybe it's a good idea to download the accompanying sample data from the
>> tutorial and look if you can recreate the shown data structure. Then look
>> closer into the values of the respective fields. That should give you a
>> better grasp on what is required there.
>>
>> I have not fully looked into the code but my feeling is that
>> spikeTrials.timestamp is not of any further use and is just carried from
>> the data structure before (which was not cut into trials and where the raw
>> timestamps were useful). The timing of spikes relative to the trial zero
>> point is fully described in the fields ".time", ".trial" and ".trialtime".
>> Best,
>> Andreas
>>
>>
>> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
>> on behalf of 马天阳 [tianyangma2013 at gmail.com]
>> *Sent:* Monday, August 28, 2017 5:31 PM
>> *To:* FieldTrip discussion list
>> *Subject:* Re: [FieldTrip] Using PPC method
>>
>> Dear Andreas,
>>
>> I still don't quite understand the tutorial.
>>
>> spikeTrials =
>>          label: {'sig002a_wf'  'sig003a_wf'}
>>      timestamp: {[1x83601 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
>>       waveform: {[1x32x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>           unit: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>            hdr: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>>         dimord: '{chan}_lead_time_spike'
>>            cfg: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>>           time: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>          trial: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>      trialtime: [600x2 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]
>>
>> Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?
>>
>> I feel I am quite lost.
>>
>> Best,
>>
>> Tianyang
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From awutz at mit.edu  Mon Sep 11 15:57:02 2017
From: awutz at mit.edu (Andreas Wutz)
Date: Mon, 11 Sep 2017 13:57:02 +0000
Subject: [FieldTrip] Using PPC method
In-Reply-To: <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>,
 <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>
Message-ID: <E117808318BC9745BF72B56E5563172A44579AF3@OC11EXPO29.exchange.mit.edu>

Dear Julia,

your frequency resolution depends on the time window you give to the FFT (cfg.timwin). Increasing that window will increase your freq resolution.


________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Julia Coopi [juliacoopiza at gmail.com]
Sent: Monday, September 11, 2017 9:24 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Finally, I managed to get PPC result from fliedtrip, now I have a problem:  I am using mtmfft as method I wnat to have fine frequency resolution atleast I wan to have a point for each 1 hz. I have used
 cfg.foi       =2:1:80;

But it did't work, my output has a frequncy vector like this:[ 5 10 15 20 ... 80];

do you have any suggestion for better frequency resolution.
If any body has a suggestion, that woulb be great to share it.

Thanks,
Julia

On Mon, Sep 4, 2017 at 6:24 AM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Julia,

I did not see your error message. Maybe, your lfp data structure is still in a continuous recording format without a trial definition?

________________________________
From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of Julia Coopi [juliacoopiza at gmail.com<mailto:juliacoopiza at gmail.com>]
Sent: Sunday, September 03, 2017 11:14 AM

To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Tianyang,

maybe it's a good idea to download the accompanying sample data from the tutorial and look if you can recreate the shown data structure. Then look closer into the values of the respective fields. That should give you a better grasp on what is required there.

I have not fully looked into the code but my feeling is that spikeTrials.timestamp is not of any further use and is just carried from the data structure before (which was not cut into trials and where the raw timestamps were useful). The timing of spikes relative to the trial zero point is fully described in the fields ".time", ".trial" and ".trialtime".
Best,
Andreas


From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of 马天阳 [tianyangma2013 at gmail.com<mailto:tianyangma2013 at gmail.com>]
Sent: Monday, August 28, 2017 5:31 PM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

I still don't quite understand the tutorial.

spikeTrials =
         label: {'sig002a_wf'  'sig003a_wf'}
     timestamp: {[1x83601 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
      waveform: {[1x32x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
          unit: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
           hdr: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
        dimord: '{chan}_lead_time_spike'
           cfg: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
          time: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
         trial: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
     trialtime: [600x2 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]

Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?

I feel I am quite lost.

Best,

Tianyang

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From nasseroleslami at gmail.com  Mon Sep 11 19:09:52 2017
From: nasseroleslami at gmail.com (Bahman Nasseroleslami)
Date: Mon, 11 Sep 2017 18:09:52 +0100
Subject: [FieldTrip] Research Assistant in Position Neural Engineering
 Position - Trinity College Dublin, the University of Dublin, Dublin, Ireland
Message-ID: <CAGgfpUYU1EwV3zqdznbAg-LQeZmKR4iVR4P7sjs=fQELTGK+kQ@mail.gmail.com>

Dear all,

There is a research assistant position available in Academic Unit of
Neurology, Trinity College Dublin, the University of Dublin, Dublin,
Ireland.

---------------------------------------
Job ID : 032518
Post Title: Research Assistant in Neural Engineering
Post Status: 12 month contract, full-time
Research Group / Department / School Academic Unit of Neurology, School of
Medicine, Trinity College Dublin, the University of Dublin
Location: Trinity Biomedical Sciences Institute, Trinity College Dublin,
the University of Dublin, College Green, Dublin D02 R590, Ireland
Reports to: Professor Orla Hardiman/Dr Bahman Nasseroleslami
Salary: Research Assistant Level based on Irish Universities Association
(IUA) Guideline, Point 1: €21,459 per annum (or above commensurate with
experience).
Closing Date: 5pm on Wednesday 27th September 2017


Please note that Garda vetting will be sought in respect of individuals who
come under consideration for a post.

Post Summary

Applications are invited for a motivated and self-driven individual for the
position of research assistant with the Irish ALS Research Group, hosted in
the Trinity Biomedical Sciences Institute (TBSI)'s Academic Unit of
Neurology. The ideal candidate will have an undergraduate or master's
degree in engineering, bioengineering, mathematics, computational biology,
or a cognate area. Familiarity with and/or the ability to quickly acquire
skills in electrophysiological recordings and analysis (e.g. EEG/EMG),
would be highly desirable as would a knowledge of computer programming
(MATLAB).
---------------------------------------

The detailed job description file (PDF) and the application instructions
can be found online at http://jobs.tcd.ie.

It would be really appreciated if you could share this with those that may
be interested.

Sincerely
Bahman

––––––––––––
Bahman Nasseroleslami
Senior Research Fellow, IRC Postdoctoral Fellow
Academic Unit of Neurology, School of Medicine
Trinity College Dublin, the University of Dublin
Dublin 2, Ireland.

Room 5.43, Trinity Biomedical Sciences Institute
152-160 Pearse Street, Dublin D02 R590, Ireland.
nasserob at tcd.ie, nasseroleslami at gmail.com
www.tcd.ie

Trinity College Dublin, the University of Dublin is ranked 1st in Ireland
and in the top 100 world universities by the QS World University Rankings.
––––––––––––
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From michak at is.umk.pl  Mon Sep 11 23:16:54 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Mon, 11 Sep 2017 23:16:54 +0200
Subject: [FieldTrip] MRI low contrast
Message-ID: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>

Dear Fieldtrippers,

How to correct low contrast in MR image when using ft_mri_read and
ft_sourceplot to read and display MR image? (see attachment
mri00_lowctrst.png)

For comparison, same .nii opened with mricron software (
http://people.cas.sc.edu/rorden/mricron/install.html) displays with proper
contrast (see attachment mri00_hictrst.png)

Code:

ss = 'sub1';
f = ['../data/ind/', ss, '/mri/mri00.nii'];
mri00 = ft_read_mri(f)
ft_sourceplot([],mri00)


Thank you in advance !

Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics
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From a.stolk8 at gmail.com  Mon Sep 11 23:48:47 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Mon, 11 Sep 2017 14:48:47 -0700
Subject: [FieldTrip] MRI low contrast
In-Reply-To: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
References: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
Message-ID: <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>

Hi Michal,

There's a (undocumented) keyboard shortcut, shift+equal sign (numpad +) to
adjust the contrast scaling (use numpad - for the opposite direction).

Perhaps ft_sourceplot should additionally require the same cfg.lim option
that for instance ft_determine_coordsys uses. Will propose in a PR.

Best,
Arjen




On Mon, Sep 11, 2017 at 2:16 PM, Michał Komorowski <michak at is.umk.pl> wrote:

> Dear Fieldtrippers,
>
> How to correct low contrast in MR image when using ft_mri_read and
> ft_sourceplot to read and display MR image? (see attachment
> mri00_lowctrst.png)
>
> For comparison, same .nii opened with mricron software (
> http://people.cas.sc.edu/rorden/mricron/install.html) displays with
> proper contrast (see attachment mri00_hictrst.png)
>
> Code:
>
> ss = 'sub1';
> f = ['../data/ind/', ss, '/mri/mri00.nii'];
> mri00 = ft_read_mri(f)
> ft_sourceplot([],mri00)
>
>
> Thank you in advance !
>
> Michał Komorowski, MSc
> Nicolaus Copernicus University in Toruń
> Faculty of Physics, Astronomy and Informatics
> Department of Informatics
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From anne.urai at gmail.com  Tue Sep 12 09:01:03 2017
From: anne.urai at gmail.com (Anne Urai)
Date: Tue, 12 Sep 2017 09:01:03 +0200
Subject: [FieldTrip] BIDS data format survey
Message-ID: <CAFGbMjQ9T+yXuHSqW0AZ3vHQrX29pzuMS9OoEEp+hbT9Pu7Vzw@mail.gmail.com>

Dear FieldTrippers,

Recently, a number of people have been working on developing a common data
standard for MEG called BIDS (Brain Imaging Data Structure).

They are now requesting community feedback, so please have a look at the
brief survey here and help them out: https://t.co/BjAFmR7yhN

*Magnetoencephalography (MEG) studies produce enormous amounts of data that
need to be stored, organized and analyzed. However, naming conventions and
metadata are often incomplete or inexistent, which is an impediment to the
transfer of scientific data and knowledge, the reproducibility of research
results, and the curation of large data repositories with entries from
heterogenous origins. *


*Building on recent efforts from the MRI community, MEG-BIDS is a
proposition to standardize the arrangement of data structures in
MEG. Please refer to the MEG-BIDS manuscript and to the MEG-BIDS detailed
specifications for all details concerning the proposed structure:- MEG-BIDS
Manuscript:
<https://www.google.com/url?q=http://www.biorxiv.org/content/early/2017/08/08/172684&sa=D&ust=1505157728861000&usg=AFQjCNFOvjNkNYmsyA0_EBvZ0KyQ9n1mvA>http://www.biorxiv.org/content/early/2017/08/08/172684
<http://www.biorxiv.org/content/early/2017/08/08/172684>*
*- MEG-BIDS Specifications:
<https://www.google.com/url?q=http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf&sa=D&ust=1505157728861000&usg=AFQjCNHZY8Z-YO0UQu61iEnkxMFq7DQZUQ>http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf
<http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf>*

*We wish to survey the MEG community on its present needs with respect to
data management, and design the MEG-BIDS standard to best respond to these
presently unmet needs.*

*Your participation is truly appreciated.*

... <bloop://bloop_expand>

—
Anne E. Urai, MSc
PhD student | Institut für Neurophysiologie und Pathophysiologie
Universitätsklinikum Hamburg-Eppendorf | Martinistrasse 52, 20246 |
Hamburg, Germany
www.anneurai.net / @AnneEUrai
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From michak at is.umk.pl  Tue Sep 12 09:56:58 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Tue, 12 Sep 2017 09:56:58 +0200
Subject: [FieldTrip] MRI low contrast
In-Reply-To: <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>
References: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
 <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>
Message-ID: <CAEJSOqhuxSn73P1fuj9AmRXNXKR=h6T7q9zLfuK0T7abv+CLkw@mail.gmail.com>

Yaay !

+ and - works  ! :D


For determining coordsys one should type:

% clim adjusts contrast (default [0 1], the lower the brighter)
[dataout] = ft_determine_coordsys(mri00, 'clim', [0 0.25])


Thank you very much !

Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics

2017-09-11 23:48 GMT+02:00 Arjen Stolk <a.stolk8 at gmail.com>:

> Hi Michal,
>
> There's a (undocumented) keyboard shortcut, shift+equal sign (numpad +) to
> adjust the contrast scaling (use numpad - for the opposite direction).
>
> Perhaps ft_sourceplot should additionally require the same cfg.lim option
> that for instance ft_determine_coordsys uses. Will propose in a PR.
>
> Best,
> Arjen
>
>
>
>
> On Mon, Sep 11, 2017 at 2:16 PM, Michał Komorowski <michak at is.umk.pl>
> wrote:
>
>> Dear Fieldtrippers,
>>
>> How to correct low contrast in MR image when using ft_mri_read and
>> ft_sourceplot to read and display MR image? (see attachment
>> mri00_lowctrst.png)
>>
>> For comparison, same .nii opened with mricron software (
>> http://people.cas.sc.edu/rorden/mricron/install.html) displays with
>> proper contrast (see attachment mri00_hictrst.png)
>>
>> Code:
>>
>> ss = 'sub1';
>> f = ['../data/ind/', ss, '/mri/mri00.nii'];
>> mri00 = ft_read_mri(f)
>> ft_sourceplot([],mri00)
>>
>>
>> Thank you in advance !
>>
>> Michał Komorowski, MSc
>> Nicolaus Copernicus University in Toruń
>> Faculty of Physics, Astronomy and Informatics
>> Department of Informatics
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From behinger at uos.de  Tue Sep 12 20:14:22 2017
From: behinger at uos.de (Benedikt Ehinger)
Date: Tue, 12 Sep 2017 20:14:22 +0200
Subject: [FieldTrip] Time normalisation for trials of different lenghts
In-Reply-To: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>
References: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>
Message-ID: <1311033b-5017-443e-9cd9-0f4e4486f112@uos.de>

Dear Manuel,

first off, I do not know if or how you can do this in fieldtrip. But in
eeglab you can do something they call "timewarping". One calculates a
time-frequency (TF) decomposition for each trial and then
warps/interpolates the TF so that some given events align. This is very
similar (identical?) to what you describe and you might find more
information either in the papers or the eeglab implementation. The
method has been described in Gwin 2010 [1] and we also used it in on of
our own studies [2].

Whether you can do the same also for phase (=> coherence) I don't know.

I hope that helps in your analysis.
Best, Benedikt

[1] https://www.ncbi.nlm.nih.gov/pubmed/20410364
[2] https://www.ncbi.nlm.nih.gov/pubmed/24616681


Am 11.09.2017 um 14:32 schrieb Bange, Manuel:
> Dear fieldtrip community,
> 
>  
> 
> My name is Manuel Bange and I am working in the Movement Disorder and
> Neurostimulation Lab in Mainz, Germany. Currently I am working on a
> project where we recorded EEG and EMG data combined with simultaneously
> recorded ground reaction forces during walking on a treadmill. I intend
> to use complete gait cycles as trials in order to, for example,
> calculate the inter-trial coherence. The issue I now face is that every
> cycle lasts a different amount of time.
> 
> In a first step I have separated the continuous data into trials that
> correspond to whole gait cycles. One cycle starts with the onset of the
> right foot and ends one sample before the following onset of the same
> foot. This results in a number of trials (around 18 per subject) of
> different lengths.
> 
> Following this, I performed a time-frequency analysis for each
> individual trial by
> 
>  
> 
> 1.       selecting a specific trial
> 
> /cfg.trials      = 2;
>                                                               % select a
> a trial, here trial 2/
> 
> /trial2 = ft_selectdata(cfg, data)/
> 
> / /
> 
> 2.       performing time-frequency analysis
> 
> /cfg          = [];/
> 
> /cfg.output   = 'pow';/
> 
> /cfg.method   = 'mtmconvol';/
> 
> /cfg.taper    = 'hanning';/
> 
> /cfg.foi      = [1:1:40];/
> 
> /cfg.t_ftimwin    = ones(length(cfg.foi),1).*1;/
> 
> /cfg.toi          =
> trial2.time{1,1}(1,125):0.01:trial2.time{1,1}(1,end-125-1);            /
> 
> /cfg.keeptrials = 'yes'/
> 
> /data_tfa1     = ft_freqanalysis(cfg, trial2);/
> 
> /resulting in/
> 
> /  data_tfa1 = /
> 
> /  struct with fields:/
> 
> /label: {257×1 cell}/
> 
> /dimord: 'rpt_chan_freq_time'/
> 
> /freq: [1×40 double]/
> 
> /time: [1×172
> double]                                                                   
> % differs for each trial/
> 
> /powspctrm: [1×257×40×172 double]/
> 
> /cumtapcnt: [1×40 double]/
> 
> /cfg: [1×1 struct]/
> 
>  
> 
> Now my question is:
> 
> Is there a way to normalise this data over the time-axis, so that all
> trials have the same length? This is important to calculate an average
> time-frequency representation, or in another step, to calculate the
> inter-trial coherence. I have thought of normalising the time axis and
> interpolating the corresponding power-values.
> 
>  
> 
> Thanks and best regards,
> 
>  
> 
> Manuel Bange
> 
> M.Sc. Sports Science
> 
>  
> 
>  
> 
> Johannes-Gutenberg-University Hospital
> 
> Movement Disorders and Neurostimulation
> 
> Department of Neurology
> 
> Langenbeckstr. 1
> 
> 55131 Mainz, Germany
> 
> www.unimedizin-mainz.de
> 
>  
> 
> Email: manuel.bange at unimedizin-mainz.de
> 
>  
> 
> 
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 


From psc.dav at gmail.com  Tue Sep 12 20:47:15 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Tue, 12 Sep 2017 20:47:15 +0200
Subject: [FieldTrip] inverse imaging
Message-ID: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>

Dear fieldtrip experts,

I was wondering if anyone has experience with extracting single trials
estimates of source activity (using MNE or Loreta-based approaches) from
regions of interest, and what would be the best procedure…


Thanks in advance
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From julian.keil at gmail.com  Wed Sep 13 12:22:01 2017
From: julian.keil at gmail.com (Julian Keil)
Date: Wed, 13 Sep 2017 12:22:01 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
Message-ID: <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>

Hi David,

do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors <http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors>
In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.

Good luck,

Julian


> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
> 
> Dear fieldtrip experts,
> 
> I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…
> 
> 
> 
> Thanks in advance
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From psc.dav at gmail.com  Wed Sep 13 13:22:46 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Wed, 13 Sep 2017 13:22:46 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
Message-ID: <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>

Thaks Julian,
that is the approach I was using, with eLoreta.
I am not sure about two steps,though.
One is the estimate and use of the signal covariance to input for single-trial
activity in source space.
The other is the choice of the optimal lambda.

If you have some advice, that wold be very helpful.

Thanks,
David

2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com>:

> Hi David,
>
> do you want to obtain single-trial activity in source space? In that case,
> have you looked at the „virtual sensors“-tutorial? http://www.
> fieldtriptoolbox.org/tutorial/shared/virtual_sensors
> In the tutorial, LCMV is used for the source analysis, but it should also
> work with sloreta, as the output-structure of the source-analysis is
> identical. I’m not sure about MNE though.
>
> Good luck,
>
> Julian
>
>
> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
>
> Dear fieldtrip experts,
>
> I was wondering if anyone has experience with extracting single trials
> estimates of source activity (using MNE or Loreta-based approaches) from
> regions of interest, and what would be the best procedure…
>
>
> Thanks in advance
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From julian.keil at gmail.com  Wed Sep 13 13:41:22 2017
From: julian.keil at gmail.com (Julian Keil)
Date: Wed, 13 Sep 2017 13:41:22 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
 <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
Message-ID: <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>

Hi David,

regarding the lambda, I think there are different ideas floating around the fieldtrip discussion-list. I suggest searching for the term „lambda“ to get a rough idea. Personally, for our EEG-data I usually use 10%.

What is your question exactly regarding the covariance as input?

Cheers,

Julian

> Am 13.09.2017 um 13:22 schrieb David Pascucci <psc.dav at gmail.com>:
> 
> Thaks Julian,
> that is the approach I was using, with eLoreta.
> I am not sure about two steps,though.
> One is the estimate and use of the signal covariance to input for single-trial activity in source space.
> The other is the choice of the optimal lambda.
> 
> If you have some advice, that wold be very helpful.
> 
> Thanks,
> David
> 
> 2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com <mailto:julian.keil at gmail.com>>:
> Hi David,
> 
> do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors <http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors>
> In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.
> 
> Good luck,
> 
> Julian
> 
> 
>> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com <mailto:psc.dav at gmail.com>>:
>> 
>> Dear fieldtrip experts,
>> 
>> I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…
>> 
>> 
>> 
>> Thanks in advance
>> 
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip <https://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip <https://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
> 
> 
> 
> -- 
> --------------------- 
> David Pascucci
> 
> Postdoctoral Fellow
> University of Fribourg
> Department of Psychology
> Rue de Faucigny 2
> 1700 Fribourg
> Switzerland
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From evelyn.muschter at unitn.it  Wed Sep 13 13:50:06 2017
From: evelyn.muschter at unitn.it (Evelyn Muschter)
Date: Wed, 13 Sep 2017 13:50:06 +0200
Subject: [FieldTrip] Any update on the group-level source MNE stats?/
	Vol 82, Issue 13
In-Reply-To: <mailman.9.1505124001.16132.fieldtrip@science.ru.nl>
References: <mailman.9.1505124001.16132.fieldtrip@science.ru.nl>
Message-ID: <88486D75-ED4E-40E0-8EE1-95A1A21E60CB@unitn.it>

Hi Miguel and all, 

I have been wondering this myself! 
I have also followed various tutorial snippets here, but I am stuck with how to properly do group level stats. 
Any suggestions and input would be greatly appreciated! 

Best, 

Evelyn 

> On Sep 11, 2017, at 12:00 PM, fieldtrip-request at science.ru.nl wrote:
> 
> Send fieldtrip mailing list submissions to
> 	fieldtrip at science.ru.nl
> 
> To subscribe or unsubscribe via the World Wide Web, visit
> 	https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> or, via email, send a message with subject or body 'help' to
> 	fieldtrip-request at science.ru.nl
> 
> You can reach the person managing the list at
> 	fieldtrip-owner at science.ru.nl
> 
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of fieldtrip digest..."
> 
> 
> Today's Topics:
> 
>   1. Any update on the group-level source MNE stats?
>      (Miguel Granja Espirito Santo)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Mon, 11 Sep 2017 09:19:13 +0000
> From: Miguel Granja Espirito Santo
> 	<Miguel.Granjaespiritosanto at nottingham.ac.uk>
> To: "fieldtrip at science.ru.nl" <fieldtrip at science.ru.nl>
> Subject: [FieldTrip] Any update on the group-level source MNE stats?
> Message-ID:
> 	<AM2PR06MB086699834D86F136DC514BFE83680 at AM2PR06MB0866.eurprd06.prod.outlook.com>
> 	
> Content-Type: text/plain; charset="iso-8859-1"
> 
> Hi fieldtripers,
> 
> 
> I was wondering if there is any update on doing group level stats after conducting an MNE source analysis. I found several threads on the mailings list which I have successfully replicated, but I was wondering if there is any official FT approved way.
> 
> 
> At the end of the MNE page it says that this is under development, so is anyone privy to what the status of documentation is?
> 
> Just asking because of supervisor enquiry for publication of our results.
> 
> 
> Best,
> 
> Miguel
> PhD Student
> School of Psychology
> University of Nottingham
> 
> 
> 
> 
> 
> This message and any attachment are intended solely for the addressee
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> ------------------------------
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 
> End of fieldtrip Digest, Vol 82, Issue 13
> *****************************************




From psc.dav at gmail.com  Wed Sep 13 15:33:57 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Wed, 13 Sep 2017 15:33:57 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
 <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
 <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>
Message-ID: <CAK-BAC872zH8R=v4QBrfj0Draw2aygYsdqkVf+esZjDSY+s62w@mail.gmail.com>

Thanks again Julian,
About the covariance, I am not sure about its usage in the reconstruction
of single-trials activity.
According to the example, this is done by multiplying the spatial filters
with the EEG data.

Whereas the covariance (Cf) is used to compute the avg.pow and ori in
ft_eloreta (line 160-168)

% get the power
dip.pow = zeros(size(dip.pos,1),1);
dip.ori = cell(size(dip.pos,1),1);
for i=1:size(dip.pos,1)
  csd        = dip.filter{i}**Cf**dip.filter{i}';
  [u,s,vv]    = svd(real(csd));
  dip.pow(i) = s(1);
  dip.ori{i} = u(:,1);
end

It does not seem to be considered when creating and storing spatial filters
(later used for single-trials reconstruction)
(line 152-158, ft_eloreta)

% use existing filters, or compute them
if ~isfield(dip, 'filter')
  filt = mkfilt_eloreta_v2(leadfield, lambda);
  for i=1:size(dip.pos,1)
    dip.filter{i,1} = squeeze(filt(:,i,:))';
  end
end

My question is, am I getting this wrong?
and if not, should I ignore the covariance estimation in the case of
single-trials reconstructed via filters*data?


Cheers,
David


2017-09-13 13:41 GMT+02:00 Julian Keil <julian.keil at gmail.com>:

> Hi David,
>
> regarding the lambda, I think there are different ideas floating around
> the fieldtrip discussion-list. I suggest searching for the term „lambda“ to
> get a rough idea. Personally, for our EEG-data I usually use 10%.
>
> What is your question exactly regarding the covariance as input?
>
> Cheers,
>
> Julian
>
> Am 13.09.2017 um 13:22 schrieb David Pascucci <psc.dav at gmail.com>:
>
> Thaks Julian,
> that is the approach I was using, with eLoreta.
> I am not sure about two steps,though.
> One is the estimate and use of the signal covariance to input for single-trial
> activity in source space.
> The other is the choice of the optimal lambda.
>
> If you have some advice, that wold be very helpful.
>
> Thanks,
> David
>
> 2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com>:
>
>> Hi David,
>>
>> do you want to obtain single-trial activity in source space? In that
>> case, have you looked at the „virtual sensors“-tutorial? http://www.
>> fieldtriptoolbox.org/tutorial/shared/virtual_sensors
>> In the tutorial, LCMV is used for the source analysis, but it should also
>> work with sloreta, as the output-structure of the source-analysis is
>> identical. I’m not sure about MNE though.
>>
>> Good luck,
>>
>> Julian
>>
>>
>> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
>>
>> Dear fieldtrip experts,
>>
>> I was wondering if anyone has experience with extracting single trials
>> estimates of source activity (using MNE or Loreta-based approaches) from
>> regions of interest, and what would be the best procedure…
>>
>>
>> Thanks in advance
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
>
> --
> ---------------------
> David Pascucci
>
> Postdoctoral Fellow
> University of Fribourg
> Department of Psychology
> Rue de Faucigny 2
> 1700 Fribourg
> Switzerland
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From Adeen.Flinker at nyumc.org  Wed Sep 13 20:23:40 2017
From: Adeen.Flinker at nyumc.org (Flinker, Adeen)
Date: Wed, 13 Sep 2017 18:23:40 +0000
Subject: [FieldTrip] ECoG postdoc position
Message-ID: <99026305-0472-4915-871A-C55374055912@nyumc.org>

NYU School of Medicine is looking for candidates for two post-doctoral positions in Human Electrocortigoraphy (ECoG) research. Both positions will be under the supervision of Dr. Adeen Flinker, investigating speech processing and cortical network dynamics.

The research will be conducted at NYU Comprehensive Epilepsy Center working with a population of surgical patients undergoing treatment for refractory epilepsy. Research paradigms will be conducted in close collaboration with the clinical neurology team. The candidate will conduct neurophysiological research in patients implanted with intracranial electrodes (surface, depth, laminar, Utah arrays) and in intraoperative patients undergoing acute recording, stimulation or cooling.

The ideal applicant must have a Ph.D. in Neuroscience, Psychology, Biomedical Engineering or a related field. Proficiency in oral and written English is mandatory. A solid background in programming, statistics and scientific writing is required. The candidate is expected to be autonomous and to have a track-record of peer-reviewed publication. Previous experience with human electrophysiology or machine learning will be an asset.

One postdoctoral position is funded by a MURI grant investigating event segmentation and episodic memory. The candidate will have an opportunity to work closely with collaborators in Princeton (Dr. Hasson, Dr. Norman), Harvard (Dr. Gershman), UC Davis (Dr. Ranganath) and Washington University (Dr. Zacks).

Interested individuals should send an email to adeen.flinker at nyumc.org, including a cover letter describing research experience and qualifications, academic CV, and contact information of referees.


Adeen Flinker, PhD
Assistant Professor
Department of Neurology
NYU School of Medicine
145 East 32nd Street
New York, NY 10016
646-754-2228



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From C.Mazzetti at donders.ru.nl  Thu Sep 14 13:28:45 2017
From: C.Mazzetti at donders.ru.nl (Mazzetti, C. (Cecilia))
Date: Thu, 14 Sep 2017 11:28:45 +0000
Subject: [FieldTrip] ICA warning messages
Message-ID: <389DA1293690C94C93E3A53201F6C91E569EB8E6@exprd01.hosting.ru.nl>

Hi Evryone,
I was wondering why do i get this type of warnings when running ICA on my data.
this is the script I am using:


cfg.resamplefs = 300;
    cfg.detrend    = 'no';
    datads = ft_resampledata(cfg, data_clean);

    cfg=[];
    cfg.bpfilter='yes'; % bandpass , use low freqs for  alpha compponents
    cfg.bpfreq = [0.5 30];
    datatmp= ft_preprocessing(cfg, datads)


    cfg.method = 'runica';
    cfg.runica.maxsteps =30;
    comp_filt = ft_componentanalysis(cfg, datatmp);

    clear datads


    cfg           = [];
    cfg.unmixing  = comp_filt.unmixing;
    cfg.topolabel = comp_filt.topolabel;
    comp_origin = ft_componentanalysis(cfg, data_clean);

    clear comp

    cfg          = [];
      cfg.viewmode = 'component';
    cfg.layout   = 'CTF275.lay';
    ft_databrowser(cfg, comp_origin)


    cfg           = [];
    cfg.component = input('bad comps = ');
    meg_ica    = ft_rejectcomponent(cfg, comp_origin,data_clean);


later on after having selected the bad components i get these messages :

Warning: unexpected channel unit "unknown" in channel 158  (i get this for all the channels but i copy-pasted just one of them for obv reasons)
Warning: copying input chantype to montage
Warning: copying input chanunit to montage

Thanks in advance for your hints!

Cecilia

Cecilia Mazzetti - Ph.D. Candidate
Donders Centre for Cognitive Neuroimaging, room 1.170
Kapittelweg 29 | 6525 EN Nijmegen

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From nirofir2 at gmail.com  Thu Sep 14 14:10:46 2017
From: nirofir2 at gmail.com (Nir Ofir)
Date: Thu, 14 Sep 2017 15:10:46 +0300
Subject: [FieldTrip] Using ft_redefinetrial with minlength and
	begsample/endsample option
Message-ID: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>

Hi fieldtrip users,

I have a data structure containing MEG trials which are aligned to stimulus
onset. I now want to realign them to the target onset, as well as removing
trials which are too short. I thought the easiest way to do this would be
to use ft_redefinetrial in the following way:

offset = dat.trialinfo(:,5); % this column contains the duration of the
stimulus-target intervel
cfg = [];
cfg.minlength = -dat.time{1}(1)+cfgx.pretarget+0.5; % prestim defined by
dat sructure + 0.5 s ERF + cfgx.pretarget
cfg.begsample = round((offset - cfgx.pretarget)*1000);
cfg.endample = round(offset*1000);
dat = ft_redefinetrial(cfg, dat);

When I run this, I get the following error:

Index exceeds matrix dimensions.

Error in ft_redefinetrial (line 209)
    data.trial{i} = data.trial{i}(:, begsample(i):endsample(i));

So I looked into ft_redefinetrials a bit, and it seems like when minlength
is defined, the trials themselves are removed, but the begample/endsample
vector are not cut to contain only the relevant trials. For now I moved to
a 2-step solution (first removing trials, than realigning), but it seems
like this could have a relatively simple fix. Suggestions?

Thanks!
Nir Ofir
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From v.piai.research at gmail.com  Fri Sep 15 12:45:01 2017
From: v.piai.research at gmail.com (Vitoria Piai)
Date: Fri, 15 Sep 2017 12:45:01 +0200
Subject: [FieldTrip] Postdoc position: Magnetoencephalography and
 Tractography applied to Language in Neurological Populations
Message-ID: <CADAXq1RZuTfHM=9iQjWAU7HUSdM_WpnbNZ6OcVuXFEZ8_sXWTg@mail.gmail.com>

*3-year postdoctoral position on the topic of magnetoencephalography and
tractography applied to language in neurological populations*


We are looking for a postdoctoral candidate with demonstrable experience in
analysis of structural imaging and tractography to strengthen our research
group. Our group aims at integrating brain measures with high temporal
resolution, obtained using magnetoencephalography, with measures of
structural connectivity to better understand language function in healthy
and neurological populations. Ongoing projects include studying chronic
stroke and brain tumour patients.


More information on
https://www.languageininteraction.nl/jobs/postdoc-position-388.html
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From jan.schoffelen at donders.ru.nl  Fri Sep 15 15:46:58 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Fri, 15 Sep 2017 13:46:58 +0000
Subject: [FieldTrip] Fwd: Question for Fieldtrip
References: <4b2644a5.aff0.15e8581f6ac.Coremail.zhangwenjia2732@126.com>
Message-ID: <420905E5-98A4-4C9C-96C4-1C8A28325786@donders.ru.nl>



Begin forwarded message:

From: 张文嘉 <zhangwenjia2732 at 126.com<mailto:zhangwenjia2732 at 126.com>>
Subject: Question for Fieldtrip
Date: 15 September 2017 at 14:27:20 GMT+2
To: <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>>



Dear Fieldtrip expert,

I am Wenjia from NYU Shanghai. I am doing timefrequency analysis with frildtrip.
I have a question that I cannot solve. And, I am wondering whether you could help me.

Specifically, I want to import the EGI data that has been preprocessed (after segmentation but no average) into fieldtrip, and further do timefrequency analysis.
I donot know how to do this.

I have found a script like following:
    cfg = [];
    cfg.triggertype = 'Stimulus';
    cfg.prestim = 1.0; %1.0s before the onset
    cfg.poststim = 2.0;  %2.0s after the onset
    cfg.inputfile = sprintf('s02_32_tf');
    cfg.triggercode = 'S 32';
    data_32 = read_analyzer_data(cfg);
"s02_32_tf" is the name that I exported from EGI system, then included 3 files: asc, vhdr and vmrk.
However an error poped up: Undefined function 'read_analyzer_data' for input arguments of type 'struct'.
Any advice are appreciated. Thank you very much.

--
Wenjia
NYU Shanghai













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From m.chait at ucl.ac.uk  Mon Sep 18 13:01:41 2017
From: m.chait at ucl.ac.uk (Chait, Maria)
Date: Mon, 18 Sep 2017 11:01:41 +0000
Subject: [FieldTrip] Research Assistant Position at the UCL Ear Institute
Message-ID: <AM3PR01MB109105F3944F78ECA01E0AB4A8630@AM3PR01MB1091.eurprd01.prod.exchangelabs.com>

I would appreciate your help in forwarding the advert below to relevant members of your department.



We are seeking to appoint an enthusiastic and motivated Research Assistant and Laboratory Manager to provide essential support for ongoing research aimed at developing transformative treatments for deafness and hearing problems. The post is funded for 12 months (with a possibility of extension for up to 5 years). The post will involve collection and analysis of audiometry as well as behavioral (psychophysics), eye tracking and EEG data in humans. The postholder will also be expected to contribute to the induction and direction of other research staff and students.  The UCL Ear Institute, located in the heart of London,  provides state-of-the-art research facilities across a wide range of disciplines and is one of the foremost centres for hearing, speech and language-related research within Europe.



Applicants should hold a 1st class, or upper 2nd (or equivalent) BSc or MSc degree in an engineering or Neuroscience-related subject. Previous experience with neuroscience research, functional brain imaging and/or acoustics is desirable.

More information and a link to the application site are in the following link: http://www.jobs.ac.uk/job/BEH401/research-assistant-in-auditory-neuroscience



Closing Date: 15 October 2017


Maria Chait PhD
m.chait at ucl.ac.uk<mailto:m.chait at ucl.ac.uk>
Professor in Auditory Cognitive Neuroscience
Lab site: http://www.ucl.ac.uk/ear/research/chaitlab/
UCL Ear Institute
332 Gray's Inn Road
London WC1X 8EE
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From hweeling.lee at gmail.com  Mon Sep 18 13:57:51 2017
From: hweeling.lee at gmail.com (Hwee Ling Lee)
Date: Mon, 18 Sep 2017 13:57:51 +0200
Subject: [FieldTrip] Normalizing log-transformed EEG power
Message-ID: <CABG7z0Sqe5vdMU8X_Kjp7aQVRn-y-rRGxevpKzHEnpZLs00Grw@mail.gmail.com>

Dear all,


I have a question regarding how to compute z-scores for log-transformed EEG
power across all events separately for each electrode and frequency. I have
searched everywhere on how to implement this in the fieldrip environment,
however, I will be very grateful if someone can help me out on this.

Thanks!

Cheers,
Hweeling
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From lxykh0700073 at outlook.com  Tue Sep 19 03:51:59 2017
From: lxykh0700073 at outlook.com (Xinyi Li)
Date: Tue, 19 Sep 2017 01:51:59 +0000
Subject: [FieldTrip] simple main effects and permutation
Message-ID: <HK2PR06MB07057B48729B2D6F77CC0A0581600@HK2PR06MB0705.apcprd06.prod.outlook.com>

Hi all,


I have a mixed design and my hypothesis is about simple main effects. So for example, I have two groups of people, and each person experience the same 2x2 factorial design with conditions A1, A2, B1, B2. And my hypotheses are something like the simple main effect of A within group 1 and condition B1.


My questions:

1) Can I just subset the data to include only the group and condition I want (B1 & group 1) and do a t-test between condition A1 & A2 after subsetting? If I understand correctly, this approach will bias the standard error of the estimates? But I'm not sure if this matters in a permutation framework, and I also don't know if it's a common practice to do this in EEG analysis?

2) Alternatively, I can run a full mixed ANOVA model and then the simple main effect in R to get the test statistics I want. But for this approach I'm not sure how I should perform the permutation since fieldtrip doesn't support a mixed design. Should I only permute A1 & A2 within condition B1 and group 1? Or should I permute everything within both factors A & B? And what about the group labels?


Any suggestions? Thanks!


Xinyi
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From da401 at kent.ac.uk  Tue Sep 19 06:25:36 2017
From: da401 at kent.ac.uk (D.Abdallah)
Date: Tue, 19 Sep 2017 04:25:36 +0000
Subject: [FieldTrip] Question about MVPA topographic map
In-Reply-To: <1504785200819.38370@kent.ac.uk>
References: <1504785200819.38370@kent.ac.uk>
Message-ID: <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>


Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.

I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING

%Reading the data

cfg = [];

cfg.dataset = filename1;

cfg.reref ='yes';

cfg.channel = {'Cz','PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};

cfg.refchannel ='Cz';

cfg.demean ='yes';

data_eeg1 = ft_preprocessing(cfg);

%Segmenting data

cfg.trialdef.eventtype ='?';

Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;

cfg.trialdef.poststim = 0.6;

cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype ='STATUS';

cfg.trialdef.eventvalue = [100];

stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];

FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];

VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers

stimulusTrigger = 100;

faceResponseTrigger = 1;

vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing

[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';

FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing

[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';

Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP

FaceTrials.reref ='no';

FaceTrials.keeptrials ='yes';% classifiers operate on individual trials

FaceTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only

FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP

VaseTrials.reref ='no';

VaseTrials.keeptrials ='yes';% classifiers operate on individual trials

VaseTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only

VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA

cfg.layout ='biosemi64.lay';

cfg.method ='crossvalidate';

cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];

cfg.nfolds = 4;

cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy''binomial''contingency'};

stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);

stat.statistic.contingency

%Plot MVPA Results

stat.mymodel = stat.model{2}.primal;

cfg.parameter ='mymodel';

cfg.xlim = [-0.1 0.6];

cfg.comments ='';

cfg.colorbar ='yes';

cfg.interplimits='electrodes';

ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.

We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).

Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,

Diane Abdallah




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From jan.schoffelen at donders.ru.nl  Tue Sep 19 07:47:49 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 05:47:49 +0000
Subject: [FieldTrip] Question about MVPA topographic map
In-Reply-To: <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>
References: <1504785200819.38370@kent.ac.uk>
 <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>
Message-ID: <A9C685DD-18DD-4F2F-99F6-99BA89CA43A4@donders.ru.nl>

Hi Diane,

First of all, I would recommend to share figures not as a matlab-figure, but as a screenshot bitmap or so. This saves people who are reading your mail a lot of overhead if they want to look at it, because they don’t need to start a matlab session etc.

Your topographical image looks ‘different’ from the one on the wiki because the distribution of your electrodes is more around the whole ‘rim’ of the head. The colored plane that shows up within the circle is the consequence of a spatial interpolation (in 2D) of the data points represented at the locations of the electrodes. For this reason also, there’s no need to be very explicit about the meaning of the x and y axes: they represent space.

Best wishes and good luck,

Jan-Mathijs



On 19 Sep 2017, at 06:25, D.Abdallah <da401 at kent.ac.uk<mailto:da401 at kent.ac.uk>> wrote:


Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.
I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING
%Reading the data
cfg = [];
cfg.dataset = filename1;
cfg.reref ='yes';
cfg.channel = {'Cz','PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};
cfg.refchannel ='Cz';
cfg.demean ='yes';
data_eeg1 = ft_preprocessing(cfg);

%Segmenting data
cfg.trialdef.eventtype ='?';
Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;
cfg.trialdef.poststim = 0.6;
cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype ='STATUS';
cfg.trialdef.eventvalue = [100];
stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];
FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];
VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers
stimulusTrigger = 100;
faceResponseTrigger = 1;
vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing
[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);
hdr = ft_read_header(cfg.dataset);
event = ft_read_event(cfg.dataset);
FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';
FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);
Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing
[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);
hdr = ft_read_header(cfg.dataset);
event = ft_read_event(cfg.dataset);
VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';
Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);
Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP
FaceTrials.reref ='no';
FaceTrials.keeptrials ='yes';% classifiers operate on individual trials
FaceTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only
FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP
VaseTrials.reref ='no';
VaseTrials.keeptrials ='yes';% classifiers operate on individual trials
VaseTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only
VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA
cfg.layout ='biosemi64.lay';
cfg.method ='crossvalidate';
cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];
cfg.nfolds = 4;
cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy''binomial''contingency'};
stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);
stat.statistic.contingency

%Plot MVPA Results
stat.mymodel = stat.model{2}.primal;
cfg.parameter ='mymodel';
cfg.xlim = [-0.1 0.6];
cfg.comments ='';
cfg.colorbar ='yes';
cfg.interplimits='electrodes';
ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.
We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).
Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,
Diane Abdallah




<Imagesc Subject 8.fig><Subject 8 Topographical map.fig><tutorial topographic map.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From jan.schoffelen at donders.ru.nl  Tue Sep 19 08:02:53 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 06:02:53 +0000
Subject: [FieldTrip] Using ft_redefinetrial with minlength and
 begsample/endsample option
In-Reply-To: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>
References: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>
Message-ID: <6728EEE0-D0EF-4A9D-AAEA-50A61E765FC9@donders.ru.nl>

Dear Nir Ofir,

Thanks for reporting this. It seems that you have also identified a possible solution, which would be to do the ‘too short trial removal’, only after the realignment of the time axis of the trials.
I think the best way to proceed would be that you try to implement this fix in your own local version of the FieldTrip git repository, and initiate a pull request once you are sure it works well. We will then review the suggested fix, and merge it into  Fieldtrip’s code base, so that everyone can benefit from your efforts.

Best wishes,

Jan-Mathijs


> On 14 Sep 2017, at 14:10, Nir Ofir <nirofir2 at gmail.com> wrote:
> 
> Hi fieldtrip users,
> 
> I have a data structure containing MEG trials which are aligned to stimulus onset. I now want to realign them to the target onset, as well as removing trials which are too short. I thought the easiest way to do this would be to use ft_redefinetrial in the following way:
> 
> offset = dat.trialinfo(:,5); % this column contains the duration of the stimulus-target intervel
> cfg = [];
> cfg.minlength = -dat.time{1}(1)+cfgx.pretarget+0.5; % prestim defined by dat sructure + 0.5 s ERF + cfgx.pretarget
> cfg.begsample = round((offset - cfgx.pretarget)*1000);
> cfg.endample = round(offset*1000);
> dat = ft_redefinetrial(cfg, dat);
> 
> When I run this, I get the following error:
> 
> Index exceeds matrix dimensions.
> 
> Error in ft_redefinetrial (line 209)
>     data.trial{i} = data.trial{i}(:, begsample(i):endsample(i));
> 
> So I looked into ft_redefinetrials a bit, and it seems like when minlength is defined, the trials themselves are removed, but the begample/endsample vector are not cut to contain only the relevant trials. For now I moved to a 2-step solution (first removing trials, than realigning), but it seems like this could have a relatively simple fix. Suggestions?
> 
> Thanks!
> Nir Ofir
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip




From isac.sehlstedt at psy.gu.se  Tue Sep 19 08:07:41 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Tue, 19 Sep 2017 06:07:41 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
Message-ID: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,

I have conducted a EEG experiement and am currently in a wedge. The PCA-function used in matlab ( i.e. pca() )  gives me the latent and coeff values that I want to use for further analysis. Sadly, I have cannot figure out how to perform a group level analysis using the matlab function and later "unmix" the group analysis to the subject level.

The a group analysis ft_componentanalysis function is easier to "unmix" thanks to its description of how to do so. However, I have not found a way to extract the latent and coefficient variables from the variables included in the comp-structure.

My question is:
Can you extract the latent and coefficient variables from the ft_componentanalysis results?

Alternatively:
Is it possible to extract the subject level latent and coefficient variables using the matlab function pca() ?

Very best,
Isac
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From jan.schoffelen at donders.ru.nl  Tue Sep 19 08:57:52 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 06:57:52 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>
References: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>
Message-ID: <3197AF78-5B87-4F7C-A587-FFD8E8FF2071@donders.ru.nl>

Hi Isac,

My question is:
Can you extract the latent and coefficient variables from the ft_componentanalysis results?

I’d say that the ‘latent variables’ are in the comp.trial field, and the coefficients are in comp.topo


Alternatively:
Is it possible to extract the subject level latent and coefficient variables using the matlab function pca() ?

I don’t know.

Best wishes,
Jan-Mathijs



Very best,
Isac
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From jean-michel.badier at univ-amu.fr  Tue Sep 19 15:52:53 2017
From: jean-michel.badier at univ-amu.fr (Jean-Michel Badier)
Date: Tue, 19 Sep 2017 15:52:53 +0200
Subject: [FieldTrip] Open positions at INS, Marseille. France
Message-ID: <fc154009-2f7a-ab63-68de-9cb58d5d9617@univ-amu.fr>


  /Post-doctoral position in the Theoretical Neuroscience Group - INS,
  Marseille, France/

*Summary*

The Theoretical Neuroscience Group (Head: Viktor Jirsa) is seeking to 
fill a post-doctoral position in the context of the project EPINOV to 
work on statistical & dynamical modeling of seizure propagation using 
personalized brain modeling and neuroinformatics approaches on a cohort 
of hundreds of epilepsy patients. EPINOV is one of 10 large-scale 
projects selected in the 3rd round of French scientific excellence 
program «RHU» managed by the National Research Agency (ANR). The aim of 
the EPINOV project is to significantly improve presurgical 
interpretation, guide surgical strategies and translate computational 
tools into clinical routine of personalized medicine. We use individual 
MRI scans to reconstruct brain anatomy and connectivity, which are 
combined with a neural mass model and fit using the Bayesian modeling 
software Stan to individuals’ intracranial electrophysiology data 
(stereotactic EEG), validated by clinical data from other modalities, 
such as MEG, fMRI, and semiology.

*Responsibilities*

• Scale up statistical models “vertically” to handle more data and 
higher resolution anatomy, using model comparison techniques to evaluate 
the advantage of different model structures

• Scale out models “horizontally”, performing coherent, reliable 
inference across a large cohort of patients using dedicated, on-site HPC 
resources

• Develop routines to evaluate and visualize inference results, making 
them amenable to clinical interpretation

• Integrate developed code into existing code bases and pipelines

*Qualification *

• Highly motivated to work on an interdisciplinary project and 
collaborate with the various members of the consortium.

• PhD degree in computational neuroscience, mathematical or statistical 
modeling, machine learning or equivalent level of knowledge.

• Significant, demonstrable experience in data fitting (Bayesian 
modeling, Dynamical Causal Modeling (DCM), Monte Carlo, etc) will be 
highly appreciated.

• Experience with working in a Linux/HPC environment

• Programming in a numerically oriented language (R, Python, MATLAB)

• Familiarity with Git, unit testing, Docker/VMs is a plus

*The Theoretical Neuroscience group *

We are a multi-national team interested in understanding the mechanisms 
underlying the spatiotemporal organization of large-scale brain 
networks. Our work comprises mathematical and computational modeling of 
large-scale network dynamics and human brain imaging data, the 
development of neuroinformatics tools for studying large-scale brain 
networks applied to concrete functions, dysfunctions (epilepsy, 
dementia) and aging.

*Terms of salary and employment *

A 12-month renewable contract will be established. Salary will depend on 
the diploma and experience. Operating language in the laboratory is 
English. Applications including a cover letter, curriculum vitae and the 
names of two referees should be sent by September 30th 2017 to: Dr. 
Irene Yujnovsky at irene.yujnovsky at univ-amu.fr 
<mailto:irene.yujnovsky at univ-amu.fr>

More information about the INS and the Theoretical Neurosciences Group 
can be found at: http://ins.univ-amu.fr <http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>*Clinical data manager for national consortium 
on epilepsy surgery - **Aix-Marseille Université *

*Marseille, FRANCE*

**

**

A position for an experienced clinical data manager is open to create 
and maintain an epileptic patient database including registration, 
normalization and security issues and to ensure the communication with 
the key partners in the academic, clinical and industry sectors with the 
aim of generating individual Virtual Patient models using The Virtual 
Brain (TVB) platform as framework (see 
http://www.thevirtualbrain.org).//This database will be generated in the 
context of the EPINOV (/Improving EPilepsy surgery management and 
progNOsis using Virtual brain technology) /projectled by Professor 
Fabrice Bartolomei 
(http://fr.ap-hm.fr/service/neurophysiologie-clinique-hopital-timone) 
funded by the RHU programme.

*Qualification*

**

Candidates must be highly motivated to work on an interdisciplinary 
project and collaborate with the various members of the consortium. They 
should have a degree in biomedical engineering, medical informatics or 
equivalent level of knowledge. Candidates must possess a solid 
experience in management of clinical and/or research data and 
programming (C, MATLAB, Python). Experience with neuroimaging data 
(stereotactic EEG, MRI, DTI, MEG, EEG), clinical trials, 
neuroinformatics and its standard formats (for instance DICOM, XNAT, 
BIDS), machine learning and Big Data would be considered an advantage.

*The EPINOV project and consortium *

We are a national consortium composed of clinicians, researchers and 
industrial partners interested in improving epilepsy surgical prognosis 
using large–scale brain modelling based on individual epileptic patient 
data. A prospective, randomized multicenter trial will be conducted with 
subjects suffering from drug-resistant epilepsy. The clinical trial will 
systematically evaluate the added value of personalized brain modelling 
in the surgical decision making.

*Terms of salary and employment*

Salary will depend on the diploma and previous experience. Operating 
languages in the consortium are both French and English.

Applications including a cover letter, curriculum vitae and the names of 
two referees should be sent by October 31st 2017 to: *Dr. Irene 
Yujnovsky* at irene.yujnovsky at univ-amu.fr 
<mailto:irene.yujnovsky at univ-amu.fr>


-- 
Jean michel Badier /- UMR S 1106 Institut de Neurosciences des Systèmes/
Aix-Marseille Université - Laboratoire MEG - TIMONE - 27 Boulevard Jean 
Moulin - 13005 Marseille
Tél: +33(0)4 91 38 55 62 - Fax : +33(0)4 91 78 99 14
Site : http://www.univ-amu.fr - Email : jean-michel.badier at univ-amu.fr 
<mailto:jean-michel.badier at univ-amu.fr>
/Afin de respecter l'environnement, merci de n'imprimer cet email que si 
nécessaire./
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From johnnguyen.education at gmail.com  Tue Sep 19 18:41:34 2017
From: johnnguyen.education at gmail.com (John Nguyen)
Date: Tue, 19 Sep 2017 12:41:34 -0400
Subject: [FieldTrip] Source analysis and sensor space differences
Message-ID: <CACzaKkHpp+LMKBLdjcbFooe5w0tddrGVeUgR1AFSU5n8Ya5BRA@mail.gmail.com>

Hi All,

My Name is John Nguyen and I am working at the Reinhart Cognitive
Neuroscience Lab at Boston University. I have been using Fieldtrip (Version
4/10/17)​​ for several months now and have decided to tackle
SourceAnalysis. After a few weeks of struggling with it, I find myself
still far from the goal post.

A milestone I'm trying to achieve is plotting activity in the visual cortex
due to a visual stimulus onset. This activity is easily reflected in my
sensor-level plots but, in the source-level, it's projection is more than a
bit wonky [image link, Negative sensor potential ,1.09-1.23s, following
visual stimulus followed by positive sensor potential at 1.22-1.48s neither
of which are present in source space.]
[https://drive.google.com/file/d/0B2UdTHvTeS9NNWV6dGJKZ2JWbk0/
view?usp=sharing]

In my current code I am using Fieldtrip templates to minimize error on my
end as much as possible.
"elec = ft_read_sens('standard_1020.elc');
load('standard_bem.mat','vol')
load('standard_sourcemodel3d8mm.mat','sourcemodel')
mri = ft_read_mri('single_subj_T1_1mm.nii');"

My Sourceanalysis is timelocked LCMV with a relative baseline change at
times -0.2 to 0.0s. I also preformed a relative baseline change on my
sensor-level data because I was wondering if the disparity was a baseline
issue. I've reached a dead-end and would appreciate any help. I've attached
links to my script
[https://drive.google.com/file/d/0B2UdTHvTeS9NT05uWUk4T0hDUjQ/
view?usp=sharing]

and data (no rereference, no artifact reject)
[https://drive.google.com/file/d/0B2UdTHvTeS9NSl8yaW9fcXZWSjQ/
view?usp=sharing]

and sensor cap layout.
[https://drive.google.com/file/d/0B2UdTHvTeS9NSGJXZUszOHBUbGM/
view?usp=sharing]


Best regards,
John Nguyen
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From isac.sehlstedt at psy.gu.se  Wed Sep 20 09:15:56 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Wed, 20 Sep 2017 07:15:56 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
Message-ID: <D5E7D932.19177%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,


This is a follow-up question to a previous question with the same mail-topic.


I have included my code below to show what I am doing (in case I have made errors) and print screens (which are also attached) of the variables I get after the ft_componentanalysis that I get.


Sadly, I cannot see any variable named comp.trial (see Unknown.tiff, or Unknown-1.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see Unknown.tiff-2) . Why am I not getting that in ft? Is it possible to get that using ft?


Very Best,

Isac



----------------- The code -----------------


clear all; close all;


%% Load

load('averages_for_ft.mat')




%% define layout

cfg = [];

cfg.elec=PreOdd_ft{1, 1}.elec;

cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)

layout = ft_prepare_layout(cfg)




%% Make the computations


% Dummy varibles

Cond1 = [];

Cond2 = [];

theDiff = [];

theDiff_ft = {};



%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions

    curr_Cond2 = Cond2_ft{i}.avg;

    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions

    cfg = [];

    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});

    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math

    cfg  = [];

    cfg.operation = 'subtract';

    cfg.parameter = 'avg';

    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);

    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions

    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.

    Cond2 = [Cond2 curr_Cond2];

    Cond1 = [Cond1 curr_Cond1];

    theDiff = [theDiff curr_difference_avg];

end




%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};

dummy_Cond2.avg = Cond2;

dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};

dummy_Cond1.avg = Cond1;

dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};

dummy_theDiff.avg = theDiff;

dummy_theDiff.time = 1:1:size(theDiff,2);




%% Run the PCA



cfg = [];

cfg.method  = 'pca';

cfg.layout = layout;


Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);

Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);

theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);




%% Revert back to subject level


cfgCond2 = [];

cfgCond2.unmixing = Cond2_comp.unmixing;

cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];

cfgCond1.unmixing = Cond1_comp.unmixing;

cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];

cfgtheDiff.unmixing = theDiff_comp.unmixing;

cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)

    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});

    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});

    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );

end



----------------- The variables/results  -----------------




[X]



[X]


[X]
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From litvak.vladimir at gmail.com  Wed Sep 20 12:56:01 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Wed, 20 Sep 2017 11:56:01 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
Message-ID: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results
when doing TF decomposition compared to computing a spectrum for the same
time window. I'm not sure I got to the bottom of it yet but one thing I
found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
differently by increasing padding. For short padding the results are
similar but with increasing padding there are differences both in offset of
the spectrum and its overall shape. See attached images where the top one
shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as
well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper',
'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
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From r.oostenveld at donders.ru.nl  Wed Sep 20 16:26:37 2017
From: r.oostenveld at donders.ru.nl (Robert Oostenveld)
Date: Wed, 20 Sep 2017 16:26:37 +0200
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
Message-ID: <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com> wrote:
> 
> Dear Fieldtrippers,
> 
> I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero. 
> 
> Is this a bug or a feature?
> 
> Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.
> 
> Thanks,
> 
> Vladimir
> 
> 
> -------------------------------------
> 
> pad = 0.5;%1%10
> 
> 
> freqoi = 5:45;
> timwin = 0.4+0*freqoi;
> 
> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
> 
> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
> 
> figure;
> subplot(2,1,1)
> plot(freqoi, s1);
> subplot(2,1,2);
> plot(freqoi, s1-s1(1));
> %%
> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
> 
> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
> 
> subplot(2,1,1)
> hold on
> plot(freqoi, s2, 'r');
> subplot(2,1,2)
> hold on
> plot(freqoi, s2-s2(1), 'r');
> <pad0_5.png><pad1.png><pad10.png>_______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Wed Sep 20 17:03:52 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Wed, 20 Sep 2017 17:03:52 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
Message-ID: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper
segmentation for which changing individual brain/scalp thresholds works in
many cases but not all, e.g. when the scalp 'bleeds' into some noise
outside of the head. Also, changing parameters in spm8 for normalization,
such as number of iterations (directly in in spm_normalize, since FT does
not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from
outsides of the head 'bleed' onto the scalp, thereby preventing optimal
scalp-segmentation and thereby normalization. Others have an inappropriate
contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved
preprocessing procedures. However, it does not seem supported in FT yet.
Does anyone have experience with this, and can perhaps share how they
extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
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From hamedtaheri at yahoo.com  Wed Sep 20 20:00:47 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Wed, 20 Sep 2017 18:00:47 +0000 (UTC)
Subject: [FieldTrip] Splitting EEG
References: <1779089334.6211483.1505930447659.ref@mail.yahoo.com>
Message-ID: <1779089334.6211483.1505930447659@mail.yahoo.com>

Hello Fieldtrip users,
I have a continues EEG data ( 80 seconds) and I would like to analyze some part of it.For instance, I want to analyse seconds 20 to 25 ( 5 seconds).Would you please let me know how can I select my times of interest.I've written a simple code but I don't know how can I split the data. 
cfg    = [];
cfg.dataset = '........  .eeg';
data_org                = ft_preprocessing(cfg); %Original Data

% Step1:  Filtering Row Data
cfg                        = [];
cfg.bpfilter            = 'yes';
cfg.bpfreq             = [1 30];
data_Filtered        = ft_preprocessing(cfg,data_org); 
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From sarang at cfin.au.dk  Wed Sep 20 22:22:51 2017
From: sarang at cfin.au.dk (Sarang S. Dalal)
Date: Wed, 20 Sep 2017 20:22:51 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
Message-ID: <0A0286C7-606F-4B30-B8F4-6689EAAD9620@cfin.au.dk>

Hi Stephen,

We have a pipeline that can use either SPM8 or SPM12 to perform both segmentation and normalization, though perhaps in a way that’s different from the official FieldTrip tutorials. Have a look at:
https://github.com/meeg-cfin/nemolab/blob/master/basics/nemo_mriproc.m

ft_volumesegment should use whichever SPM is in your path (be careful about fieldtrip/external/spm8!), and (according to my memory) SPM12 sometimes can succeed where SPM8 doesn’t provide good segmentations.

Note that for the normalization in SPM12, our script defines “/OldNorm/T1.nii” as the template, which indeed seems to give results equivalent to SPM8. I think you could change this to SPM12’s default template if you prefer.

NB: we use MRI coordinates as the base coordinate system in our pipelines, so MEG/EEG is transformed to MRI, rather than MRI to MEG/EEG.

Cheers,
Sarang


On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at GMAIL.COM<mailto:stephen.whitmarsh at GMAIL.COM>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From cornelia.quaedflieg at uni-hamburg.de  Wed Sep 20 22:51:57 2017
From: cornelia.quaedflieg at uni-hamburg.de (Conny Quaedflieg)
Date: Wed, 20 Sep 2017 22:51:57 +0200
Subject: [FieldTrip] Splitting EEG
In-Reply-To: <1779089334.6211483.1505930447659@mail.yahoo.com>
References: <1779089334.6211483.1505930447659.ref@mail.yahoo.com>
 <1779089334.6211483.1505930447659@mail.yahoo.com>
Message-ID: <20170920205155.9C930B5309@mailhost.uni-hamburg.de>

Dear Hamed, 

You can use ft_select data with cfg.latency 
See
http://www.fieldtriptoolbox.org/reference/ft_selectdata 

best 
Conny

Van: Hamed Taheri
Verzonden: woensdag 20 september 2017 20:12
Aan: fieldtrip at science.ru.nl
Onderwerp: [FieldTrip] Splitting EEG

Hello Fieldtrip users,

I have a continues EEG data ( 80 seconds) and I would like to analyze some part of it.
For instance, I want to analyse seconds 20 to 25 ( 5 seconds).
Would you please let me know how can I select my times of interest.
I've written a simple code but I don't know how can I split the data. 

cfg    = [];
cfg.dataset = '........  .eeg';
data_org                = ft_preprocessing(cfg); %Original Data

% Step1:  Filtering Row Data
cfg                        = [];
cfg.bpfilter            = 'yes';
cfg.bpfreq             = [1 30];
data_Filtered        = ft_preprocessing(cfg,data_org); 

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From jan.schoffelen at donders.ru.nl  Thu Sep 21 09:09:06 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 07:09:06 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
Message-ID: <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>

Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).

Best,
Jan-Mathijs

On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
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From litvak.vladimir at gmail.com  Thu Sep 21 11:17:11 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 10:17:11 +0100
Subject: [FieldTrip] MEG technician post at UCL
Message-ID: <CACq1Bh+rMEWNX-LiMp6gBWeCiQB9dkyXAmsw5=Jn9STB3nHrTA@mail.gmail.com>

*Senior MEG Research Technician*



Applications are invited for a Senior Research Technician in the Wellcome
Centre for Human Neuroimaging, UCL Institute of Neurology.



The Centre houses an Electroencephalography (EEG) system, two
Magnetoencephalography (MEG) systems - a CTF 275 channel Omega System and
an Optically Pumped Magnetometer (OPM) System -  and Magnetic Resonance
Imaging (MRI) facilities - two 3T Siemens Prisma scanners and a 3T Siemens
Trio.  The successful candidate will be responsible for coordinating and
maintaining an efficient MEG and EEG service for a range of different
research projects. This role will be constantly evolving as new and
alternative technologies are incorporated into the functional imaging
department.





*Applicants are required to have:*

·         Experience of EEG/MEG or similar electrophysiological recording
methods (e.g. cardiology/audiology) within a clinical or research
environment.

·         A commitment to academic research.

·         MEG experience is not essential as training will be provided.



*Salary - UCL Grade 7:*

£34,653 to £41,864 inclusive of London Allowance.

The post is available immediately and is funded until Nov 2021 in the first
instance.



Applications through UCL's online recruitment – www.ucl.ac.uk/hr/jobs where
you can download a job description and person specification using ref:
1671016.



Informal enquiries to Elaine Williams:  elaine.williams at ucl.ac.uk . If you
have any queries regarding the application process, please contact Samantha
Robinson, HR Officer, Institute of Neurology, 23 Queen Square, London, WC1N
3BG
<https://maps.google.com/?q=23+Queen+Square,+London,+WC1N+3BG&entry=gmail&source=g>
(email: ion.hradmin at ucl.ac.uk).



*Closing date:  26th September 2017*

*Taking Action for Equality*
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From jan.schoffelen at donders.ru.nl  Thu Sep 21 11:53:05 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 09:53:05 +0000
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
Message-ID: <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>

Hi to all who’s reading along,

Perhaps the two cases will become more similar once the ‘timwin’ is increased in length for the mtmconvol case….

Best wishes,

JM

On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl<mailto:r.oostenveld at donders.ru.nl>> wrote:

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
<pad0_5.png><pad1.png><pad10.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From litvak.vladimir at gmail.com  Thu Sep 21 12:29:12 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 11:29:12 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
Message-ID: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>

Hi Jan-Mathijs,

Yes, you are right about Robert's example. But if you do:

pad = 10;

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
'hanning', 'pad', pad);

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');


You will see the problem that I'm talking about. We discussed with Robert
yesterday and this is indeed 'a feature' which has to do with the fact that
the outputs of mtmfft and mtmconvol have different units. The former is
spectral density whereas the latter is spectral power.

Here is what Robert wrote me:


the units of computations (also here) are a known and long-standing issue.
I know for a long time that the two have different scaling, but did not
think about it for a long time. I recall something like this: To compare
TFRs over frequencies, you don't want the bandwidth to affect the estimate.
Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect
those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with
conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral
resolution over which the signal(and noise) spreads is different. If you
were to compute the TFR in V^2/Hz, the same V at 20Hz would have a
different value, because the length of the wavelet affects the 1/Hz.
something related (but nevertheless different) applies to the mtmfft: if
you want to estimate broadband activity in a window of 1 second or a window
of 2 seconds, you would get different spectral resolutions. The nyquist is
the same, but the power gets distributed over more bins between 0 and
Fnyquist/2. That would cause the values to appear smaller in the 2-s case.
Hence we compute spectral density, which somehow normalizes for this. I
never found a really clear explanation, but google got me this
https://dsp.stackexchange.com/questions/33957/what-is-the-difference-between-the-psd-and-the-power-spectrum
what confuses me is that power (or variance) is already normalized, i.e.
sum of squared values divided by N. So we have energy (which increases with
length), power (which does not increase with length), and power density


So one issue is that most people don't know about this including me and
possibly you. I think a good solution would be to add an option to specify
the output units for all the methods as there might be quite subtle
considerations for choosing one over the other as Robert suggests.

Vladimir

On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi to all who’s reading along,
>
> Perhaps the two cases will become more similar once the ‘timwin’ is
> increased in length for the mtmconvol case….
>
> Best wishes,
>
> JM
>
> On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl>
> wrote:
>
> Hi Vladimir,
>
> I suggest that you first start with a simpler case, like this
>
> fsample = 1000;
> time = (1:1000)/fsample;
> dat = randn(size(time));
>
> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
> 'hanning');
>
> power1 = abs(spectrum1).^2;
> power1 = squeeze(power1);
>
> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);
>
> power2 = abs(spectrum2).^2;
> power2 = squeeze(power2);
>
> figure
> plot(freqoi1, power1);
> hold on
> plot(freqoi2, power2, 'r');
>
> Note that these are not the same (albeit similar), which I had expected…
>
> best
> Robert
>
>
>
> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com>
> wrote:
>
> Dear Fieldtrippers,
>
> I'm looking into an issue of one of SPM users who gets different results
> when doing TF decomposition compared to computing a spectrum for the same
> time window. I'm not sure I got to the bottom of it yet but one thing I
> found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
> differently by increasing padding. For short padding the results are
> similar but with increasing padding there are differences both in offset of
> the spectrum and its overall shape. See attached images where the top one
> shows original spectra and the bottom one aligns the lowermost bin to zero.
>
> Is this a bug or a feature?
>
> Below is the script that produces these plots. I could provide the data as
> well but this could probably be reproduced with any data.
>
> Thanks,
>
> Vladimir
>
>
> -------------------------------------
>
> pad = 0.5;%1%10
>
>
> freqoi = 5:45;
> timwin = 0.4+0*freqoi;
>
> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time,
> 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>
> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
>
> figure;
> subplot(2,1,1)
> plot(freqoi, s1);
> subplot(2,1,2);
> plot(freqoi, s1-s1(1));
> %%
> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
> 'hanning', 'freqoi', freqoi,...
>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>
> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
>
> subplot(2,1,1)
> hold on
> plot(freqoi, s2, 'r');
> subplot(2,1,2)
> hold on
> plot(freqoi, s2-s2(1), 'r');
> <pad0_5.png><pad1.png><pad10.png>__________________________
> _____________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From litvak.vladimir at gmail.com  Thu Sep 21 12:34:23 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 11:34:23 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
 <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
Message-ID: <CACq1BhKab0TFFr=eX1peo_w2PPBuq5tGuCzBh9muB7Dyvm=R_A@mail.gmail.com>

Another thing that I noticed is that in the mtmconvol case padding is added
to the entire trial, not to the short window over which FFT is actually
computed. This might be because you actually use a wavelet which moves
along the data (I didn't check that). Anyhow right now this doesn't make
much difference because padding doesn't affect mtmconvol in such a dramatic
way as mtmfft. However, if you do allow specifying the units as power
rather than density then the way things are now mtmconvol and mtmfft with
the same padding would not be equivalent.

Vladimir

On Thu, Sep 21, 2017 at 11:29 AM, Vladimir Litvak <litvak.vladimir at gmail.com
> wrote:

> Hi Jan-Mathijs,
>
> Yes, you are right about Robert's example. But if you do:
>
> pad = 10;
>
> fsample = 1000;
> time = (1:1000)/fsample;
> dat = randn(size(time));
>
> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
> 'hanning', 'pad', pad);
>
> power1 = abs(spectrum1).^2;
> power1 = squeeze(power1);
>
> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);
>
> power2 = abs(spectrum2).^2;
> power2 = squeeze(power2);
>
> figure
> plot(freqoi1, power1);
> hold on
> plot(freqoi2, power2, 'r');
>
>
> You will see the problem that I'm talking about. We discussed with Robert
> yesterday and this is indeed 'a feature' which has to do with the fact that
> the outputs of mtmfft and mtmconvol have different units. The former is
> spectral density whereas the latter is spectral power.
>
> Here is what Robert wrote me:
>
>
> the units of computations (also here) are a known and long-standing issue.
> I know for a long time that the two have different scaling, but did not
> think about it for a long time. I recall something like this: To compare
> TFRs over frequencies, you don't want the bandwidth to affect the estimate.
> Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect
> those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with
> conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral
> resolution over which the signal(and noise) spreads is different. If you
> were to compute the TFR in V^2/Hz, the same V at 20Hz would have a
> different value, because the length of the wavelet affects the 1/Hz.
> something related (but nevertheless different) applies to the mtmfft: if
> you want to estimate broadband activity in a window of 1 second or a window
> of 2 seconds, you would get different spectral resolutions. The nyquist is
> the same, but the power gets distributed over more bins between 0 and
> Fnyquist/2. That would cause the values to appear smaller in the 2-s case.
> Hence we compute spectral density, which somehow normalizes for this. I
> never found a really clear explanation, but google got me this
> https://dsp.stackexchange.com/questions/33957/what-is-the-
> difference-between-the-psd-and-the-power-spectrum
> what confuses me is that power (or variance) is already normalized, i.e.
> sum of squared values divided by N. So we have energy (which increases with
> length), power (which does not increase with length), and power density
>
>
> So one issue is that most people don't know about this including me and
> possibly you. I think a good solution would be to add an option to specify
> the output units for all the methods as there might be quite subtle
> considerations for choosing one over the other as Robert suggests.
>
> Vladimir
>
> On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi to all who’s reading along,
>>
>> Perhaps the two cases will become more similar once the ‘timwin’ is
>> increased in length for the mtmconvol case….
>>
>> Best wishes,
>>
>> JM
>>
>> On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl>
>> wrote:
>>
>> Hi Vladimir,
>>
>> I suggest that you first start with a simpler case, like this
>>
>> fsample = 1000;
>> time = (1:1000)/fsample;
>> dat = randn(size(time));
>>
>> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
>> 'hanning');
>>
>> power1 = abs(spectrum1).^2;
>> power1 = squeeze(power1);
>>
>> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
>> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);
>>
>> power2 = abs(spectrum2).^2;
>> power2 = squeeze(power2);
>>
>> figure
>> plot(freqoi1, power1);
>> hold on
>> plot(freqoi2, power2, 'r');
>>
>> Note that these are not the same (albeit similar), which I had expected…
>>
>> best
>> Robert
>>
>>
>>
>> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com>
>> wrote:
>>
>> Dear Fieldtrippers,
>>
>> I'm looking into an issue of one of SPM users who gets different results
>> when doing TF decomposition compared to computing a spectrum for the same
>> time window. I'm not sure I got to the bottom of it yet but one thing I
>> found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
>> differently by increasing padding. For short padding the results are
>> similar but with increasing padding there are differences both in offset of
>> the spectrum and its overall shape. See attached images where the top one
>> shows original spectra and the bottom one aligns the lowermost bin to zero.
>>
>> Is this a bug or a feature?
>>
>> Below is the script that produces these plots. I could provide the data
>> as well but this could probably be reproduced with any data.
>>
>> Thanks,
>>
>> Vladimir
>>
>>
>> -------------------------------------
>>
>> pad = 0.5;%1%10
>>
>>
>> freqoi = 5:45;
>> timwin = 0.4+0*freqoi;
>>
>> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time,
>> 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>>
>> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
>>
>> figure;
>> subplot(2,1,1)
>> plot(freqoi, s1);
>> subplot(2,1,2);
>> plot(freqoi, s1-s1(1));
>> %%
>> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
>> 'hanning', 'freqoi', freqoi,...
>>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>>
>> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
>>
>> subplot(2,1,1)
>> hold on
>> plot(freqoi, s2, 'r');
>> subplot(2,1,2)
>> hold on
>> plot(freqoi, s2-s2(1), 'r');
>> <pad0_5.png><pad1.png><pad10.png>___________________________
>> ____________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
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From jan.schoffelen at donders.ru.nl  Thu Sep 21 12:36:30 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 10:36:30 +0000
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
 <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
Message-ID: <612489C6-6EFD-4D56-A9FC-189A180CC961@donders.ru.nl>

Don’t worry Vladimir,

Robert and I have discussed these issues at length on several occasions in the past ;)
Best wishes,
JM

On 21 Sep 2017, at 12:29, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Hi Jan-Mathijs,

Yes, you are right about Robert's example. But if you do:

pad = 10;

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning', 'pad', pad);

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');


You will see the problem that I'm talking about. We discussed with Robert yesterday and this is indeed 'a feature' which has to do with the fact that the outputs of mtmfft and mtmconvol have different units. The former is spectral density whereas the latter is spectral power.

Here is what Robert wrote me:

the units of computations (also here) are a known and long-standing issue. I know for a long time that the two have different scaling, but did not think about it for a long time. I recall something like this: To compare TFRs over frequencies, you don't want the bandwidth to affect the estimate. Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral resolution over which the signal(and noise) spreads is different. If you were to compute the TFR in V^2/Hz, the same V at 20Hz would have a different value, because the length of the wavelet affects the 1/Hz.
something related (but nevertheless different) applies to the mtmfft: if you want to estimate broadband activity in a window of 1 second or a window of 2 seconds, you would get different spectral resolutions. The nyquist is the same, but the power gets distributed over more bins between 0 and Fnyquist/2. That would cause the values to appear smaller in the 2-s case. Hence we compute spectral density, which somehow normalizes for this. I never found a really clear explanation, but google got me this https://dsp.stackexchange.com/questions/33957/what-is-the-difference-between-the-psd-and-the-power-spectrum
what confuses me is that power (or variance) is already normalized, i.e. sum of squared values divided by N. So we have energy (which increases with length), power (which does not increase with length), and power density


So one issue is that most people don't know about this including me and possibly you. I think a good solution would be to add an option to specify the output units for all the methods as there might be quite subtle considerations for choosing one over the other as Robert suggests.

Vladimir

On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi to all who’s reading along,

Perhaps the two cases will become more similar once the ‘timwin’ is increased in length for the mtmconvol case….

Best wishes,

JM

On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl<mailto:r.oostenveld at donders.ru.nl>> wrote:

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
<pad0_5.png><pad1.png><pad10.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Thu Sep 21 14:36:43 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 14:36:43 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
Message-ID: <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in
/external), to use SPM12 for segmentation of my template and my subject
MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
improvement over 8 when it comes to brain-segmentation, which now does not
require individual treatments anymore. It also outputs more compartments
which gives me a little bit more to work with when dealing with scans that
have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to
spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in
ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!
Stephen



On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi Stephen,
>
> Please note that FT now has full support for SPM12, both using the
> old-style segmentation, and the new one (the latter yielding 6 tissue
> types).
>
> Best,
> Jan-Mathijs
>
> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
> wrote:
>
> Dear all,
>
> I having some problems in normalizing MRIs for my study. Some have
> improper segmentation for which changing individual brain/scalp thresholds
> works in many cases but not all, e.g. when the scalp 'bleeds' into some
> noise outside of the head. Also, changing parameters in spm8 for
> normalization, such as number of iterations (directly in in spm_normalize,
> since FT does not pass these parameters) improves the transformation.
>
> However, some scans I cannot deal with, either because they have noise
> from outsides of the head 'bleed' onto the scalp, thereby preventing
> optimal scalp-segmentation and thereby normalization. Others have an
> inappropriate contrast MRI sequence.
>
> Some fMRI researchers advised me to use SPM12, because of its improved
> preprocessing procedures. However, it does not seem supported in FT yet.
> Does anyone have experience with this, and can perhaps share how they
> extracted the transformation matrix from the resulting nifti's?
>
> Thanks,
> Stephen
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From hgould at memphis.edu  Thu Sep 21 15:56:38 2017
From: hgould at memphis.edu (Herbert J Gould (hgould))
Date: Thu, 21 Sep 2017 13:56:38 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>,
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
Message-ID: <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>

I have retired please remove me from the mail list

Herbert Jay Gould
Professor Emeritus
The University of Memphis



Sent from my Verizon Wireless 4G LTE smartphone


-------- Original message --------
From: Stephen Whitmarsh
Date:09/21/2017 7:43 AM (GMT-06:00)
To: FieldTrip discussion list
Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!
Stephen



On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).

Best,
Jan-Mathijs

On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From a.stolk8 at gmail.com  Thu Sep 21 17:00:20 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Thu, 21 Sep 2017 08:00:20 -0700
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
Message-ID: <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>

Hey Stephen,

Look for discussions regarding spm12 and also dartel on bugzilla. It's been a while but as far as I can remember ft_volumenormalize is the only function now that has not been integrated. Reason being that it wasnt straightforward to house the dartel procedure under a single function, so this is ongoing work still. You can however use spm12's coregistration function with ft_volumerealign (for rigid body transformations), which Im using quite a bit and never let me down (and is much faster than before). But that wouldnt work for normalization to template space though (use spm8).

Best

> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu> wrote:
> 
> I have retired please remove me from the mail list 
> 
> Herbert Jay Gould
> Professor Emeritus
> The University of Memphis
> 
> 
> 
> Sent from my Verizon Wireless 4G LTE smartphone
> 
> 
> -------- Original message --------
> From: Stephen Whitmarsh
> Date:09/21/2017 7:43 AM (GMT-06:00)
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization
> 
> Dear Sarang and Jan-Mathijs,
> 
> Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.
> 
> Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12. 
> 
> In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.
> 
> Best wishes and thanks again!
> Stephen
> 
> 
> 
>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl> wrote:
>> Hi Stephen,
>> 
>> Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).
>> 
>> Best,
>> Jan-Mathijs
>> 
>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com> wrote:
>>> 
>>> Dear all,
>>> 
>>> I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.
>>> 
>>> However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.
>>> 
>>> Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's? 
>>> 
>>> Thanks,
>>> Stephen
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> 
>> 
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From zhangwenjia2732 at 126.com  Thu Sep 21 17:30:13 2017
From: zhangwenjia2732 at 126.com (=?GBK?B?1cXOxLzO?=)
Date: Thu, 21 Sep 2017 23:30:13 +0800 (CST)
Subject: [FieldTrip] Reading data too slow
Message-ID: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>





Dear all,


I have some problems in reading data into fieldtrip.
Specifically, I used EGI system to record EEG data and preprocessed them with Brainvison analyzer 
Then, I exported the preprocessed data into generic data format, making 3 files: .eeg, .vhdr and vmrk.
Last, I used ft_definetrial and ft_preprocessing to read these data into FieldTrip.
However, the reading is very very slow.


I tried to make only 2 channels left and tried methods as follow:
http://www.fieldtriptoolbox.org/faq/reading_is_slow_can_i_write_my_raw_data_to_a_more_efficient_file_format
But, they all did not work.


Does anyone know what I am doing wrong? Any advice very appreciated. 
Thank you


--

Wenjia
NYU Shanghai
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From stephen.whitmarsh at gmail.com  Thu Sep 21 17:52:31 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 17:52:31 +0200
Subject: [FieldTrip] Reading data too slow
In-Reply-To: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>
References: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>
Message-ID: <CAFrxm=yXm=k-AAaNjKgz0pGABc6pnYyP8hCgPQFy0j=e_3BUyg@mail.gmail.com>

Hi Wenjia,

It's impossible to give specific advice with no extra information.

How slow?
Is the data read at all?
Any error messages?
What script are you exactly running and what is the output?
See:
http://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community

I would also check your computer resources (CPU and memory) during loading
to see if you are running into a memory/CPU problem specific for your
system.
Finally, I would start with no filters. They sometimes take a while.

Cheers,
Stephen



On 21 September 2017 at 17:30, 张文嘉 <zhangwenjia2732 at 126.com> wrote:

>
>
> Dear all,
>
> I have some problems in reading data into fieldtrip.
> Specifically, I used EGI system to record EEG data and preprocessed them
> with Brainvison analyzer
> Then, I exported the preprocessed data into generic data format, making 3
> files: .eeg, .vhdr and vmrk.
> Last, I used ft_definetrial and ft_preprocessing to read these data into
> FieldTrip.
> However, the reading is very very slow.
>
> I tried to make only 2 channels left and tried methods as follow:
> http://www.fieldtriptoolbox.org/faq/reading_is_slow_can_i_
> write_my_raw_data_to_a_more_efficient_file_format
> But, they all did not work.
>
> Does anyone know what I am doing wrong? Any advice very appreciated.
> Thank you
>
> --
> Wenjia
> NYU Shanghai
>
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From stephen.whitmarsh at gmail.com  Thu Sep 21 18:20:47 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 18:20:47 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
Message-ID: <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>

Hi Arjen,

Thanks, and good to hear you've not been let down yet. It might be the fact
that I have some bad quality MRIs to deal with. However... does this
problem (see attached) ring a bell for anyone?:

Brain segmentation is proper, and co-registration with polhemus  head-shape
as well, but inverse warp to MNI result in a tilted grid. Linear vs.
non-linear transformation gives the same result. Other subjects going
through the same procedure work fine, except two others wherein I
identified it as a problem in segmenting the scalp and therefor the first
step of the normalization. This one looks absolutely fine in every other
regard, however.

I'm stumped...

    cfg                        = [];
    cfg.spmversion          = 'spm12';
    cfg.grid.warpmni        = 'yes';
    cfg.grid.template       = template_grid;
    cfg.grid.nonlinear      = 'yes';
    cfg.mri                 = mri_realigned;
    cfg.grid.unit           = 'mm';
    subject_grid            = ft_prepare_sourcemodel(cfg);

Cheers,
Stephen


On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> Hey Stephen,
>
> Look for discussions regarding spm12 and also dartel on bugzilla. It's
> been a while but as far as I can remember ft_volumenormalize is the only
> function now that has not been integrated. Reason being that it wasnt
> straightforward to house the dartel procedure under a single function, so
> this is ongoing work still. You can however use spm12's coregistration
> function with ft_volumerealign (for rigid body transformations), which Im
> using quite a bit and never let me down (and is much faster than before).
> But that wouldnt work for normalization to template space though (use spm8).
>
> Best
>
> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu>
> wrote:
>
> I have retired please remove me from the mail list
>
> Herbert Jay Gould
> Professor Emeritus
> The University of Memphis
>
>
>
> Sent from my Verizon Wireless 4G LTE smartphone
>
>
> -------- Original message --------
> From: Stephen Whitmarsh
> Date:09/21/2017 7:43 AM (GMT-06:00)
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
> normalization
>
> Dear Sarang and Jan-Mathijs,
>
> Thanks a lot. I am now able (after updating FT, which now includes SPM12
> in /external), to use SPM12 for segmentation of my template and my subject
> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
> improvement over 8 when it comes to brain-segmentation, which now does not
> require individual treatments anymore. It also outputs more compartments
> which gives me a little bit more to work with when dealing with scans that
> have bad delineation of the scalp for normalization.
>
> Pleas note that defaults seems to differ - some FT functions default to
> spm8, others to spm12.
>
> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
> 'spm12'. In other words the cfg.spmversion is not passed along.
>
> Best wishes and thanks again!
> Stephen
>
>
>
> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi Stephen,
>>
>> Please note that FT now has full support for SPM12, both using the
>> old-style segmentation, and the new one (the latter yielding 6 tissue
>> types).
>>
>> Best,
>> Jan-Mathijs
>>
>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
>> wrote:
>>
>> Dear all,
>>
>> I having some problems in normalizing MRIs for my study. Some have
>> improper segmentation for which changing individual brain/scalp thresholds
>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>> noise outside of the head. Also, changing parameters in spm8 for
>> normalization, such as number of iterations (directly in in spm_normalize,
>> since FT does not pass these parameters) improves the transformation.
>>
>> However, some scans I cannot deal with, either because they have noise
>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>> optimal scalp-segmentation and thereby normalization. Others have an
>> inappropriate contrast MRI sequence.
>>
>> Some fMRI researchers advised me to use SPM12, because of its improved
>> preprocessing procedures. However, it does not seem supported in FT yet.
>> Does anyone have experience with this, and can perhaps share how they
>> extracted the transformation matrix from the resulting nifti's?
>>
>> Thanks,
>> Stephen
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From a.stolk8 at gmail.com  Thu Sep 21 18:45:46 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Thu, 21 Sep 2017 09:45:46 -0700
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
Message-ID: <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>

First thought is a registration of brain outline to skull (instead of
brain), although at closer inspection the shift seems overall just a bit
too large for that. You could try calculating the normalization parameters
on skullstripped volumes (unless you want to keep non-brain tissue).

On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
stephen.whitmarsh at gmail.com> wrote:

> Hi Arjen,
>
> Thanks, and good to hear you've not been let down yet. It might be the
> fact that I have some bad quality MRIs to deal with. However... does this
> problem (see attached) ring a bell for anyone?:
>
> Brain segmentation is proper, and co-registration with polhemus
>  head-shape as well, but inverse warp to MNI result in a tilted grid.
> Linear vs. non-linear transformation gives the same result. Other subjects
> going through the same procedure work fine, except two others wherein I
> identified it as a problem in segmenting the scalp and therefor the first
> step of the normalization. This one looks absolutely fine in every other
> regard, however.
>
> I'm stumped...
>
>     cfg                        = [];
>     cfg.spmversion          = 'spm12';
>     cfg.grid.warpmni        = 'yes';
>     cfg.grid.template       = template_grid;
>     cfg.grid.nonlinear      = 'yes';
>     cfg.mri                 = mri_realigned;
>     cfg.grid.unit           = 'mm';
>     subject_grid            = ft_prepare_sourcemodel(cfg);
>
> Cheers,
> Stephen
>
>
> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>
>> Hey Stephen,
>>
>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>> been a while but as far as I can remember ft_volumenormalize is the only
>> function now that has not been integrated. Reason being that it wasnt
>> straightforward to house the dartel procedure under a single function, so
>> this is ongoing work still. You can however use spm12's coregistration
>> function with ft_volumerealign (for rigid body transformations), which Im
>> using quite a bit and never let me down (and is much faster than before).
>> But that wouldnt work for normalization to template space though (use spm8).
>>
>> Best
>>
>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu>
>> wrote:
>>
>> I have retired please remove me from the mail list
>>
>> Herbert Jay Gould
>> Professor Emeritus
>> The University of Memphis
>>
>>
>>
>> Sent from my Verizon Wireless 4G LTE smartphone
>>
>>
>> -------- Original message --------
>> From: Stephen Whitmarsh
>> Date:09/21/2017 7:43 AM (GMT-06:00)
>> To: FieldTrip discussion list
>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>> normalization
>>
>> Dear Sarang and Jan-Mathijs,
>>
>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>> in /external), to use SPM12 for segmentation of my template and my subject
>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>> improvement over 8 when it comes to brain-segmentation, which now does not
>> require individual treatments anymore. It also outputs more compartments
>> which gives me a little bit more to work with when dealing with scans that
>> have bad delineation of the scalp for normalization.
>>
>> Pleas note that defaults seems to differ - some FT functions default to
>> spm8, others to spm12.
>>
>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>
>> Best wishes and thanks again!
>> Stephen
>>
>>
>>
>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>> jan.schoffelen at donders.ru.nl> wrote:
>>
>>> Hi Stephen,
>>>
>>> Please note that FT now has full support for SPM12, both using the
>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>> types).
>>>
>>> Best,
>>> Jan-Mathijs
>>>
>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
>>> wrote:
>>>
>>> Dear all,
>>>
>>> I having some problems in normalizing MRIs for my study. Some have
>>> improper segmentation for which changing individual brain/scalp thresholds
>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>> noise outside of the head. Also, changing parameters in spm8 for
>>> normalization, such as number of iterations (directly in in spm_normalize,
>>> since FT does not pass these parameters) improves the transformation.
>>>
>>> However, some scans I cannot deal with, either because they have noise
>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>> optimal scalp-segmentation and thereby normalization. Others have an
>>> inappropriate contrast MRI sequence.
>>>
>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>> Does anyone have experience with this, and can perhaps share how they
>>> extracted the transformation matrix from the resulting nifti's?
>>>
>>> Thanks,
>>> Stephen
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From sarang at cfin.au.dk  Thu Sep 21 19:30:25 2017
From: sarang at cfin.au.dk (Sarang S. Dalal)
Date: Thu, 21 Sep 2017 17:30:25 +0000
Subject: [FieldTrip] Using fixed orientations for men-source estimation
In-Reply-To: <71D8A67A81D69A4CB5BE2B979021C26ECAFFE748@esen3.imed.uni-magdeburg.de>
References: <71D8A67A81D69A4CB5BE2B979021C26ECAFFE748@esen3.imed.uni-magdeburg.de>
Message-ID: <1506015025.9072.22.camel@cfin.au.dk>

Hi Christian,

I had flagged your email to follow-up later but lost track of it -- it seems you didn't get a response yet, but I hope mine is still useful!

The strategy that the 'fixedori' implements for the beamformer variants (and sLORETA) are not based on the anatomical normal, but rather the direction that maximizes the theoretical SNR (adaptively determined from the signal characteristics). This optimal direction is dependent on the particular weight calculation formula for each source localization variant.

Therefore, a similar SNR optimization strategy for minimum norm would actually require a different formula than you see used for the others. It's simple enough to implement if you know what that formula is. :-) It is likely to be contained in the book by Sekihara & Nagarajan (2008), if you'd like to have a go at it yourself.

That said, min-norm is often (or perhaps usually) performed with the solution space constrained to gray matter voxels, and the orientations defined to be normal to the cortical surface. If you independently have a way to obtain anatomically derived orientations, then you can manually provide them in lf.ori. (Or maybe there is a FieldTrip function that could obtain these normals from the MRI segmentation procedure?)

Cheers,
Sarang



On Tue, 2017-08-01 at 11:11 +0000, christian.merkel at med.ovgu.de wrote:

Hello,


I am running ft_sourceanalysis and am wondering why I can restrict the parameter-estimation in LCMV and sLORETA by setting the parameter 'fixedori' but not when using MNE. Shouldn't one be able to also just use the normal direction of each source position here as well? Can I just apply the same logic in the script 'minimumnormestimate' to change the field 'lf.ori' as, for example, in 'ft_sloreta' or would this be problematic down the line?

Thank You,

Christian

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Fri Sep 22 10:09:11 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 10:09:11 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
 <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
Message-ID: <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>

Hi Arjen,

Indeed, I do not think there is a problem with segmentation of brain/skull,
as can be seen on the image. Stripping some skin of two subjects
(thresholding the 'soft_tissue' probability output of SPM12, then removing
it) with a similar problem of solved the rotation, but resulted in too
small grids... On this subject I attached this procedure has no effect.

However, at least for those other subjects normalization on skullstripped,
or rather, scalpstripped MRIs might do the trick. As I understand it,
however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
with (affine) transformation based on the scalp first, after which it
optimizes it based on brain segmentation. I would not know how to therefor
do normalization without scalp. In fact, it expects a full volumetric
image, not a (pre-)segmented one.

Cheers,
Stephen

On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> First thought is a registration of brain outline to skull (instead of
> brain), although at closer inspection the shift seems overall just a bit
> too large for that. You could try calculating the normalization parameters
> on skullstripped volumes (unless you want to keep non-brain tissue).
>
> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com> wrote:
>
>> Hi Arjen,
>>
>> Thanks, and good to hear you've not been let down yet. It might be the
>> fact that I have some bad quality MRIs to deal with. However... does this
>> problem (see attached) ring a bell for anyone?:
>>
>> Brain segmentation is proper, and co-registration with polhemus
>>  head-shape as well, but inverse warp to MNI result in a tilted grid.
>> Linear vs. non-linear transformation gives the same result. Other subjects
>> going through the same procedure work fine, except two others wherein I
>> identified it as a problem in segmenting the scalp and therefor the first
>> step of the normalization. This one looks absolutely fine in every other
>> regard, however.
>>
>> I'm stumped...
>>
>>     cfg                        = [];
>>     cfg.spmversion          = 'spm12';
>>     cfg.grid.warpmni        = 'yes';
>>     cfg.grid.template       = template_grid;
>>     cfg.grid.nonlinear      = 'yes';
>>     cfg.mri                 = mri_realigned;
>>     cfg.grid.unit           = 'mm';
>>     subject_grid            = ft_prepare_sourcemodel(cfg);
>>
>> Cheers,
>> Stephen
>>
>>
>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>
>>> Hey Stephen,
>>>
>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>> been a while but as far as I can remember ft_volumenormalize is the only
>>> function now that has not been integrated. Reason being that it wasnt
>>> straightforward to house the dartel procedure under a single function, so
>>> this is ongoing work still. You can however use spm12's coregistration
>>> function with ft_volumerealign (for rigid body transformations), which Im
>>> using quite a bit and never let me down (and is much faster than before).
>>> But that wouldnt work for normalization to template space though (use spm8).
>>>
>>> Best
>>>
>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>> hgould at memphis.edu> wrote:
>>>
>>> I have retired please remove me from the mail list
>>>
>>> Herbert Jay Gould
>>> Professor Emeritus
>>> The University of Memphis
>>>
>>>
>>>
>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>
>>>
>>> -------- Original message --------
>>> From: Stephen Whitmarsh
>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>> To: FieldTrip discussion list
>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>> normalization
>>>
>>> Dear Sarang and Jan-Mathijs,
>>>
>>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>>> in /external), to use SPM12 for segmentation of my template and my subject
>>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>> require individual treatments anymore. It also outputs more compartments
>>> which gives me a little bit more to work with when dealing with scans that
>>> have bad delineation of the scalp for normalization.
>>>
>>> Pleas note that defaults seems to differ - some FT functions default to
>>> spm8, others to spm12.
>>>
>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>
>>> Best wishes and thanks again!
>>> Stephen
>>>
>>>
>>>
>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>> jan.schoffelen at donders.ru.nl> wrote:
>>>
>>>> Hi Stephen,
>>>>
>>>> Please note that FT now has full support for SPM12, both using the
>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>> types).
>>>>
>>>> Best,
>>>> Jan-Mathijs
>>>>
>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>
>>>> Dear all,
>>>>
>>>> I having some problems in normalizing MRIs for my study. Some have
>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>> since FT does not pass these parameters) improves the transformation.
>>>>
>>>> However, some scans I cannot deal with, either because they have noise
>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>> inappropriate contrast MRI sequence.
>>>>
>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>> Does anyone have experience with this, and can perhaps share how they
>>>> extracted the transformation matrix from the resulting nifti's?
>>>>
>>>> Thanks,
>>>> Stephen
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From stephen.whitmarsh at gmail.com  Fri Sep 22 13:01:25 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 13:01:25 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
 <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
 <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>
Message-ID: <CAFrxm=yCDxWGxk6tusWb8aN09i8yujxpq8BgkVUcE9n0peSGJQ@mail.gmail.com>

Hi Arjen, Jan-Mathijs, et. al.,

It seems the rotation was caused by a bug my side. The segmentation using
SPM12 solved problems caused by low quality MRIS.
Thanks!
Stephen


On 22 September 2017 at 10:09, Stephen Whitmarsh <
stephen.whitmarsh at gmail.com> wrote:

> Hi Arjen,
>
> Indeed, I do not think there is a problem with segmentation of
> brain/skull, as can be seen on the image. Stripping some skin of two
> subjects (thresholding the 'soft_tissue' probability output of SPM12, then
> removing it) with a similar problem of solved the rotation, but resulted in
> too small grids... On this subject I attached this procedure has no effect.
>
> However, at least for those other subjects normalization on skullstripped,
> or rather, scalpstripped MRIs might do the trick. As I understand it,
> however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
> with (affine) transformation based on the scalp first, after which it
> optimizes it based on brain segmentation. I would not know how to therefor
> do normalization without scalp. In fact, it expects a full volumetric
> image, not a (pre-)segmented one.
>
> Cheers,
> Stephen
>
> On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>
>> First thought is a registration of brain outline to skull (instead of
>> brain), although at closer inspection the shift seems overall just a bit
>> too large for that. You could try calculating the normalization parameters
>> on skullstripped volumes (unless you want to keep non-brain tissue).
>>
>> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
>> stephen.whitmarsh at gmail.com> wrote:
>>
>>> Hi Arjen,
>>>
>>> Thanks, and good to hear you've not been let down yet. It might be the
>>> fact that I have some bad quality MRIs to deal with. However... does this
>>> problem (see attached) ring a bell for anyone?:
>>>
>>> Brain segmentation is proper, and co-registration with polhemus
>>>  head-shape as well, but inverse warp to MNI result in a tilted grid.
>>> Linear vs. non-linear transformation gives the same result. Other subjects
>>> going through the same procedure work fine, except two others wherein I
>>> identified it as a problem in segmenting the scalp and therefor the first
>>> step of the normalization. This one looks absolutely fine in every other
>>> regard, however.
>>>
>>> I'm stumped...
>>>
>>>     cfg                        = [];
>>>     cfg.spmversion          = 'spm12';
>>>     cfg.grid.warpmni        = 'yes';
>>>     cfg.grid.template       = template_grid;
>>>     cfg.grid.nonlinear      = 'yes';
>>>     cfg.mri                 = mri_realigned;
>>>     cfg.grid.unit           = 'mm';
>>>     subject_grid            = ft_prepare_sourcemodel(cfg);
>>>
>>> Cheers,
>>> Stephen
>>>
>>>
>>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>>
>>>> Hey Stephen,
>>>>
>>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>>> been a while but as far as I can remember ft_volumenormalize is the only
>>>> function now that has not been integrated. Reason being that it wasnt
>>>> straightforward to house the dartel procedure under a single function, so
>>>> this is ongoing work still. You can however use spm12's coregistration
>>>> function with ft_volumerealign (for rigid body transformations), which Im
>>>> using quite a bit and never let me down (and is much faster than before).
>>>> But that wouldnt work for normalization to template space though (use spm8).
>>>>
>>>> Best
>>>>
>>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>>> hgould at memphis.edu> wrote:
>>>>
>>>> I have retired please remove me from the mail list
>>>>
>>>> Herbert Jay Gould
>>>> Professor Emeritus
>>>> The University of Memphis
>>>>
>>>>
>>>>
>>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>>
>>>>
>>>> -------- Original message --------
>>>> From: Stephen Whitmarsh
>>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>>> To: FieldTrip discussion list
>>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>>> normalization
>>>>
>>>> Dear Sarang and Jan-Mathijs,
>>>>
>>>> Thanks a lot. I am now able (after updating FT, which now includes
>>>> SPM12 in /external), to use SPM12 for segmentation of my template and my
>>>> subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>>> require individual treatments anymore. It also outputs more compartments
>>>> which gives me a little bit more to work with when dealing with scans that
>>>> have bad delineation of the scalp for normalization.
>>>>
>>>> Pleas note that defaults seems to differ - some FT functions default to
>>>> spm8, others to spm12.
>>>>
>>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>>
>>>> Best wishes and thanks again!
>>>> Stephen
>>>>
>>>>
>>>>
>>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>>> jan.schoffelen at donders.ru.nl> wrote:
>>>>
>>>>> Hi Stephen,
>>>>>
>>>>> Please note that FT now has full support for SPM12, both using the
>>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>>> types).
>>>>>
>>>>> Best,
>>>>> Jan-Mathijs
>>>>>
>>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>>
>>>>> Dear all,
>>>>>
>>>>> I having some problems in normalizing MRIs for my study. Some have
>>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>>> since FT does not pass these parameters) improves the transformation.
>>>>>
>>>>> However, some scans I cannot deal with, either because they have noise
>>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>>> inappropriate contrast MRI sequence.
>>>>>
>>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>>> Does anyone have experience with this, and can perhaps share how they
>>>>> extracted the transformation matrix from the resulting nifti's?
>>>>>
>>>>> Thanks,
>>>>> Stephen
>>>>> _______________________________________________
>>>>> fieldtrip mailing list
>>>>> fieldtrip at donders.ru.nl
>>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>>
>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> fieldtrip mailing list
>>>>> fieldtrip at donders.ru.nl
>>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
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From hamedtaheri at yahoo.com  Fri Sep 22 13:53:44 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Fri, 22 Sep 2017 11:53:44 +0000 (UTC)
Subject: [FieldTrip] Artifact Rejection Problem
References: <258456736.7597235.1506081224221.ref@mail.yahoo.com>
Message-ID: <258456736.7597235.1506081224221@mail.yahoo.com>

Hello Dear Fieldtrip users,
I have an EEG signal which I want to do artifact rejection on it.I've recorded the EEG during watching a video clip. 
My signal is 100 seconds and I've selected 50 seconds.I can find EOG artifact but I can't reject it. Would you please let me know how can I do it.


cfg  = [];
cfg.dataset   = 'myfile.eeg';  %BrainVision Recoreder EEG

cfg.trialdef.triallength   = inf;
cfg.trialdef.ntrials         = inf;
cfg   = ft_definetrial(cfg); trl     = cfg.trl;
data_org = ft_preprocessing(cfg);

%Select 30sec of data
cfg.latency        = 'all';
cfg.latency     = [0 30]; %start point and end point
cfg.avgovertime = 'no';
cfg.nanmean     = 'no';
data_s = ft_selectdata(cfg, data_org);
[cfg, artifact] = ft_artifact_eog(cfg,data_s);clean_data = ft_rejectartifact(cfg,data_s);
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From stephen.whitmarsh at gmail.com  Fri Sep 22 14:12:11 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 14:12:11 +0200
Subject: [FieldTrip] Warnings on CentOS break code?
Message-ID: <CAFrxm=yerQMk1i4DMm1XJp9JEGyO6Q5hf7_qBLLqQcRZmrym1w@mail.gmail.com>

Hi there,

Since upgrading to the latest FT version, some warnings throw an error
because FT cannot determine it's in a CentOS distro. At least, that's what
I think it is?

Am I missing something?

Best,
Stephen

cfg = [];
cfg.artfctdef               = artdef_MEG{ipart};
cfg.artfctdef.reject        = 'partial';
cfg.artfctdef.minaccepttim  = 3;
data{ipart}                 = ft_rejectartifact(cfg,data{ipart});

results in:

Error using ft_platform_supports (line 134)
unsupported value for first argument: html

Error in ft_notification (line 376)
          if ft_platform_supports('html')

Error in ft_warning (line 63)
  ft_notification(varargin{:});

Error in getdimord>warning_dimord_could_not_be_determined (line 621)
  ft_warning('%s\n\n%s', msg,content);

Error in getdimord (line 572)
  warning_dimord_could_not_be_determined(field,data);

Error in ft_selectdata (line 201)
  dimord{i} = getdimord(varargin{1}, datfield{i});

Error in WANDER_common_filter_DICS (line 85)
    hdr = ft_selectdata(cfg,hdr);

134     error('unsupported value for first argument: %s', what);
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From hamedtaheri at yahoo.com  Fri Sep 22 19:06:53 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Fri, 22 Sep 2017 17:06:53 +0000 (UTC)
Subject: [FieldTrip] Artifact Removing Problem
References: <28989140.7825054.1506100013966.ref@mail.yahoo.com>
Message-ID: <28989140.7825054.1506100013966@mail.yahoo.com>

Hi all
I've tried to remove EOG, jump and muscle artifact from my EEG.I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts. ( No filter, remove) . I want to filter my signal no remove some part of mt signal. 
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.Could you please help me what is my wrong?
Best Regards,Hamed
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From hamedtaheri at yahoo.com  Sun Sep 24 17:37:43 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Sun, 24 Sep 2017 15:37:43 +0000 (UTC)
Subject: [FieldTrip] Artifact Rejection Problem
References: <383387083.4716793.1506267463295.ref@mail.yahoo.com>
Message-ID: <383387083.4716793.1506267463295@mail.yahoo.com>

Hi all
I've tried to remove EOG, jump and muscle artifact from my EEG.I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts. I want to filter my signal no remove some part of my signal. 
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.Could you please help me what is my wrong?
cfg.artfctdef.eog.artifact = artifact_EOG; 
cfg.artfctdef.jump.artifact = artifact_jump;
cfg.artfctdef.muscle.artifact = artifact_muscle;
cfg.artfctdef.reject = 'complet' ;
data_no_artifacts = ft_rejectartifact(cfg,data_int);


Best Regards,Hamed

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From mailtome.2113 at gmail.com  Mon Sep 25 03:23:35 2017
From: mailtome.2113 at gmail.com (Arti Abhishek)
Date: Mon, 25 Sep 2017 11:23:35 +1000
Subject: [FieldTrip] Question regarding clusterplot
Message-ID: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>

Dear list,

I am trying to plot significant clusters from the cluster based permutation
test on the ERPs. I want to plot the p values on a binary fashion (p<.05).
I just don't want to highlight the electrodes, but I want to interpolate
the p values and plot topography (just like ERP topography, but in abinary
fashion). I was wondering whether there is a way to do it?

Thanks,
Arti
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From tzvetan.popov at uni-konstanz.de  Mon Sep 25 06:29:05 2017
From: tzvetan.popov at uni-konstanz.de (Tzvetan Popov)
Date: Mon, 25 Sep 2017 06:29:05 +0200
Subject: [FieldTrip] Question regarding clusterplot
In-Reply-To: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>
References: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>
Message-ID: <BCD13C5C-6A8F-4931-BF0A-4BC1E4ABCF05@uni-konstanz.de>

Hi Arti,

you could specify cfg.parameter = ‘mask’; instead of ‘avg’ which is the default.

best
tzvetan

> Am 25.09.2017 um 03:23 schrieb Arti Abhishek <mailtome.2113 at gmail.com>:
> 
> Dear list,
> 
> I am trying to plot significant clusters from the cluster based permutation test on the ERPs. I want to plot the p values on a binary fashion (p<.05). I just don't want to highlight the electrodes, but I want to interpolate the p values and plot topography (just like ERP topography, but in abinary fashion). I was wondering whether there is a way to do it?
> 
> Thanks,
> Arti
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip




From simeon.wong at sickkids.ca  Mon Sep 25 16:45:04 2017
From: simeon.wong at sickkids.ca (Simeon Wong)
Date: Mon, 25 Sep 2017 14:45:04 +0000
Subject: [FieldTrip] Artifact Rejection Problem
In-Reply-To: <383387083.4716793.1506267463295@mail.yahoo.com>
References: <383387083.4716793.1506267463295.ref@mail.yahoo.com>,
 <383387083.4716793.1506267463295@mail.yahoo.com>
Message-ID: <YTXPR0101MB1456A96D8CE2B437CDEFE9A3FD7A0@YTXPR0101MB1456.CANPRD01.PROD.OUTLOOK.COM>

Hi Hamed,

I believe ft_rejectartefact does not offer an option to simply remove any artefact. Removing artefacts is actually a non-trivial process that you can approach from several different ways.

Try using ft_componentanalysis to apply ICA to remove eye blinks and some eye movement. I'm not too sure about muscle artefact in EEG but perhaps setting a bandpass filter from 1-30 Hz may help attenuate most of it. You probably don't need to worry about jump artifacts since that typically applies only to MEG.


Regards,
Simeon Wong
________________________________
From: fieldtrip-bounces at science.ru.nl <fieldtrip-bounces at science.ru.nl> on behalf of Hamed Taheri <hamedtaheri at yahoo.com>
Sent: September 24, 2017 11:37:43 AM
To: FieldTrip Discussion List
Subject: [FieldTrip] Artifact Rejection Problem

Hi all

I've tried to remove EOG, jump and muscle artifact from my EEG.
I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts.
I want to filter my signal no remove some part of my signal.
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.
Could you please help me what is my wrong?

cfg.artfctdef.eog.artifact = artifact_EOG;
cfg.artfctdef.jump.artifact = artifact_jump;
cfg.artfctdef.muscle.artifact = artifact_muscle;
cfg.artfctdef.reject = 'complet' ;
data_no_artifacts = ft_rejectartifact(cfg,data_int);


Best Regards,
Hamed


________________________________

This e-mail may contain confidential, personal and/or health information(information which may be subject to legal restrictions on use, retention and/or disclosure) for the sole use of the intended recipient. Any review or distribution by anyone other than the person for whom it was originally intended is strictly prohibited. If you have received this e-mail in error, please contact the sender and delete all copies.



From bqrosen at ucsd.edu  Mon Sep 25 22:05:39 2017
From: bqrosen at ucsd.edu (Burke Rosen)
Date: Mon, 25 Sep 2017 13:05:39 -0700
Subject: [FieldTrip] ft_combineplanar 'svd' method
Message-ID: <C10A6011-0A3E-419C-8396-E238EFA66428@ucsd.edu>

Hello,

What is the principle behind the ‘svd’ and ‘absvd’ methods for ft_combineplanar?
And/or is there a citation which introduces these methods?

Thank you,

Burke Rosen







From michak at is.umk.pl  Mon Sep 25 23:44:00 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Mon, 25 Sep 2017 23:44:00 +0200
Subject: [FieldTrip] AAL Surface plot - weird black spots
In-Reply-To: <CAEJSOqhQCVyPfR1woRC69a8s1cgJimyfe3GBEuc38Uds9cVRbg@mail.gmail.com>
References: <CAEJSOqhQCVyPfR1woRC69a8s1cgJimyfe3GBEuc38Uds9cVRbg@mail.gmail.com>
Message-ID: <CAEJSOqg7wr4xn1b19sr1TKbOLMvpKAj+nFnZR4QONf_=_rFZHw@mail.gmail.com>

Dear Fieldtrippers,

I found the solution and it is simple. Just make sure that you have in your
config follwing lines:

cfg.projmethod = 'project'
cfg.projvec = [0 5]

Have a nice plots !


Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics

2017-07-31 14:15 GMT+02:00 Michał Komorowski <michak at is.umk.pl>:

> Dear Fieldtrippers,
>
> I am trying to reproduce brain surface pictures from this paper (Fig.5) :
> http://journals.plos.org/plosbiology/article?id=10.
> 1371/journal.pbio.1002498
>
> I wonder why I get weid black spots in surface plot (e.g. occipital area).
> What should I do to get those nice picures from link above?
> What I am doing wrong (code below)?
>
> Code for generating erroneous pictures (see attachment):
>
> mrifile             = [FieldtripPath 'template/anatomy/single_subj_
> T1.nii']
>
> mri = ft_read_mri(mrifile)
> mri.coordsys = 'mni'; % to prevent manual fixing of coordsys
>
> atlaspath       = [FieldtripPath 'template/atlas/aal/ROI_MNI_V4.nii'];
> atlas           = ft_read_atlas(atlaspath)
> atlas.anatomy   = mri.anatomy;
>
> cfg                     = [];
> cfg.method              = 'surface';
> cfg.projmethod          = 'project';
> cfg.camlight            = 'yes';
> %cfg.surffile = [FieldtripPath 'template/anatomy/surface_pial_left.mat'];
> % uncomment to project half brain
> cfg.locationcoordinates = 'voxel';
> cfg.cmap                = jet(116);
> cfg.cmap                = [[0,0,0]; cfg.cmap]
> cfg.funcolormap         = cfg.cmap;
> cfg.funparameter        = 'tissue';
> cfg.atlas               = atlaspath;
> ft_sourceplot(cfg, atlas)
>
>
> % check fit anatomy to atlas
> cfg                     = [];
> cfg.method              = 'ortho';
> cfg.locationcoordinates = 'voxel';
> cfg.cmap                = jet(116);
> cfg.cmap                = [[0,0,0]; cfg.cmap] % color map
> cfg.funcolormap         = cfg.cmap;
> cfg.funparameter        = 'tissue';
> ft_sourceplot(cfg, atlas)
>
> Best wishes.
>
> Michał Komorowski
>
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From nirofir2 at gmail.com  Tue Sep 26 15:12:30 2017
From: nirofir2 at gmail.com (Nir Ofir)
Date: Tue, 26 Sep 2017 16:12:30 +0300
Subject: [FieldTrip] Variable Number of Tapers in 'mtmfft' Frequency Analysis
Message-ID: <CAKiBOH=U2m3JYMrpitfbLi-bagc+yu0fw1yK2tJ7Lj8YoLZuuA@mail.gmail.com>

Hi Fieldtrip users,

ft_freqanalysis (FT version 20170404) does not allow using a variable
number of tapers in 'mtmfft' mode (lines 462-465 display a warning and keep
only the first element of cfg.tapsmofrq), but it seems like
ft_specest_mtmfft does have an implementation of a variable number of
tapers (lines 286-348). It also seems like mtm_specest_mtmconvol, which
allows variable number of tapers, calls ft_specest_mtmfft. So 2 questions:
1. Is the variable number of tapers option used in mtmfft in some other way?
2. What is the reason for not allowing a variable number of tapers in
mtmfft generally?

Thanks!
Nir Ofir
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From bog.louisa at gmail.com  Wed Sep 27 22:01:53 2017
From: bog.louisa at gmail.com (Louisa Bogaerts)
Date: Wed, 27 Sep 2017 23:01:53 +0300
Subject: [FieldTrip] reading in and preprocessing gtec_mat data
Message-ID: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>

Hello everyone,



In the lab or Dr. Landau we recently started using a *g.tech EEG setup* and
*Simulink* record the data. We used the newest version of Fieldtrip to try
analyze the data.



Simulink outputs the data as a .mat file (an example here:
https://www.dropbox.com/s/6xgio9w81qx94bq/example.mat?dl=0), and according
to the fieldtrip documentation this data format should now be supported: e.g.,
https://github.com/fieldtrip/fieldtrip/blob/master/fileio/ft_read_data.m,
lines 274-276:

if any(strcmp(dataformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat',

'gtec_hdf5', 'mega_neurone'}))



However, it seems that multiple Fieldtrip functions are “looking” for a
header file that is not found.

-   When reading in the data with ft_read_data() we get the following error
messages (whereas simply loading them with load() works fine):

Error using ft_notification (line 340)

unsupported header format "matlab"



Error in ft_error (line 39)

  ft_notification(varargin{:});



Error in ft_read_header (line 2325)

        ft_error('unsupported header format "%s"', headerformat);



Error in ft_read_data (line 200)

  hdr = ft_read_header(filename, 'headerformat', headerformat, 'chanindx',

  chanindx, 'checkmaxfilter', checkmaxfilter);



- The same error messages show when using ft_preprocessing().



Does anyone have experience reading in and preprocessing gtech_mat data and
can he/she help us understand how to save the header info so that fieldtrip
can read it and recognise the data as gtec_mat?



Any help will be very much appreciated.



Louisa, Omri & Flor
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From isac.sehlstedt at psy.gu.se  Fri Sep 29 12:19:51 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Fri, 29 Sep 2017 10:19:51 +0000
Subject: [FieldTrip] Follow up question: Computing pca variables (i.e.
 Latent, and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
References: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
Message-ID: <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,


This is a kind reminder of a follow-up question to a previous question with the same mail-topic.


I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.


Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?


Pictures are found using this link:

https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0


Very Best,

Isac



————————————————— The code —————————————————


clear all; close all;


%% Load

load('averages_for_ft.mat')




%% define layout

cfg = [];

cfg.elec=PreOdd_ft{1, 1}.elec;

cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)

layout = ft_prepare_layout(cfg)




%% Make the computations


% Dummy varibles

Cond1 = [];

Cond2 = [];

theDiff = [];

theDiff_ft = {};



%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions

    curr_Cond2 = Cond2_ft{i}.avg;

    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions

    cfg = [];

    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});

    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math

    cfg  = [];

    cfg.operation = 'subtract';

    cfg.parameter = 'avg';

    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);

    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions

    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.

    Cond2 = [Cond2 curr_Cond2];

    Cond1 = [Cond1 curr_Cond1];

    theDiff = [theDiff curr_difference_avg];

end




%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};

dummy_Cond2.avg = Cond2;

dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};

dummy_Cond1.avg = Cond1;

dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};

dummy_theDiff.avg = theDiff;

dummy_theDiff.time = 1:1:size(theDiff,2);




%% Run the PCA



cfg = [];

cfg.method  = 'pca';

cfg.layout = layout;


Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);

Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);

theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);




%% Revert back to subject level


cfgCond2 = [];

cfgCond2.unmixing = Cond2_comp.unmixing;

cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];

cfgCond1.unmixing = Cond1_comp.unmixing;

cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];

cfgtheDiff.unmixing = theDiff_comp.unmixing;

cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)

    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});

    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});

    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );

end
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From jan.schoffelen at donders.ru.nl  Fri Sep 29 12:36:30 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Fri, 29 Sep 2017 10:36:30 +0000
Subject: [FieldTrip] Follow up question: Computing pca variables (i.e.
 Latent, and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>
References: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
 <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>
Message-ID: <97B82971-ABD0-49F2-81C4-235AE4D09162@donders.ru.nl>

perhaps you may want to check the ‘avg’ field.

JM

On 29 Sep 2017, at 12:19, Isac Sehlstedt <isac.sehlstedt at psy.gu.se<mailto:isac.sehlstedt at psy.gu.se>> wrote:

Dear fieldtripers,

This is a kind reminder of a follow-up question to a previous question with the same mail-topic.

I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.

Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?

Pictures are found using this link:
https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0

Very Best,
Isac


————————————————— The code —————————————————

clear all; close all;

%% Load
load('averages_for_ft.mat')



%% define layout
cfg = [];
cfg.elec=PreOdd_ft{1, 1}.elec;
cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)
layout = ft_prepare_layout(cfg)



%% Make the computations

% Dummy varibles
Cond1 = [];
Cond2 = [];
theDiff = [];
theDiff_ft = {};


%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions
    curr_Cond2 = Cond2_ft{i}.avg;
    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions
    cfg = [];
    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});
    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math
    cfg  = [];
    cfg.operation = 'subtract';
    cfg.parameter = 'avg';
    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);
    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions
    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.
    Cond2 = [Cond2 curr_Cond2];
    Cond1 = [Cond1 curr_Cond1];
    theDiff = [theDiff curr_difference_avg];
end



%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};
dummy_Cond2.avg = Cond2;
dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};
dummy_Cond1.avg = Cond1;
dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};
dummy_theDiff.avg = theDiff;
dummy_theDiff.time = 1:1:size(theDiff,2);



%% Run the PCA



cfg = [];
cfg.method  = 'pca';
cfg.layout = layout;

Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);
Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);
theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);



%% Revert back to subject level

cfgCond2 = [];
cfgCond2.unmixing = Cond2_comp.unmixing;
cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];
cfgCond1.unmixing = Cond1_comp.unmixing;
cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];
cfgtheDiff.unmixing = theDiff_comp.unmixing;
cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)
    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});
    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});
    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );
end
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Fri Sep 29 12:42:41 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 29 Sep 2017 12:42:41 +0200
Subject: [FieldTrip] reading in and preprocessing gtec_mat data
In-Reply-To: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>
References: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>
Message-ID: <CAFrxm=w7zoX0bberQ2t-tzAmCCRjy9YXM3noDsaapZ7OozM8WQ@mail.gmail.com>

Hi Louisa et al,

It seems that you are actually not trying to read data in the Gtec data
format, but that of simulink (which was saved as a mat file, as a matlab
file).
So, you should be able to just read your mat file and then put your data in
a fieldtrip data structure.

See: http://www.fieldtriptoolbox.org/faq/how_can_i_import_my_own_dataformat

Cheers,
Stephen










On 27 September 2017 at 22:01, Louisa Bogaerts <bog.louisa at gmail.com> wrote:

> Hello everyone,
>
>
>
> In the lab or Dr. Landau we recently started using a *g.tech EEG setup*
> and *Simulink* record the data. We used the newest version of Fieldtrip
> to try analyze the data.
>
>
>
> Simulink outputs the data as a .mat file (an example here:
> https://www.dropbox.com/s/6xgio9w81qx94bq/example.mat?dl=0), and
> according to the fieldtrip documentation this data format should now be
> supported: e.g., https://github.com/fieldtrip/f
> ieldtrip/blob/master/fileio/ft_read_data.m, lines 274-276:
>
> if any(strcmp(dataformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat',
>
> 'gtec_hdf5', 'mega_neurone'}))
>
>
>
> However, it seems that multiple Fieldtrip functions are “looking” for a
> header file that is not found.
>
> -   When reading in the data with ft_read_data() we get the following
> error messages (whereas simply loading them with load() works fine):
>
> Error using ft_notification (line 340)
>
> unsupported header format "matlab"
>
>
>
> Error in ft_error (line 39)
>
>   ft_notification(varargin{:});
>
>
>
> Error in ft_read_header (line 2325)
>
>         ft_error('unsupported header format "%s"', headerformat);
>
>
>
> Error in ft_read_data (line 200)
>
>   hdr = ft_read_header(filename, 'headerformat', headerformat, 'chanindx',
>
>   chanindx, 'checkmaxfilter', checkmaxfilter);
>
>
>
> - The same error messages show when using ft_preprocessing().
>
>
>
> Does anyone have experience reading in and preprocessing gtech_mat data
> and can he/she help us understand how to save the header info so that
> fieldtrip can read it and recognise the data as gtec_mat?
>
>
>
> Any help will be very much appreciated.
>
>
>
> Louisa, Omri & Flor
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From psc.dav at gmail.com  Sat Sep  2 16:45:47 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Sat, 2 Sep 2017 16:45:47 +0200
Subject: [FieldTrip] Fwd: single trials eLoreta
In-Reply-To: <CAK-BAC9v4U74suf_A4WNKKHRZtNM=zWWR7ZdpFSjQVzXrfp=uA@mail.gmail.com>
References: <CAK-BAC9v4U74suf_A4WNKKHRZtNM=zWWR7ZdpFSjQVzXrfp=uA@mail.gmail.com>
Message-ID: <CAK-BAC_sO4pJiSnNC1qY2EYdojO1XJgmmmhKmKGLLhF-F0QT_A@mail.gmail.com>

Dear fieldtrip experts,
I am posting again my questions, hoping that someone has experience with a
similar pipeline and can give some feedback.

I am trying to use the eLoreta method to get single-trial estimates of
source activity from specific ROIs, in the following way:

% eLORETA
cfg = [];
cfg.method                          = 'eloreta';
cfg.grid                                  = leadfield;
cfg.headmodel                       = headmodel;
cfg.eloreta.keepfilter              = 'yes';
cfg.eloreta.normalize               = 'yes';
cfg.eloreta.lambda                  = 0.05;
     *(1)
cfg.eloreta.projectnoise            = 'yes';
eLO_source                          = ft_sourceanalysis(cfg,data);

% in the above line, "data" is the results of ft_timelockanalysis
% with cfg.covariance = 'yes';
     *(2)

% then I put the source positions from the MNI template I
% used for the sourcemodel (see:
% http://www.fieldtriptoolbox.org/tutorial/sourcemodel#subject-
% specific_grids_that_are_equivalent_across_subjects_in_normalized_space
eLO_source.pos                      = template_grid.pos;
iPOS                                = eLO_source.pos;
iPOS(eLO_source.inside==0,:)        = NaN; % only points inside gray matter

% Then I select ROIs (here only one for simplicity) to extract single-trial
source activity:
[v,I]      = min(pdist2(iPOS, ROIs_mni , 'euclidean'));

% And I multiply the spatial filter for the EEG data in each trial
W            = eLO_source.avg.filter{I}; % filter at my ROI of interest
for tr = 1:size(data.trial,1)
       % loop over trials
     trials{tr} = W * squeeze(data.trial(tr,:,:));
   *(3)
end

Is this approach correct?
My main questions are:

*(1) Is there a way to select the best lambda parameter (e.g., selecting
the one that best approximates the activity at the EEG channels level)?

*(2) I am confused about the role of the covariance, since it doesn't seem
to be used when source activity is estimated using the set of spatial
filters at the single trial

*(3) Is the "trials{tr} = W * squeeze(data.trial(tr,:,:)); " approach
correct to get time-series of source activity in a ROI?

Looking forward for your feedback.

Best,
David


-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From juliacoopiza at gmail.com  Sun Sep  3 17:14:06 2017
From: juliacoopiza at gmail.com (Julia Coopi)
Date: Sun, 3 Sep 2017 09:14:06 -0600
Subject: [FieldTrip] Using PPC method
In-Reply-To: <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
Message-ID: <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have
any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu> wrote:

> Dear Tianyang,
>
> maybe it's a good idea to download the accompanying sample data from the
> tutorial and look if you can recreate the shown data structure. Then look
> closer into the values of the respective fields. That should give you a
> better grasp on what is required there.
>
> I have not fully looked into the code but my feeling is that
> spikeTrials.timestamp is not of any further use and is just carried from
> the data structure before (which was not cut into trials and where the raw
> timestamps were useful). The timing of spikes relative to the trial zero
> point is fully described in the fields ".time", ".trial" and ".trialtime".
> Best,
> Andreas
>
>
> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
> on behalf of 马天阳 [tianyangma2013 at gmail.com]
> *Sent:* Monday, August 28, 2017 5:31 PM
> *To:* FieldTrip discussion list
> *Subject:* Re: [FieldTrip] Using PPC method
>
> Dear Andreas,
>
> I still don't quite understand the tutorial.
>
> spikeTrials =
>          label: {'sig002a_wf'  'sig003a_wf'}
>      timestamp: {[1x83601 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
>       waveform: {[1x32x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>           unit: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>            hdr: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>         dimord: '{chan}_lead_time_spike'
>            cfg: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>           time: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>          trial: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>      trialtime: [600x2 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]
>
> Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?
>
> I feel I am quite lost.
>
> Best,
>
> Tianyang
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From k.lehongre-ihu at icm-institute.org  Mon Sep  4 11:26:52 2017
From: k.lehongre-ihu at icm-institute.org (Katia Lehongre)
Date: Mon, 4 Sep 2017 11:26:52 +0200
Subject: [FieldTrip] Workshop on intracranial recordings in human,
	October 3-4, Paris
Message-ID: <6eba6c50-169d-a0f0-ab1e-73d808e81d24@icm-institute.org>

Dear all,

The first*WIRED*(*W*orkshop on *I*ntracranial *R*ecordings in humans : 
*E*pilepsy, *D*BS), will be held in Paris, France, at the Brain and 
Spine Institute (ICM) on October 3 and 4.
Registration is opened and*free*.

Conferences, technical discussions, poster session (prize worth 800€ to 
be awarded), commercial solutions, wine and cheese…

  All information and registration to this event on our website: 
http://wired-icm.org <http://wired-icm.org/>

_Note that_ only *a few spots are left* for poster presentation and that 
we have reach *70% of full capacity* including people from 10 Parisian 
institutes, 6 major French cities and 9 countries.

We hope to see you there!
The organizers

_Organization_ : Katia Lehongre, Adrien Schramm –

_Scientific Committee_: Vincent Navarro, Katia Lehongre, Brian Lau, 
Nathalie George, Michel Le Van Quyen, Marie Laure Welter, Lionel Naccache



*_Program_*

*Tuesday October 3^rd *

*AM*

*Research Lecture Session*

**

9:00 *   – /Welcome breakfast/*

9:30*    –   Event Introduction* 
/V. Navarro,  K. Lehongre/

9:45*     –   Keynote : Epileptic ictal wavefront*

*/C. Schevon/ 
<http://columbianeurology.org/profile/caschevon>*/ (Columbia, USA)/

10:45 *– /Break/*

11:00*   –   DBS: Title to be determined*

/J. Bastin (Grenoble, France)/

11:45*   –   DBS: **Title to be determined*

12:30*    – /Lunch & Poster session/*

	

*PM*

*Methodological aspects*

**

14:00 *   –   Equipment : Focus on new electrodes* 
 /L. Valton/ 
<http://www.cerco.ups-tlse.fr/Luc-Valton>/ (Toulouse, France)/

15:00*    –   Analysis : Spike sorting techniques* 
 /F. Mormann/ 
<http://www.sfb1089.de/index.php/2014-01-16-17-35-38/principal-investigatiors/29-priv-doz-dr-dr-florian-mormann>/ (Bonn, 
Germany)/

16:00 *– /Coffe Break/*

16:30*   –   Imaging: Electrode localization*

*/S. Fernandez/ <http://www.cenir.org/>*/ (Paris, France/)

18:00*   –   Wine and Cheese session*

**

*Wednesday October 4^th *

*AM*

*Research Lecture Session*

8:45 *   – /Breakfast/*

9:00*    –   Keynote : Title to be determined*

*/P. Brown/ <https://www.ndcn.ox.ac.uk/team/peter-brown>*/ (Oxford, UK)/

10:00*   –   Memory encoding*

*/N. Axmacher/ 
<http://www.rd.ruhr-uni-bochum.de/neuro/wiss/pi/axmacher/index.html.en>*/ (Bochum, 
Germany)/

10:45 *–   Visual processing*

/L. Reddy / <http://cerco.ups-tlse.fr/%7Elreddy/Leila_Reddy/>/(Toulouse, 
France/)

11:30*   – /Coffee Break/*

11:45*   –   Focus: Imaging and electrophysiology*

/C. Ciumas-Gaumond (Lyon, France)/

12:45*    –  Conclusion & poster award*

	

*PM – Off event*

*Réunion du groupe Français Microelectrode*

*
*

*Note that *The *Blackrock Microsystems’ Clinical Electrophysiology 
Workshop* will occur on Monday 2nd. All info and registration on 
this satellite event click here 
<http://blackrockmicro.com/european-workshop>.

**

-- 
Katia Lehongre

Ingénieure de recherche IHU-A-ICM
PF STIM CENIR, bureau -1.041
ICM, UPMC/Inserm U1127/CNRS UMR7225
Institut du Cerveau et de la Moelle épinière
Hôpital Pitié-Salpêtrière
47 Boulevard de l'Hôpital
CS 21414
75646 PARIS CEDEX 13
tel: 01 57 27 47 14

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From linzhangysu at outlook.com  Mon Sep  4 13:09:45 2017
From: linzhangysu at outlook.com (linzhangysu at outlook.com)
Date: Mon, 4 Sep 2017 11:09:45 +0000
Subject: [FieldTrip] WPLI
Message-ID: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>


[cid:image002.png at 01D325AA.E537BE20]

I want to calculate the WPLI of 64 channels for one subject. But I met some questions.
Firstly, I didn’t understand the meaning of repetitions (just as the maker of the figure ). The dimension of repetitions was 1 in my MATLAB code , which resulted  in the WPLI result are NaN vectors. How can I solve the problem about ‘repetitions’?
I am looking forward to your reply very urgently, Thank you !

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From michelic72 at gmail.com  Mon Sep  4 13:19:27 2017
From: michelic72 at gmail.com (Cristiano Micheli)
Date: Mon, 4 Sep 2017 13:19:27 +0200
Subject: [FieldTrip] WPLI
In-Reply-To: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
References: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
Message-ID: <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>

Dear Linzhangysu

the wPLI metric requires you to have your experimental design matrix
organized in 'repetitions' or 'trials'.
This is typically the case (but not only) of an evoked related design,
where the repetitions dimension is used to calculate your 'average' wPLI
across trials, and this is what the FT code is doing for you in the
*ft_connectivity_wpli* function.
In summary, with this formula you will not be able to apply wPLI to a
single trial (e.g. like in resting state). If your experiment allows
organizing the experimental data into 'trials' (with the operation of
epoching) then you can solve your problem, otherwise you will have to use
other metrics of phase coupling.

IHTH
Cris Micheli



On Mon, Sep 4, 2017 at 1:09 PM, linzhangysu at outlook.com <
linzhangysu at outlook.com> wrote:

>
>
> [image: cid:image002.png at 01D325AA.E537BE20]
>
>
>
> I want to calculate the WPLI of 64 channels for one subject. But I met
> some questions.
>
> Firstly, I didn’t understand the meaning of repetitions (just as the
> maker of the figure ). The dimension of repetitions was 1 in my MATLAB code
> , which resulted  in the WPLI result are NaN vectors. How can I solve the
> problem about ‘repetitions’?
>
> I am looking forward to your reply very urgently, Thank you !
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From awutz at mit.edu  Mon Sep  4 14:24:47 2017
From: awutz at mit.edu (Andreas Wutz)
Date: Mon, 4 Sep 2017 12:24:47 +0000
Subject: [FieldTrip] Using PPC method
In-Reply-To: <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>,
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
Message-ID: <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>

Dear Julia,

I did not see your error message. Maybe, your lfp data structure is still in a continuous recording format without a trial definition?

________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Julia Coopi [juliacoopiza at gmail.com]
Sent: Sunday, September 03, 2017 11:14 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Tianyang,

maybe it's a good idea to download the accompanying sample data from the tutorial and look if you can recreate the shown data structure. Then look closer into the values of the respective fields. That should give you a better grasp on what is required there.

I have not fully looked into the code but my feeling is that spikeTrials.timestamp is not of any further use and is just carried from the data structure before (which was not cut into trials and where the raw timestamps were useful). The timing of spikes relative to the trial zero point is fully described in the fields ".time", ".trial" and ".trialtime".
Best,
Andreas


From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of 马天阳 [tianyangma2013 at gmail.com<mailto:tianyangma2013 at gmail.com>]
Sent: Monday, August 28, 2017 5:31 PM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

I still don't quite understand the tutorial.

spikeTrials =
         label: {'sig002a_wf'  'sig003a_wf'}
     timestamp: {[1x83601 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
      waveform: {[1x32x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
          unit: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
           hdr: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
        dimord: '{chan}_lead_time_spike'
           cfg: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
          time: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
         trial: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
     trialtime: [600x2 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]

Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?

I feel I am quite lost.

Best,

Tianyang

_______________________________________________
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From christine.blume at sbg.ac.at  Mon Sep  4 14:28:47 2017
From: christine.blume at sbg.ac.at (Blume Christine)
Date: Mon, 4 Sep 2017 12:28:47 +0000
Subject: [FieldTrip] Effect size measure for cluster-based
	permutation	tests
In-Reply-To: <CAGZVU0-MSDjvM3xZAAr9J5z25DD=ia+eFMAFPwnq30qGK-oeiQ@mail.gmail.com>
References: <E20FE863CD1C294AA55E415F4C446424C2B80B2D@xchmbx2.is.sbg.ac.at>
 <CAGZVU0-MSDjvM3xZAAr9J5z25DD=ia+eFMAFPwnq30qGK-oeiQ@mail.gmail.com>
Message-ID: <E20FE863CD1C294AA55E415F4C446424C2B8347B@xchmbx2.is.sbg.ac.at>

Hi Alik,

Thanks a lot for your suggestion, which I hoped would prompt more answers. Does anyone have suggestions on how exactly to implement the calculation of an effect size measure?

Best,
Christine

Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Alik Widge
Gesendet: Mittwoch, 23. August 2017 16:42
An: FieldTrip discussion list
Betreff: Re: [FieldTrip] Effect size measure for cluster-based permutation tests

My naive answer, which perhaps will provoke Eric to provide a better one: you have the actual  cluster statistic and its permutation distribution under the null hypothesis. It seems as though that distribution could be assumed Gaussian and something like Cohen's d calculated.

On Aug 23, 2017 9:35 AM, "Blume Christine" <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>> wrote:
Dear all,

I came across a question posted by someone about a year ago, which concerned effect size measures for cluster-based permutation tests. Unfortunately, the question does not seem to have been answered…

Q: I am using cluster-based permutation tests (depsamplesT, on time-frequency data) and am wondering how to best calculate an effect size from that.

Best,
Christine



_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From xiew1202 at gmail.com  Mon Sep  4 14:44:26 2017
From: xiew1202 at gmail.com (Xie Wanze)
Date: Mon, 04 Sep 2017 12:44:26 +0000
Subject: [FieldTrip] WPLI
In-Reply-To: <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>
References: <HK2PR03MB142721E1BC1F844CBCB28E2CB2910@HK2PR03MB1427.apcprd03.prod.outlook.com>
 <CADW7XCCfqsqCxGiNqXZJ8b6i=Y2hQUCrtoZf-wOsyQWDTDC46A@mail.gmail.com>
Message-ID: <CAA6G0Y87vgohT_W-vS-M4TUyoBdCPNBpokCVkUZy1XCt-Z5y1w@mail.gmail.com>

Dear Linzhang,
As Cris mentioned, you cannot calculate the WPLi value with one trial. The
WPLI toolbox calculates the CSD and PSD for each single trial, and then get
the "correlation" of the phase information across trials.  This apparently
could not be done with one trial.
If you have continuous data you may try to segment it into epochs.

Wanze


Cristiano Micheli <michelic72 at gmail.com>于2017年9月4日 周一上午7:35写道：

> Dear Linzhangysu
>
> the wPLI metric requires you to have your experimental design matrix
> organized in 'repetitions' or 'trials'.
> This is typically the case (but not only) of an evoked related design,
> where the repetitions dimension is used to calculate your 'average' wPLI
> across trials, and this is what the FT code is doing for you in the
> *ft_connectivity_wpli* function.
> In summary, with this formula you will not be able to apply wPLI to a
> single trial (e.g. like in resting state). If your experiment allows
> organizing the experimental data into 'trials' (with the operation of
> epoching) then you can solve your problem, otherwise you will have to use
> other metrics of phase coupling.
>
> IHTH
> Cris Micheli
>
>
>
> On Mon, Sep 4, 2017 at 1:09 PM, linzhangysu at outlook.com <
> linzhangysu at outlook.com> wrote:
>
>>
>>
>> [image: cid:image002.png at 01D325AA.E537BE20]
>>
>>
>>
>> I want to calculate the WPLI of 64 channels for one subject. But I met
>> some questions.
>>
>> Firstly, I didn’t understand the meaning of repetitions (just as the
>> maker of the figure ). The dimension of repetitions was 1 in my MATLAB code
>> , which resulted  in the WPLI result are NaN vectors. How can I solve the
>> problem about ‘repetitions’?
>>
>> I am looking forward to your reply very urgently, Thank you !
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From e.maris at donders.ru.nl  Tue Sep  5 14:12:52 2017
From: e.maris at donders.ru.nl (Maris, E.G.G. (Eric))
Date: Tue, 5 Sep 2017 12:12:52 +0000
Subject: [FieldTrip] Effect size measure for cluster-based	permutation
	tests
In-Reply-To: <mailman.12.1504605602.27164.fieldtrip@science.ru.nl>
References: <mailman.12.1504605602.27164.fieldtrip@science.ru.nl>
Message-ID: <59403DFC-9FBC-4585-928E-84787AE7E99F@donders.ru.nl>

Dear discussion list readers & contributors (especially Christine Blume),

There have been many questions (not only on the FT discussion list) about the calculation of effect size measures in the context of cluster-based permutation tests. I will continue my reply under the quotes below.


From: Blume Christine <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>>
Subject: Re: [FieldTrip] Effect size measure for cluster-based permutation tests
Date: 4 September 2017 at 14:28:47 GMT+2
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Reply-To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>


Hi Alik,

Thanks a lot for your suggestion, which I hoped would prompt more answers. Does anyone have suggestions on how exactly to implement the calculation of an effect size measure?

Best,
Christine

Von: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Alik Widge
Gesendet: Mittwoch, 23. August 2017 16:42
An: FieldTrip discussion list
Betreff: Re: [FieldTrip] Effect size measure for cluster-based permutation tests

My naive answer, which perhaps will provoke Eric to provide a better one: you have the actual  cluster statistic and its permutation distribution under the null hypothesis. It seems as though that distribution could be assumed Gaussian and something like Cohen's d calculated.

On Aug 23, 2017 9:35 AM, "Blume Christine" <christine.blume at sbg.ac.at<mailto:christine.blume at sbg.ac.at>> wrote:
Dear all,

I came across a question posted by someone about a year ago, which concerned effect size measures for cluster-based permutation tests. Unfortunately, the question does not seem to have been answered…

Q: I am using cluster-based permutation tests (depsamplesT, on time-frequency data) and am wondering how to best calculate an effect size from that.

Best,
Christine


Giving a useful answer to this question requires that one knows for what this effect size measure will be used. Typically, a standardised effect size measure is required to perform a power calculation. A power calculation is possible for a number of parametric statistical tests such as the T- and  the F-test. As input for this power calculation, Cohen’s d is required. A sensible value for Cohen’s d can sometimes be found in published studies (preferably with large sample sizes).

Cohen’s d can easily be obtained from the outcome of a cluster-based permutation test:

  1.  Calculate the non-standardised effect sizes by averaging the (sensor, frequency, time)-specific effects within the cluster of interest. Typically, the (sensor, frequency, time)-specific effects are raw differences between the subject averages for the experimental conditions that are being compared.
  2.  Calculate the standard deviation over the subjects of these non-standardised effect sizes.
  3.  Calculate Cohen’s d by dividing the grand average of the non-standardised effect sizes by the standard deviation obtained in 2.

Unfortunately, Cohen’s d calculated in this way, will be biased, and therefore cannot be used for a power calculation. This type of bias is sometimes denoted as “double dipping”.

In general, it is extremely challenging to perform a power calculations for statistical analyses that involve high-dimensional data. This does not only hold for electrophysiological, but also for fMRI data. To get idea about the difficulties that one encounters, have a look at this paper from the fMRI community: http://www.biorxiv.org/content/early/2016/04/20/049429. For the analysis of high-dimensional electrophysiological data, quite some statistical work still has to be done.

best,
Eric Maris












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From bioeng.yoosofzadeh at gmail.com  Wed Sep  6 04:22:10 2017
From: bioeng.yoosofzadeh at gmail.com (Vahab Yousofzadeh)
Date: Tue, 5 Sep 2017 22:22:10 -0400
Subject: [FieldTrip] Effect size measure for cluster-based
Message-ID: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>

Hi Christine,

Based on my understanding from the following link the effect size
(that correspond to the significant clusters) can not be derived from
p (or t)-values by
ft_sourcestatistics:
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test

Cheers,
Vahab


From christine.blume at sbg.ac.at  Wed Sep  6 09:10:25 2017
From: christine.blume at sbg.ac.at (Blume Christine)
Date: Wed, 6 Sep 2017 07:10:25 +0000
Subject: [FieldTrip] Effect size measure for cluster-based
In-Reply-To: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>
References: <CAL5FLdKLdLdXq6i6CqnGxM4cRoc1-QXCWP0kTCAhZkdEdqojMw@mail.gmail.com>
Message-ID: <E20FE863CD1C294AA55E415F4C446424C2B83B02@xchmbx2.is.sbg.ac.at>

Dear all,

Thank you so much for all the suggestions and hints. I will look into them!

Best,
Christine


________________________________________
Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]&quot; im Auftrag von &quot;Vahab Yousofzadeh [bioeng.yoosofzadeh at gmail.com]
Gesendet: Mittwoch, 06. September 2017 04:22
An: fieldtrip at science.ru.nl
Betreff: Re: [FieldTrip] Effect size measure for cluster-based

Hi Christine,

Based on my understanding from the following link the effect size
(that correspond to the significant clusters) can not be derived from
p (or t)-values by
ft_sourcestatistics:
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test

Cheers,
Vahab
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip



From preted at mcmaster.ca  Wed Sep  6 16:48:26 2017
From: preted at mcmaster.ca (David)
Date: Wed, 6 Sep 2017 10:48:26 -0400
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>

Hello everyone, 

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I’ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I’m running into is stated below as well as my code and I’ve attached an image of what the data looks like. I’ve tried following the tutorials and searching through the mailing list and can’t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE: 
“Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);
  
Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);”



Thank you, 
David Prete
Ph.D. Candidate 
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour 
McMaster University 

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From jan.schoffelen at donders.ru.nl  Wed Sep  6 17:18:53 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Wed, 6 Sep 2017 15:18:53 +0000
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>
References: <201709061448.v86EmJec019710@pinegw03.uts.mcmaster.ca>
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570@donders.ru.nl>

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I’ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I’m running into is stated below as well as my code and I’ve attached an image of what the data looks like. I’ve tried following the tutorials and searching through the mailing list and can’t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
“Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);”



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
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fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
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From tokimoto at mejiro.ac.jp  Wed Sep  6 17:57:17 2017
From: tokimoto at mejiro.ac.jp (=?utf-8?B?5pmC5pys55yf5ZC+?=)
Date: Thu, 7 Sep 2017 00:57:17 +0900
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8@mejiro.ac.jp>

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp
******************************************

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From smoratti at psi.ucm.es  Wed Sep  6 18:25:54 2017
From: smoratti at psi.ucm.es (STEPHAN MORATTI)
Date: Wed, 6 Sep 2017 18:25:54 +0200
Subject: [FieldTrip] Learning agreementbuybr 8
In-Reply-To: <CAE20z9+OVvw9x6VbsgJzT4eQH9YNNxc09WzHMN3H522WadtHqg@mail.gmail.com>
References: <CAE20z9+OVvw9x6VbsgJzT4eQH9YNNxc09WzHMN3H522WadtHqg@mail.gmail.com>
Message-ID: <CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA@mail.gmail.com>

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es> escribió:

Buenos días Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
¿Serías tan amable de enviármelo firmado?

Gracias y perdón por las molestias,
Sara
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From preted at mcmaster.ca  Wed Sep  6 18:43:41 2017
From: preted at mcmaster.ca (David)
Date: Wed, 6 Sep 2017 12:43:41 -0400
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
References: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
Message-ID: <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>

Hi Jan-Mathijs, 

I’ve double checked and the label is written as ‘CZ’. So, it seems to be more than a typo, unfortunately. 

David 

From: fieldtrip-request at science.ru.nl
Sent: September 6, 2017 12:26 PM
To: fieldtrip at science.ru.nl
Subject: fieldtrip Digest, Vol 82, Issue 8

Send fieldtrip mailing list submissions to
	fieldtrip at science.ru.nl

To subscribe or unsubscribe via the World Wide Web, visit
	https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
or, via email, send a message with subject or body 'help' to
	fieldtrip-request at science.ru.nl

You can reach the person managing the list at
	fieldtrip-owner at science.ru.nl

When replying, please edit your Subject line so it is more specific
than "Re: Contents of fieldtrip digest..."


Today's Topics:

   1. Re: Reshape Error Using ft_freqstatistics
      (Schoffelen, J.M. (Jan Mathijs))
   2. Cluster-based permutation tests for 3 conditions (????)
   3. Re: Learning agreementbuybr 8 (STEPHAN MORATTI)


----------------------------------------------------------------------

Message: 1
Date: Wed, 6 Sep 2017 15:18:53 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl>
Content-Type: text/plain; charset="utf-8"

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I?ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I?m running into is stated below as well as my code and I?ve attached an image of what the data looks like. I?ve tried following the tutorials and searching through the mailing list and can?t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
?Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);?



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
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------------------------------

Message: 2
Date: Thu, 7 Sep 2017 00:57:17 +0900
From: ???? <tokimoto at mejiro.ac.jp>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp>
Content-Type: text/plain; charset="us-ascii"

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp
******************************************

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------------------------------

Message: 3
Date: Wed, 6 Sep 2017 18:25:54 +0200
From: STEPHAN MORATTI <smoratti at psi.ucm.es>
To: SARA RODRIGUEZ FREGENAL <srodri10 at ucm.es>, FieldTrip discussion
	list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] Learning agreementbuybr 8
Message-ID:
	<CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es> escribi?:

Buenos d?as Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
?Ser?as tan amable de envi?rmelo firmado?

Gracias y perd?n por las molestias,
Sara
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------------------------------

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

End of fieldtrip Digest, Vol 82, Issue 8
****************************************

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From jan.schoffelen at donders.ru.nl  Thu Sep  7 07:19:09 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 7 Sep 2017 05:19:09 +0000
Subject: [FieldTrip] Reshape Error Using ft_freqstatistics
In-Reply-To: <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>
References: <mailman.284.1504715158.2418.fieldtrip@science.ru.nl>
 <201709061643.v86GhZqL004768@pinegw03.uts.mcmaster.ca>
Message-ID: <5038FBE1-5E7B-4F6B-B152-4A500879F002@donders.ru.nl>

In that case I recommend that you try and interpret the error message in a bit more detail. From the information you provide nobody can tell what’s going on, apart from the fact that it is a low-level matlab error. I suggest to use the matlab debugger to investigate the size of ‘meanreshapeddat', and the value of ‘nchan’ ‘nfreq’ ‘nrepl’ ‘ntime’ in this specific case.

And also think about your specification of cfg.latency. Note that you only include positive latencies to be tested, but you ask for actvsblT as a test statistic, which name suggests to use a baseline (i.e. require latencies of < 0 in the data).

JM


On 6 Sep 2017, at 18:43, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca>> wrote:

Hi Jan-Mathijs,

I’ve double checked and the label is written as ‘CZ’. So, it seems to be more than a typo, unfortunately.

David

From: fieldtrip-request at science.ru.nl<mailto:fieldtrip-request at science.ru.nl>
Sent: September 6, 2017 12:26 PM
To: fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>
Subject: fieldtrip Digest, Vol 82, Issue 8

Send fieldtrip mailing list submissions to
                fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>

To subscribe or unsubscribe via the World Wide Web, visit
                https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
or, via email, send a message with subject or body 'help' to
                fieldtrip-request at science.ru.nl<mailto:fieldtrip-request at science.ru.nl>

You can reach the person managing the list at
                fieldtrip-owner at science.ru.nl<mailto:fieldtrip-owner at science.ru.nl>

When replying, please edit your Subject line so it is more specific
than "Re: Contents of fieldtrip digest..."


Today's Topics:

   1. Re: Reshape Error Using ft_freqstatistics
      (Schoffelen, J.M. (Jan Mathijs))
   2. Cluster-based permutation tests for 3 conditions (????)
   3. Re: Learning agreementbuybr 8 (STEPHAN MORATTI)


----------------------------------------------------------------------

Message: 1
Date: Wed, 6 Sep 2017 15:18:53 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>>
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Reshape Error Using ft_freqstatistics
Message-ID: <0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl<mailto:0DE2CF55-749C-4F63-9DFA-FE226A971570 at donders.ru.nl>>
Content-Type: text/plain; charset="utf-8"

Hi David,

Have you checked whether this could be due to a potential typo in the specification of your cfg.channel (e.g.: CZ versus Cz)?
Best,
Jan-Mathijs



On 6 Sep 2017, at 16:48, David <preted at mcmaster.ca<mailto:preted at mcmaster.ca><mailto:preted at mcmaster.ca>> wrote:

Hello everyone,

I am running into an error when I try to compute the cluster based permutation test in the frequency-time domain comparing baseline to when the stimulus is present. I?ve calculated the power for 300 milliseconds (1 millisecond is equal to one time point) before the onset of the stimulus and 300ms during the stimulus for a range of 10 frequencies. The error I?m running into is stated below as well as my code and I?ve attached an image of what the data looks like. I?ve tried following the tutorials and searching through the mailing list and can?t seem to find a solution.  Has anyone else run into this problem and found a solution or am I making an error somewhere in my analysis?


MY CODE:
    cfg = [];
    cfg.channel          = 'CZ';
    cfg.latency          = [0.1 0.4];
    cfg.method           = 'montecarlo';
    cfg.frequency        = 'all';
    cfg.statistic        = 'ft_statfun_actvsblT';
    cfg.correctm         = 'cluster';
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;

    ntrials = size(TFR_FFR.powspctrm,1);
    design  = zeros(2,2*ntrials);
    design(1,1:ntrials) = 1;
    design(1,ntrials+1:2*ntrials) = 2;
    design(2,1:ntrials) = [1:ntrials];
    design(2,ntrials+1:2*ntrials) = [1:ntrials];

    cfg.design   = design;
    cfg.ivar     = 1;
    cfg.uvar     = 2;

   TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR, TFR_FFRbaseline);


THE ERROR MESSAGE:
?Error using reshape
To RESHAPE the number of elements must not change.

Error in ft_statfun_actvsblT (line 115)
  timeavgdat=repmat(reshape(meanreshapeddat,(nchan*nfreq),nrepl),ntime,1);

Error in ft_statistics_montecarlo (line 267)
  [statobs, cfg] = statfun(cfg, dat, design);

Error in ft_freqstatistics (line 194)
    [stat, cfg] = statmethod(cfg, dat, design);

Error in TFRClusterBasedStats (line 54)
    TFR_FFR_stat =  ft_freqstatistics(cfg, TFR_FFR,
    TFR_FFRbaseline);?



Thank you,
David Prete
Ph.D. Candidate
McMaster Institute for Music and the Mind
Department Psychology, Neuroscience and Behaviour
McMaster University

<Screenshot (16).png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl><mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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------------------------------

Message: 2
Date: Thu, 7 Sep 2017 00:57:17 +0900
From: ???? <tokimoto at mejiro.ac.jp<mailto:tokimoto at mejiro.ac.jp>>
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: [FieldTrip] Cluster-based permutation tests for 3 conditions
Message-ID: <EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp<mailto:EF1907A8-7606-4B65-B3BB-CCEC15C62DA8 at mejiro.ac.jp>>
Content-Type: text/plain; charset="us-ascii"

Dear FieldTrip users,

I usually perform cluster-based permutation tests for my EEG analyses. The test is exact and useful, and I am deeply grateful for the developers. I understand permutation tests are a test between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the attached file. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.

******************************************
Shingo Tokimoto, Ph.D.
in Linguistics and Psychology
Department of Foreign Languages
Mejiro University
4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
161-8539, Japan
tokimoto at mejiro.ac.jp<mailto:tokimoto at mejiro.ac.jp>
******************************************

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------------------------------

Message: 3
Date: Wed, 6 Sep 2017 18:25:54 +0200
From: STEPHAN MORATTI <smoratti at psi.ucm.es<mailto:smoratti at psi.ucm.es>>
To: SARA RODRIGUEZ FREGENAL <srodri10 at ucm.es<mailto:srodri10 at ucm.es>>, FieldTrip discussion
                list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Learning agreementbuybr 8
Message-ID:
                <CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com<mailto:CAE20z9KE=FQERSC+-EPn=7cNrk53NpdWgh=gUrTg=64t5-MoxA at mail.gmail.com>>
Content-Type: text/plain; charset="utf-8"

C ck

El 5 sept. 2017 14:37, "SARA RODRIGUEZ FREGENAL" <srodri10 at ucm.es<mailto:srodri10 at ucm.es>> escribi?:

Buenos d?as Stephan,

Soy una de tus tuteladas del Erasmus en Glasgow. Tuve que cambiar unas
cosas en el learning agreement y me piden que me lo vuelvas a firmar.
?Ser?as tan amable de envi?rmelo firmado?

Gracias y perd?n por las molestias,
Sara
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------------------------------

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fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

End of fieldtrip Digest, Vol 82, Issue 8
****************************************

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From nugenta at mail.nih.gov  Thu Sep  7 03:02:10 2017
From: nugenta at mail.nih.gov (Nugent, Allison C. (NIH/NIMH) [E])
Date: Thu, 7 Sep 2017 01:02:10 +0000
Subject: [FieldTrip] 2nd MEG North America Meeting - Abstract Submission
	Reminder
Message-ID: <MWHPR09MB12162641EDE3ED43AEDC65DD80940@MWHPR09MB1216.namprd09.prod.outlook.com>

This is a reminder that we are currently accepting abstracts for the 2nd MEG-North America meeting, to be held in Bethesda, Maryland November 8th and 9th. Committee meetings will be held on November 8th, with the scientific session on November 9th.



There will be a poster session and oral sessions on November 9th.  We are attempting to secure funding to present several speaker honoraria for excellent abstracts submitted by early career investigators and trainees.  If you would like to be considered for this, please indicate your preference, along with your position, on your abstract submission.



Abstracts may be submitted any time up until Wednesday, September 13th at 11:59pm, directly to NIHMEGworkshop at gmail.com<mailto:NIHMEGworkshop at gmail.com> (note the deadline has been extended).



Please visit our website at https://megworkshop.nih.gov/MEGWorkshop/  - We have added additional information regarding the program!



Or register at: https://www.eventbrite.com/e/meg-north-america-2017-tickets-36315511673<https://www.eventbrite.com/e/meg-north-america-2017-tickets-36315511673?aff=utm_source%3Deb_email%26utm_medium%3Demail%26utm_campaign%3Dnew_event_email&utm_term=eventurl_text>



We hope to see you there!



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From alice.bollini at yahoo.com  Thu Sep  7 10:18:22 2017
From: alice.bollini at yahoo.com (Alice B)
Date: Thu, 7 Sep 2017 08:18:22 +0000 (UTC)
Subject: [FieldTrip] Source reconstruction issues
References: <283732377.7662318.1504772302305.ref@mail.yahoo.com>
Message-ID: <283732377.7662318.1504772302305@mail.yahoo.com>

Hello everyone,
I would like to use fieldtrip for extracting source activity from specific ROIs (using the eLoreta approach).
Here is my script, there are few things I am not sure in the pipeline (marked with numbers on the right)
% eLORETA cfg = []; cfg.method                          = 'eloreta';cfg.grid                                  = leadfield;cfg.headmodel                       = headmodel;cfg.eloreta.keepfilter              = 'yes';cfg.eloreta.normalize               = 'yes';cfg.eloreta.lambda                  = 0.05;                                      *(1)cfg.eloreta.projectnoise            = 'yes';eLO_source                          = ft_sourceanalysis(cfg,data);
% in the above line, "data" is the results of ft_timelockanalysis% with cfg.covariance = 'yes';                                                  *(2)
% then I put the source positions from the MNI template% used for the sourcemodel (http://www.fieldtriptoolbox.org/tutorial/sourcemodel#subject-specific_grids_that_are_equivalent_across_subjects_in_normalized_space)eLO_source.pos                      = template_grid.pos;iPOS                                        = eLO_source.pos;iPOS(eLO_source.inside==0,:)        = NaN; % only points inside gray matter
% Then I select ROIs (here only one for simplicity) to extract single-trial source activity:[v,I]       = min(pdist2(iPOS, ROIs_mni , 'euclidean'));
% And I multiply the spatial filter for the EEG data in each trialW            = eLO_source.avg.filter{I}; % filter at my ROI of interestfor tr = 1:size(data.trial,1)       % loop over trials         trials{tr} = W * squeeze(data.trial(tr,:,:));                            *(3)end
Is this approach correct?My main questions are:
*(1) Is there a way to select the best lambda parameter (e.g., selecting the one that best approximates the activity at the EEG channels level)?
*(2) I am confused about the role of the covariance, since it doesn't seem to be used when source activity is estimated using the set of spatial filters at the single trial
*(3) Is the "trials{tr} = W * squeeze(data.trial(tr,:,:)); " approach correct to get time-series of source activity in a ROI?

Best,Alice
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From da401 at kent.ac.uk  Thu Sep  7 13:53:20 2017
From: da401 at kent.ac.uk (D.Abdallah)
Date: Thu, 7 Sep 2017 11:53:20 +0000
Subject: [FieldTrip] Question about MVPA topographic map
Message-ID: <1504785200819.38370@kent.ac.uk>

Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.

I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING

%Reading the data

cfg = [];

cfg.dataset = filename1;

cfg.reref = 'yes';

cfg.channel = {'Cz', 'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'};

cfg.refchannel = 'Cz';

cfg.demean = 'yes';

data_eeg1 = ft_preprocessing(cfg);

%Segmenting data

cfg.trialdef.eventtype = '?';

Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;

cfg.trialdef.poststim = 0.6;

cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype = 'STATUS';

cfg.trialdef.eventvalue = [100];

stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];

FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];

VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers

stimulusTrigger = 100;

faceResponseTrigger = 1;

vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing

[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';

FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing

[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';

Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP

FaceTrials.reref = 'no';

FaceTrials.keeptrials = 'yes'; % classifiers operate on individual trials

FaceTrials.channel = {'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'}; % occipital channels only

FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP

VaseTrials.reref = 'no';

VaseTrials.keeptrials = 'yes'; % classifiers operate on individual trials

VaseTrials.channel = {'PO9', 'PO7', 'PO3', 'PO', 'PO4', 'PO8' 'PO10', 'O1', 'Oz', 'O2','O9', 'O10'}; % occipital channels only

VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA

cfg.layout = 'biosemi64.lay';

cfg.method = 'crossvalidate';

cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];

cfg.nfolds = 4;

cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy' 'binomial' 'contingency'};

stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);

stat.statistic.contingency

%Plot MVPA Results

stat.mymodel = stat.model{2}.primal;

cfg.parameter = 'mymodel';

cfg.xlim = [-0.1 0.6];

cfg.comments = '';

cfg.colorbar = 'yes';

cfg.interplimits= 'electrodes';

ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.

We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).

Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,

Diane Abdallah


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From Patrick.Rollo at uth.tmc.edu  Fri Sep  8 20:38:22 2017
From: Patrick.Rollo at uth.tmc.edu (Rollo, Patrick)
Date: Fri, 8 Sep 2017 18:38:22 +0000
Subject: [FieldTrip] Job Posting on FieldTrip message board
Message-ID: <6484964699b24059ba1dd00807892d06@uth.tmc.edu>

FieldTrip Moderators,

I have a job posting that I would like to make on this message board, our lab, Tandon Lab, has posted in the past. The advert is attached here. Please let me know if you have any questions,


Thank you,


Patrick Rollo
Research Assistant
Department of Neurosurgery
UTHealth McGovern Medical School at Houston
6431 Fannin St MSB G.550G Houston TX 77030
phone: 713-500-5475


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From lxykh0700073 at outlook.com  Sun Sep 10 03:53:24 2017
From: lxykh0700073 at outlook.com (Xinyi Li)
Date: Sun, 10 Sep 2017 01:53:24 +0000
Subject: [FieldTrip] mixed design permutation tests on time-frequency data?
Message-ID: <HK2PR06MB070552220B0902408B2D56C8816B0@HK2PR06MB0705.apcprd06.prod.outlook.com>

Hi all,


I'm wondering how to do a mixed design permutation test on time-frequency data, specifically, how to specify the design matrix.

I followed the instructions in this post https://mailman.science.ru.nl/pipermail/fieldtrip/2008-March/001500.html for design matrix, but got an error 'the design matrix variables should be constant within a block'.


Any suggestions?


Thanks!

Xinyi
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From Miguel.Granjaespiritosanto at nottingham.ac.uk  Mon Sep 11 11:19:13 2017
From: Miguel.Granjaespiritosanto at nottingham.ac.uk (Miguel Granja Espirito Santo)
Date: Mon, 11 Sep 2017 09:19:13 +0000
Subject: [FieldTrip] Any update on the group-level source MNE stats?
Message-ID: <AM2PR06MB086699834D86F136DC514BFE83680@AM2PR06MB0866.eurprd06.prod.outlook.com>

Hi fieldtripers,


I was wondering if there is any update on doing group level stats after conducting an MNE source analysis. I found several threads on the mailings list which I have successfully replicated, but I was wondering if there is any official FT approved way.


At the end of the MNE page it says that this is under development, so is anyone privy to what the status of documentation is?

Just asking because of supervisor enquiry for publication of our results.


Best,

Miguel
PhD Student
School of Psychology
University of Nottingham





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From Manuel.Bange at unimedizin-mainz.de  Mon Sep 11 14:32:57 2017
From: Manuel.Bange at unimedizin-mainz.de (Bange, Manuel)
Date: Mon, 11 Sep 2017 12:32:57 +0000
Subject: [FieldTrip] Time normalisation for trials of different lenghts
Message-ID: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>

Dear fieldtrip community,

My name is Manuel Bange and I am working in the Movement Disorder and Neurostimulation Lab in Mainz, Germany. Currently I am working on a project where we recorded EEG and EMG data combined with simultaneously recorded ground reaction forces during walking on a treadmill. I intend to use complete gait cycles as trials in order to, for example, calculate the inter-trial coherence. The issue I now face is that every cycle lasts a different amount of time.
In a first step I have separated the continuous data into trials that correspond to whole gait cycles. One cycle starts with the onset of the right foot and ends one sample before the following onset of the same foot. This results in a number of trials (around 18 per subject) of different lengths.
Following this, I performed a time-frequency analysis for each individual trial by


1.       selecting a specific trial

cfg.trials      = 2;                                                               % select a a trial, here trial 2

trial2 = ft_selectdata(cfg, data)



2.       performing time-frequency analysis

cfg          = [];

cfg.output   = 'pow';

cfg.method   = 'mtmconvol';

cfg.taper    = 'hanning';

cfg.foi      = [1:1:40];

cfg.t_ftimwin    = ones(length(cfg.foi),1).*1;

cfg.toi          = trial2.time{1,1}(1,125):0.01:trial2.time{1,1}(1,end-125-1);

cfg.keeptrials = 'yes'

data_tfa1     = ft_freqanalysis(cfg, trial2);
resulting in
  data_tfa1 =
  struct with fields:
label: {257×1 cell}
dimord: 'rpt_chan_freq_time'
freq: [1×40 double]
time: [1×172 double]                                                                    % differs for each trial
powspctrm: [1×257×40×172 double]
cumtapcnt: [1×40 double]
cfg: [1×1 struct]

Now my question is:
Is there a way to normalise this data over the time-axis, so that all trials have the same length? This is important to calculate an average time-frequency representation, or in another step, to calculate the inter-trial coherence. I have thought of normalising the time axis and interpolating the corresponding power-values.

Thanks and best regards,

Manuel Bange
M.Sc. Sports Science


Johannes-Gutenberg-University Hospital
Movement Disorders and Neurostimulation
Department of Neurology
Langenbeckstr. 1
55131 Mainz, Germany
www.unimedizin-mainz.de

Email: manuel.bange at unimedizin-mainz.de

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From juliacoopiza at gmail.com  Mon Sep 11 15:24:35 2017
From: juliacoopiza at gmail.com (Julia Coopi)
Date: Mon, 11 Sep 2017 07:24:35 -0600
Subject: [FieldTrip] Using PPC method
In-Reply-To: <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>
Message-ID: <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>

Dear Andreas,

Finally, I managed to get PPC result from fliedtrip, now I have a problem:
I am using mtmfft as method I wnat to have fine frequency resolution
atleast I wan to have a point for each 1 hz. I have used
 cfg.foi       =2:1:80;

But it did't work, my output has a frequncy vector like this:[ 5 10 15 20
... 80];

do you have any suggestion for better frequency resolution.
If any body has a suggestion, that woulb be great to share it.

Thanks,
Julia

On Mon, Sep 4, 2017 at 6:24 AM, Andreas Wutz <awutz at mit.edu> wrote:

> Dear Julia,
>
> I did not see your error message. Maybe, your lfp data structure is still
> in a continuous recording format without a trial definition?
>
> ------------------------------
> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
> on behalf of Julia Coopi [juliacoopiza at gmail.com]
> *Sent:* Sunday, September 03, 2017 11:14 AM
>
> *To:* FieldTrip discussion list
> *Subject:* Re: [FieldTrip] Using PPC method
>
> Dear Andreas,
>
> Thanks for your response, I am going through your suggestion. did you have
> any problem regarding the appending spikes and lfp. I got this error:
>
> Error using ft_appendspike (line 112)
> could not find the trial information in the continuous data
>
> thanks.
> Julia
>
> On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu> wrote:
>
>> Dear Tianyang,
>>
>> maybe it's a good idea to download the accompanying sample data from the
>> tutorial and look if you can recreate the shown data structure. Then look
>> closer into the values of the respective fields. That should give you a
>> better grasp on what is required there.
>>
>> I have not fully looked into the code but my feeling is that
>> spikeTrials.timestamp is not of any further use and is just carried from
>> the data structure before (which was not cut into trials and where the raw
>> timestamps were useful). The timing of spikes relative to the trial zero
>> point is fully described in the fields ".time", ".trial" and ".trialtime".
>> Best,
>> Andreas
>>
>>
>> *From:* fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]
>> on behalf of 马天阳 [tianyangma2013 at gmail.com]
>> *Sent:* Monday, August 28, 2017 5:31 PM
>> *To:* FieldTrip discussion list
>> *Subject:* Re: [FieldTrip] Using PPC method
>>
>> Dear Andreas,
>>
>> I still don't quite understand the tutorial.
>>
>> spikeTrials =
>>          label: {'sig002a_wf'  'sig003a_wf'}
>>      timestamp: {[1x83601 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32 <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
>>       waveform: {[1x32x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>           unit: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>            hdr: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>>         dimord: '{chan}_lead_time_spike'
>>            cfg: [1x1 struct <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
>>           time: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>          trial: {[1x83601 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
>>      trialtime: [600x2 double <http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]
>>
>> Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?
>>
>> I feel I am quite lost.
>>
>> Best,
>>
>> Tianyang
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From awutz at mit.edu  Mon Sep 11 15:57:02 2017
From: awutz at mit.edu (Andreas Wutz)
Date: Mon, 11 Sep 2017 13:57:02 +0000
Subject: [FieldTrip] Using PPC method
In-Reply-To: <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>
References: <CABSLKUdFJ7R_fAmf3LLZ702dy15SWPnTSM2oVaj2DSx2Pu8WsQ@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A44578310@OC11EXPO29.exchange.mit.edu>
 <CABSLKUcPezs+JoX0t=jEq8Ec4cHrx_hpu8-zmWKEy1X8bVTE9w@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457836D@OC11EXPO29.exchange.mit.edu>
 <CAA5UOPQWmTskxybkuuV60EoS3cQdgiFg=YxFgJxzQU9eQLvx1Q@mail.gmail.com>
 <E117808318BC9745BF72B56E5563172A4457860A@OC11EXPO29.exchange.mit.edu>,
 <CAA5UOPQeH-nxznVomn4tawPgXt_UQv-vDrvqW3Frhx79VJ-EsQ@mail.gmail.com>
Message-ID: <E117808318BC9745BF72B56E5563172A44579AF3@OC11EXPO29.exchange.mit.edu>

Dear Julia,

your frequency resolution depends on the time window you give to the FFT (cfg.timwin). Increasing that window will increase your freq resolution.


________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Julia Coopi [juliacoopiza at gmail.com]
Sent: Monday, September 11, 2017 9:24 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Finally, I managed to get PPC result from fliedtrip, now I have a problem:  I am using mtmfft as method I wnat to have fine frequency resolution atleast I wan to have a point for each 1 hz. I have used
 cfg.foi       =2:1:80;

But it did't work, my output has a frequncy vector like this:[ 5 10 15 20 ... 80];

do you have any suggestion for better frequency resolution.
If any body has a suggestion, that woulb be great to share it.

Thanks,
Julia

On Mon, Sep 4, 2017 at 6:24 AM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Julia,

I did not see your error message. Maybe, your lfp data structure is still in a continuous recording format without a trial definition?

________________________________
From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of Julia Coopi [juliacoopiza at gmail.com<mailto:juliacoopiza at gmail.com>]
Sent: Sunday, September 03, 2017 11:14 AM

To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

Thanks for your response, I am going through your suggestion. did you have any problem regarding the appending spikes and lfp. I got this error:

Error using ft_appendspike (line 112)
could not find the trial information in the continuous data

thanks.
Julia

On Mon, Aug 28, 2017 at 4:55 PM, Andreas Wutz <awutz at mit.edu<mailto:awutz at mit.edu>> wrote:
Dear Tianyang,

maybe it's a good idea to download the accompanying sample data from the tutorial and look if you can recreate the shown data structure. Then look closer into the values of the respective fields. That should give you a better grasp on what is required there.

I have not fully looked into the code but my feeling is that spikeTrials.timestamp is not of any further use and is just carried from the data structure before (which was not cut into trials and where the raw timestamps were useful). The timing of spikes relative to the trial zero point is fully described in the fields ".time", ".trial" and ".trialtime".
Best,
Andreas


From: fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl> [fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>] on behalf of 马天阳 [tianyangma2013 at gmail.com<mailto:tianyangma2013 at gmail.com>]
Sent: Monday, August 28, 2017 5:31 PM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] Using PPC method

Dear Andreas,

I still don't quite understand the tutorial.

spikeTrials =
         label: {'sig002a_wf'  'sig003a_wf'}
     timestamp: {[1x83601 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]  [1x61513 int32<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/int32.html>]}
      waveform: {[1x32x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x32x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
          unit: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
           hdr: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
        dimord: '{chan}_lead_time_spike'
           cfg: [1x1 struct<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/struct.html>]
          time: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
         trial: {[1x83601 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]  [1x61513 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]}
     trialtime: [600x2 double<http://www.mathworks.com/access/helpdesk/help/techdoc/ref/double.html>]

Like here, I don't know why for timestamp,time and trial, there are same number of values. If I have one unit, 60 trials and focus on -0.5 s to 0.5 s around stimulus event, I think trial means which trial of the 60 trials has spikes in this 1 s. The time means time point of spikes located in the 1 s around stimulus for each trial. Is it correct? But what about the timestamp? It means from the beginning of recording to the end and has nothing to do with trials?

I feel I am quite lost.

Best,

Tianyang

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From nasseroleslami at gmail.com  Mon Sep 11 19:09:52 2017
From: nasseroleslami at gmail.com (Bahman Nasseroleslami)
Date: Mon, 11 Sep 2017 18:09:52 +0100
Subject: [FieldTrip] Research Assistant in Position Neural Engineering
 Position - Trinity College Dublin, the University of Dublin, Dublin, Ireland
Message-ID: <CAGgfpUYU1EwV3zqdznbAg-LQeZmKR4iVR4P7sjs=fQELTGK+kQ@mail.gmail.com>

Dear all,

There is a research assistant position available in Academic Unit of
Neurology, Trinity College Dublin, the University of Dublin, Dublin,
Ireland.

---------------------------------------
Job ID : 032518
Post Title: Research Assistant in Neural Engineering
Post Status: 12 month contract, full-time
Research Group / Department / School Academic Unit of Neurology, School of
Medicine, Trinity College Dublin, the University of Dublin
Location: Trinity Biomedical Sciences Institute, Trinity College Dublin,
the University of Dublin, College Green, Dublin D02 R590, Ireland
Reports to: Professor Orla Hardiman/Dr Bahman Nasseroleslami
Salary: Research Assistant Level based on Irish Universities Association
(IUA) Guideline, Point 1: €21,459 per annum (or above commensurate with
experience).
Closing Date: 5pm on Wednesday 27th September 2017


Please note that Garda vetting will be sought in respect of individuals who
come under consideration for a post.

Post Summary

Applications are invited for a motivated and self-driven individual for the
position of research assistant with the Irish ALS Research Group, hosted in
the Trinity Biomedical Sciences Institute (TBSI)'s Academic Unit of
Neurology. The ideal candidate will have an undergraduate or master's
degree in engineering, bioengineering, mathematics, computational biology,
or a cognate area. Familiarity with and/or the ability to quickly acquire
skills in electrophysiological recordings and analysis (e.g. EEG/EMG),
would be highly desirable as would a knowledge of computer programming
(MATLAB).
---------------------------------------

The detailed job description file (PDF) and the application instructions
can be found online at http://jobs.tcd.ie.

It would be really appreciated if you could share this with those that may
be interested.

Sincerely
Bahman

––––––––––––
Bahman Nasseroleslami
Senior Research Fellow, IRC Postdoctoral Fellow
Academic Unit of Neurology, School of Medicine
Trinity College Dublin, the University of Dublin
Dublin 2, Ireland.

Room 5.43, Trinity Biomedical Sciences Institute
152-160 Pearse Street, Dublin D02 R590, Ireland.
nasserob at tcd.ie, nasseroleslami at gmail.com
www.tcd.ie

Trinity College Dublin, the University of Dublin is ranked 1st in Ireland
and in the top 100 world universities by the QS World University Rankings.
––––––––––––
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From michak at is.umk.pl  Mon Sep 11 23:16:54 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Mon, 11 Sep 2017 23:16:54 +0200
Subject: [FieldTrip] MRI low contrast
Message-ID: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>

Dear Fieldtrippers,

How to correct low contrast in MR image when using ft_mri_read and
ft_sourceplot to read and display MR image? (see attachment
mri00_lowctrst.png)

For comparison, same .nii opened with mricron software (
http://people.cas.sc.edu/rorden/mricron/install.html) displays with proper
contrast (see attachment mri00_hictrst.png)

Code:

ss = 'sub1';
f = ['../data/ind/', ss, '/mri/mri00.nii'];
mri00 = ft_read_mri(f)
ft_sourceplot([],mri00)


Thank you in advance !

Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics
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From a.stolk8 at gmail.com  Mon Sep 11 23:48:47 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Mon, 11 Sep 2017 14:48:47 -0700
Subject: [FieldTrip] MRI low contrast
In-Reply-To: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
References: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
Message-ID: <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>

Hi Michal,

There's a (undocumented) keyboard shortcut, shift+equal sign (numpad +) to
adjust the contrast scaling (use numpad - for the opposite direction).

Perhaps ft_sourceplot should additionally require the same cfg.lim option
that for instance ft_determine_coordsys uses. Will propose in a PR.

Best,
Arjen




On Mon, Sep 11, 2017 at 2:16 PM, Michał Komorowski <michak at is.umk.pl> wrote:

> Dear Fieldtrippers,
>
> How to correct low contrast in MR image when using ft_mri_read and
> ft_sourceplot to read and display MR image? (see attachment
> mri00_lowctrst.png)
>
> For comparison, same .nii opened with mricron software (
> http://people.cas.sc.edu/rorden/mricron/install.html) displays with
> proper contrast (see attachment mri00_hictrst.png)
>
> Code:
>
> ss = 'sub1';
> f = ['../data/ind/', ss, '/mri/mri00.nii'];
> mri00 = ft_read_mri(f)
> ft_sourceplot([],mri00)
>
>
> Thank you in advance !
>
> Michał Komorowski, MSc
> Nicolaus Copernicus University in Toruń
> Faculty of Physics, Astronomy and Informatics
> Department of Informatics
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From anne.urai at gmail.com  Tue Sep 12 09:01:03 2017
From: anne.urai at gmail.com (Anne Urai)
Date: Tue, 12 Sep 2017 09:01:03 +0200
Subject: [FieldTrip] BIDS data format survey
Message-ID: <CAFGbMjQ9T+yXuHSqW0AZ3vHQrX29pzuMS9OoEEp+hbT9Pu7Vzw@mail.gmail.com>

Dear FieldTrippers,

Recently, a number of people have been working on developing a common data
standard for MEG called BIDS (Brain Imaging Data Structure).

They are now requesting community feedback, so please have a look at the
brief survey here and help them out: https://t.co/BjAFmR7yhN

*Magnetoencephalography (MEG) studies produce enormous amounts of data that
need to be stored, organized and analyzed. However, naming conventions and
metadata are often incomplete or inexistent, which is an impediment to the
transfer of scientific data and knowledge, the reproducibility of research
results, and the curation of large data repositories with entries from
heterogenous origins. *


*Building on recent efforts from the MRI community, MEG-BIDS is a
proposition to standardize the arrangement of data structures in
MEG. Please refer to the MEG-BIDS manuscript and to the MEG-BIDS detailed
specifications for all details concerning the proposed structure:- MEG-BIDS
Manuscript:
<https://www.google.com/url?q=http://www.biorxiv.org/content/early/2017/08/08/172684&sa=D&ust=1505157728861000&usg=AFQjCNFOvjNkNYmsyA0_EBvZ0KyQ9n1mvA>http://www.biorxiv.org/content/early/2017/08/08/172684
<http://www.biorxiv.org/content/early/2017/08/08/172684>*
*- MEG-BIDS Specifications:
<https://www.google.com/url?q=http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf&sa=D&ust=1505157728861000&usg=AFQjCNHZY8Z-YO0UQu61iEnkxMFq7DQZUQ>http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf
<http://www.biorxiv.org/content/biorxiv/suppl/2017/08/08/172684.DC1/172684-1.pdf>*

*We wish to survey the MEG community on its present needs with respect to
data management, and design the MEG-BIDS standard to best respond to these
presently unmet needs.*

*Your participation is truly appreciated.*

... <bloop://bloop_expand>

—
Anne E. Urai, MSc
PhD student | Institut für Neurophysiologie und Pathophysiologie
Universitätsklinikum Hamburg-Eppendorf | Martinistrasse 52, 20246 |
Hamburg, Germany
www.anneurai.net / @AnneEUrai
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From michak at is.umk.pl  Tue Sep 12 09:56:58 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Tue, 12 Sep 2017 09:56:58 +0200
Subject: [FieldTrip] MRI low contrast
In-Reply-To: <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>
References: <CAEJSOqjmwNs4+8qTT+XTKsuy9U87U8s-34jOuYOa4mN-C=Msvg@mail.gmail.com>
 <CAAiwptRvFKYkfyR9WLBxT63WkF7KhQq+TJD8Sunah5_QX0Pryg@mail.gmail.com>
Message-ID: <CAEJSOqhuxSn73P1fuj9AmRXNXKR=h6T7q9zLfuK0T7abv+CLkw@mail.gmail.com>

Yaay !

+ and - works  ! :D


For determining coordsys one should type:

% clim adjusts contrast (default [0 1], the lower the brighter)
[dataout] = ft_determine_coordsys(mri00, 'clim', [0 0.25])


Thank you very much !

Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics

2017-09-11 23:48 GMT+02:00 Arjen Stolk <a.stolk8 at gmail.com>:

> Hi Michal,
>
> There's a (undocumented) keyboard shortcut, shift+equal sign (numpad +) to
> adjust the contrast scaling (use numpad - for the opposite direction).
>
> Perhaps ft_sourceplot should additionally require the same cfg.lim option
> that for instance ft_determine_coordsys uses. Will propose in a PR.
>
> Best,
> Arjen
>
>
>
>
> On Mon, Sep 11, 2017 at 2:16 PM, Michał Komorowski <michak at is.umk.pl>
> wrote:
>
>> Dear Fieldtrippers,
>>
>> How to correct low contrast in MR image when using ft_mri_read and
>> ft_sourceplot to read and display MR image? (see attachment
>> mri00_lowctrst.png)
>>
>> For comparison, same .nii opened with mricron software (
>> http://people.cas.sc.edu/rorden/mricron/install.html) displays with
>> proper contrast (see attachment mri00_hictrst.png)
>>
>> Code:
>>
>> ss = 'sub1';
>> f = ['../data/ind/', ss, '/mri/mri00.nii'];
>> mri00 = ft_read_mri(f)
>> ft_sourceplot([],mri00)
>>
>>
>> Thank you in advance !
>>
>> Michał Komorowski, MSc
>> Nicolaus Copernicus University in Toruń
>> Faculty of Physics, Astronomy and Informatics
>> Department of Informatics
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From behinger at uos.de  Tue Sep 12 20:14:22 2017
From: behinger at uos.de (Benedikt Ehinger)
Date: Tue, 12 Sep 2017 20:14:22 +0200
Subject: [FieldTrip] Time normalisation for trials of different lenghts
In-Reply-To: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>
References: <39A4BCA62730D84A95C53BCFC661677C01FEA9BD@mbx-02.it.klinik.uni-mainz.de>
Message-ID: <1311033b-5017-443e-9cd9-0f4e4486f112@uos.de>

Dear Manuel,

first off, I do not know if or how you can do this in fieldtrip. But in
eeglab you can do something they call "timewarping". One calculates a
time-frequency (TF) decomposition for each trial and then
warps/interpolates the TF so that some given events align. This is very
similar (identical?) to what you describe and you might find more
information either in the papers or the eeglab implementation. The
method has been described in Gwin 2010 [1] and we also used it in on of
our own studies [2].

Whether you can do the same also for phase (=> coherence) I don't know.

I hope that helps in your analysis.
Best, Benedikt

[1] https://www.ncbi.nlm.nih.gov/pubmed/20410364
[2] https://www.ncbi.nlm.nih.gov/pubmed/24616681


Am 11.09.2017 um 14:32 schrieb Bange, Manuel:
> Dear fieldtrip community,
> 
>  
> 
> My name is Manuel Bange and I am working in the Movement Disorder and
> Neurostimulation Lab in Mainz, Germany. Currently I am working on a
> project where we recorded EEG and EMG data combined with simultaneously
> recorded ground reaction forces during walking on a treadmill. I intend
> to use complete gait cycles as trials in order to, for example,
> calculate the inter-trial coherence. The issue I now face is that every
> cycle lasts a different amount of time.
> 
> In a first step I have separated the continuous data into trials that
> correspond to whole gait cycles. One cycle starts with the onset of the
> right foot and ends one sample before the following onset of the same
> foot. This results in a number of trials (around 18 per subject) of
> different lengths.
> 
> Following this, I performed a time-frequency analysis for each
> individual trial by
> 
>  
> 
> 1.       selecting a specific trial
> 
> /cfg.trials      = 2;
>                                                               % select a
> a trial, here trial 2/
> 
> /trial2 = ft_selectdata(cfg, data)/
> 
> / /
> 
> 2.       performing time-frequency analysis
> 
> /cfg          = [];/
> 
> /cfg.output   = 'pow';/
> 
> /cfg.method   = 'mtmconvol';/
> 
> /cfg.taper    = 'hanning';/
> 
> /cfg.foi      = [1:1:40];/
> 
> /cfg.t_ftimwin    = ones(length(cfg.foi),1).*1;/
> 
> /cfg.toi          =
> trial2.time{1,1}(1,125):0.01:trial2.time{1,1}(1,end-125-1);            /
> 
> /cfg.keeptrials = 'yes'/
> 
> /data_tfa1     = ft_freqanalysis(cfg, trial2);/
> 
> /resulting in/
> 
> /  data_tfa1 = /
> 
> /  struct with fields:/
> 
> /label: {257×1 cell}/
> 
> /dimord: 'rpt_chan_freq_time'/
> 
> /freq: [1×40 double]/
> 
> /time: [1×172
> double]                                                                   
> % differs for each trial/
> 
> /powspctrm: [1×257×40×172 double]/
> 
> /cumtapcnt: [1×40 double]/
> 
> /cfg: [1×1 struct]/
> 
>  
> 
> Now my question is:
> 
> Is there a way to normalise this data over the time-axis, so that all
> trials have the same length? This is important to calculate an average
> time-frequency representation, or in another step, to calculate the
> inter-trial coherence. I have thought of normalising the time axis and
> interpolating the corresponding power-values.
> 
>  
> 
> Thanks and best regards,
> 
>  
> 
> Manuel Bange
> 
> M.Sc. Sports Science
> 
>  
> 
>  
> 
> Johannes-Gutenberg-University Hospital
> 
> Movement Disorders and Neurostimulation
> 
> Department of Neurology
> 
> Langenbeckstr. 1
> 
> 55131 Mainz, Germany
> 
> www.unimedizin-mainz.de
> 
>  
> 
> Email: manuel.bange at unimedizin-mainz.de
> 
>  
> 
> 
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 


From psc.dav at gmail.com  Tue Sep 12 20:47:15 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Tue, 12 Sep 2017 20:47:15 +0200
Subject: [FieldTrip] inverse imaging
Message-ID: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>

Dear fieldtrip experts,

I was wondering if anyone has experience with extracting single trials
estimates of source activity (using MNE or Loreta-based approaches) from
regions of interest, and what would be the best procedure…


Thanks in advance
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From julian.keil at gmail.com  Wed Sep 13 12:22:01 2017
From: julian.keil at gmail.com (Julian Keil)
Date: Wed, 13 Sep 2017 12:22:01 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
Message-ID: <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>

Hi David,

do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors <http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors>
In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.

Good luck,

Julian


> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
> 
> Dear fieldtrip experts,
> 
> I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…
> 
> 
> 
> Thanks in advance
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From psc.dav at gmail.com  Wed Sep 13 13:22:46 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Wed, 13 Sep 2017 13:22:46 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
Message-ID: <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>

Thaks Julian,
that is the approach I was using, with eLoreta.
I am not sure about two steps,though.
One is the estimate and use of the signal covariance to input for single-trial
activity in source space.
The other is the choice of the optimal lambda.

If you have some advice, that wold be very helpful.

Thanks,
David

2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com>:

> Hi David,
>
> do you want to obtain single-trial activity in source space? In that case,
> have you looked at the „virtual sensors“-tutorial? http://www.
> fieldtriptoolbox.org/tutorial/shared/virtual_sensors
> In the tutorial, LCMV is used for the source analysis, but it should also
> work with sloreta, as the output-structure of the source-analysis is
> identical. I’m not sure about MNE though.
>
> Good luck,
>
> Julian
>
>
> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
>
> Dear fieldtrip experts,
>
> I was wondering if anyone has experience with extracting single trials
> estimates of source activity (using MNE or Loreta-based approaches) from
> regions of interest, and what would be the best procedure…
>
>
> Thanks in advance
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From julian.keil at gmail.com  Wed Sep 13 13:41:22 2017
From: julian.keil at gmail.com (Julian Keil)
Date: Wed, 13 Sep 2017 13:41:22 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
 <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
Message-ID: <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>

Hi David,

regarding the lambda, I think there are different ideas floating around the fieldtrip discussion-list. I suggest searching for the term „lambda“ to get a rough idea. Personally, for our EEG-data I usually use 10%.

What is your question exactly regarding the covariance as input?

Cheers,

Julian

> Am 13.09.2017 um 13:22 schrieb David Pascucci <psc.dav at gmail.com>:
> 
> Thaks Julian,
> that is the approach I was using, with eLoreta.
> I am not sure about two steps,though.
> One is the estimate and use of the signal covariance to input for single-trial activity in source space.
> The other is the choice of the optimal lambda.
> 
> If you have some advice, that wold be very helpful.
> 
> Thanks,
> David
> 
> 2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com <mailto:julian.keil at gmail.com>>:
> Hi David,
> 
> do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors <http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors>
> In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.
> 
> Good luck,
> 
> Julian
> 
> 
>> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com <mailto:psc.dav at gmail.com>>:
>> 
>> Dear fieldtrip experts,
>> 
>> I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…
>> 
>> 
>> 
>> Thanks in advance
>> 
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip <https://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl <mailto:fieldtrip at donders.ru.nl>
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip <https://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
> 
> 
> 
> -- 
> --------------------- 
> David Pascucci
> 
> Postdoctoral Fellow
> University of Fribourg
> Department of Psychology
> Rue de Faucigny 2
> 1700 Fribourg
> Switzerland
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From evelyn.muschter at unitn.it  Wed Sep 13 13:50:06 2017
From: evelyn.muschter at unitn.it (Evelyn Muschter)
Date: Wed, 13 Sep 2017 13:50:06 +0200
Subject: [FieldTrip] Any update on the group-level source MNE stats?/
	Vol 82, Issue 13
In-Reply-To: <mailman.9.1505124001.16132.fieldtrip@science.ru.nl>
References: <mailman.9.1505124001.16132.fieldtrip@science.ru.nl>
Message-ID: <88486D75-ED4E-40E0-8EE1-95A1A21E60CB@unitn.it>

Hi Miguel and all, 

I have been wondering this myself! 
I have also followed various tutorial snippets here, but I am stuck with how to properly do group level stats. 
Any suggestions and input would be greatly appreciated! 

Best, 

Evelyn 

> On Sep 11, 2017, at 12:00 PM, fieldtrip-request at science.ru.nl wrote:
> 
> Send fieldtrip mailing list submissions to
> 	fieldtrip at science.ru.nl
> 
> To subscribe or unsubscribe via the World Wide Web, visit
> 	https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> or, via email, send a message with subject or body 'help' to
> 	fieldtrip-request at science.ru.nl
> 
> You can reach the person managing the list at
> 	fieldtrip-owner at science.ru.nl
> 
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of fieldtrip digest..."
> 
> 
> Today's Topics:
> 
>   1. Any update on the group-level source MNE stats?
>      (Miguel Granja Espirito Santo)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Mon, 11 Sep 2017 09:19:13 +0000
> From: Miguel Granja Espirito Santo
> 	<Miguel.Granjaespiritosanto at nottingham.ac.uk>
> To: "fieldtrip at science.ru.nl" <fieldtrip at science.ru.nl>
> Subject: [FieldTrip] Any update on the group-level source MNE stats?
> Message-ID:
> 	<AM2PR06MB086699834D86F136DC514BFE83680 at AM2PR06MB0866.eurprd06.prod.outlook.com>
> 	
> Content-Type: text/plain; charset="iso-8859-1"
> 
> Hi fieldtripers,
> 
> 
> I was wondering if there is any update on doing group level stats after conducting an MNE source analysis. I found several threads on the mailings list which I have successfully replicated, but I was wondering if there is any official FT approved way.
> 
> 
> At the end of the MNE page it says that this is under development, so is anyone privy to what the status of documentation is?
> 
> Just asking because of supervisor enquiry for publication of our results.
> 
> 
> Best,
> 
> Miguel
> PhD Student
> School of Psychology
> University of Nottingham
> 
> 
> 
> 
> 
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> 
> End of fieldtrip Digest, Vol 82, Issue 13
> *****************************************




From psc.dav at gmail.com  Wed Sep 13 15:33:57 2017
From: psc.dav at gmail.com (David Pascucci)
Date: Wed, 13 Sep 2017 15:33:57 +0200
Subject: [FieldTrip] inverse imaging
In-Reply-To: <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>
References: <CAK-BAC-Yebzuxbo-k8kNT9JLkg6v25GVW-NZuLSSwmPvsR5jOA@mail.gmail.com>
 <D2138091-E3EC-4B71-B356-B13246FBF0AB@gmail.com>
 <CAK-BAC-5e+=MBHy5UVGb0VDntLa83CQFLrJJ-K+zgLr11Hodcg@mail.gmail.com>
 <DFA77F9C-D076-420F-B850-DF2BB0CE9FB4@gmail.com>
Message-ID: <CAK-BAC872zH8R=v4QBrfj0Draw2aygYsdqkVf+esZjDSY+s62w@mail.gmail.com>

Thanks again Julian,
About the covariance, I am not sure about its usage in the reconstruction
of single-trials activity.
According to the example, this is done by multiplying the spatial filters
with the EEG data.

Whereas the covariance (Cf) is used to compute the avg.pow and ori in
ft_eloreta (line 160-168)

% get the power
dip.pow = zeros(size(dip.pos,1),1);
dip.ori = cell(size(dip.pos,1),1);
for i=1:size(dip.pos,1)
  csd        = dip.filter{i}**Cf**dip.filter{i}';
  [u,s,vv]    = svd(real(csd));
  dip.pow(i) = s(1);
  dip.ori{i} = u(:,1);
end

It does not seem to be considered when creating and storing spatial filters
(later used for single-trials reconstruction)
(line 152-158, ft_eloreta)

% use existing filters, or compute them
if ~isfield(dip, 'filter')
  filt = mkfilt_eloreta_v2(leadfield, lambda);
  for i=1:size(dip.pos,1)
    dip.filter{i,1} = squeeze(filt(:,i,:))';
  end
end

My question is, am I getting this wrong?
and if not, should I ignore the covariance estimation in the case of
single-trials reconstructed via filters*data?


Cheers,
David


2017-09-13 13:41 GMT+02:00 Julian Keil <julian.keil at gmail.com>:

> Hi David,
>
> regarding the lambda, I think there are different ideas floating around
> the fieldtrip discussion-list. I suggest searching for the term „lambda“ to
> get a rough idea. Personally, for our EEG-data I usually use 10%.
>
> What is your question exactly regarding the covariance as input?
>
> Cheers,
>
> Julian
>
> Am 13.09.2017 um 13:22 schrieb David Pascucci <psc.dav at gmail.com>:
>
> Thaks Julian,
> that is the approach I was using, with eLoreta.
> I am not sure about two steps,though.
> One is the estimate and use of the signal covariance to input for single-trial
> activity in source space.
> The other is the choice of the optimal lambda.
>
> If you have some advice, that wold be very helpful.
>
> Thanks,
> David
>
> 2017-09-13 12:22 GMT+02:00 Julian Keil <julian.keil at gmail.com>:
>
>> Hi David,
>>
>> do you want to obtain single-trial activity in source space? In that
>> case, have you looked at the „virtual sensors“-tutorial? http://www.
>> fieldtriptoolbox.org/tutorial/shared/virtual_sensors
>> In the tutorial, LCMV is used for the source analysis, but it should also
>> work with sloreta, as the output-structure of the source-analysis is
>> identical. I’m not sure about MNE though.
>>
>> Good luck,
>>
>> Julian
>>
>>
>> Am 12.09.2017 um 20:47 schrieb David Pascucci <psc.dav at gmail.com>:
>>
>> Dear fieldtrip experts,
>>
>> I was wondering if anyone has experience with extracting single trials
>> estimates of source activity (using MNE or Loreta-based approaches) from
>> regions of interest, and what would be the best procedure…
>>
>>
>> Thanks in advance
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
>
> --
> ---------------------
> David Pascucci
>
> Postdoctoral Fellow
> University of Fribourg
> Department of Psychology
> Rue de Faucigny 2
> 1700 Fribourg
> Switzerland
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
---------------------
David Pascucci

Postdoctoral Fellow
University of Fribourg
Department of Psychology
Rue de Faucigny 2
1700 Fribourg
Switzerland
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From Adeen.Flinker at nyumc.org  Wed Sep 13 20:23:40 2017
From: Adeen.Flinker at nyumc.org (Flinker, Adeen)
Date: Wed, 13 Sep 2017 18:23:40 +0000
Subject: [FieldTrip] ECoG postdoc position
Message-ID: <99026305-0472-4915-871A-C55374055912@nyumc.org>

NYU School of Medicine is looking for candidates for two post-doctoral positions in Human Electrocortigoraphy (ECoG) research. Both positions will be under the supervision of Dr. Adeen Flinker, investigating speech processing and cortical network dynamics.

The research will be conducted at NYU Comprehensive Epilepsy Center working with a population of surgical patients undergoing treatment for refractory epilepsy. Research paradigms will be conducted in close collaboration with the clinical neurology team. The candidate will conduct neurophysiological research in patients implanted with intracranial electrodes (surface, depth, laminar, Utah arrays) and in intraoperative patients undergoing acute recording, stimulation or cooling.

The ideal applicant must have a Ph.D. in Neuroscience, Psychology, Biomedical Engineering or a related field. Proficiency in oral and written English is mandatory. A solid background in programming, statistics and scientific writing is required. The candidate is expected to be autonomous and to have a track-record of peer-reviewed publication. Previous experience with human electrophysiology or machine learning will be an asset.

One postdoctoral position is funded by a MURI grant investigating event segmentation and episodic memory. The candidate will have an opportunity to work closely with collaborators in Princeton (Dr. Hasson, Dr. Norman), Harvard (Dr. Gershman), UC Davis (Dr. Ranganath) and Washington University (Dr. Zacks).

Interested individuals should send an email to adeen.flinker at nyumc.org, including a cover letter describing research experience and qualifications, academic CV, and contact information of referees.


Adeen Flinker, PhD
Assistant Professor
Department of Neurology
NYU School of Medicine
145 East 32nd Street
New York, NY 10016
646-754-2228



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From C.Mazzetti at donders.ru.nl  Thu Sep 14 13:28:45 2017
From: C.Mazzetti at donders.ru.nl (Mazzetti, C. (Cecilia))
Date: Thu, 14 Sep 2017 11:28:45 +0000
Subject: [FieldTrip] ICA warning messages
Message-ID: <389DA1293690C94C93E3A53201F6C91E569EB8E6@exprd01.hosting.ru.nl>

Hi Evryone,
I was wondering why do i get this type of warnings when running ICA on my data.
this is the script I am using:


cfg.resamplefs = 300;
    cfg.detrend    = 'no';
    datads = ft_resampledata(cfg, data_clean);

    cfg=[];
    cfg.bpfilter='yes'; % bandpass , use low freqs for  alpha compponents
    cfg.bpfreq = [0.5 30];
    datatmp= ft_preprocessing(cfg, datads)


    cfg.method = 'runica';
    cfg.runica.maxsteps =30;
    comp_filt = ft_componentanalysis(cfg, datatmp);

    clear datads


    cfg           = [];
    cfg.unmixing  = comp_filt.unmixing;
    cfg.topolabel = comp_filt.topolabel;
    comp_origin = ft_componentanalysis(cfg, data_clean);

    clear comp

    cfg          = [];
      cfg.viewmode = 'component';
    cfg.layout   = 'CTF275.lay';
    ft_databrowser(cfg, comp_origin)


    cfg           = [];
    cfg.component = input('bad comps = ');
    meg_ica    = ft_rejectcomponent(cfg, comp_origin,data_clean);


later on after having selected the bad components i get these messages :

Warning: unexpected channel unit "unknown" in channel 158  (i get this for all the channels but i copy-pasted just one of them for obv reasons)
Warning: copying input chantype to montage
Warning: copying input chanunit to montage

Thanks in advance for your hints!

Cecilia

Cecilia Mazzetti - Ph.D. Candidate
Donders Centre for Cognitive Neuroimaging, room 1.170
Kapittelweg 29 | 6525 EN Nijmegen

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From nirofir2 at gmail.com  Thu Sep 14 14:10:46 2017
From: nirofir2 at gmail.com (Nir Ofir)
Date: Thu, 14 Sep 2017 15:10:46 +0300
Subject: [FieldTrip] Using ft_redefinetrial with minlength and
	begsample/endsample option
Message-ID: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>

Hi fieldtrip users,

I have a data structure containing MEG trials which are aligned to stimulus
onset. I now want to realign them to the target onset, as well as removing
trials which are too short. I thought the easiest way to do this would be
to use ft_redefinetrial in the following way:

offset = dat.trialinfo(:,5); % this column contains the duration of the
stimulus-target intervel
cfg = [];
cfg.minlength = -dat.time{1}(1)+cfgx.pretarget+0.5; % prestim defined by
dat sructure + 0.5 s ERF + cfgx.pretarget
cfg.begsample = round((offset - cfgx.pretarget)*1000);
cfg.endample = round(offset*1000);
dat = ft_redefinetrial(cfg, dat);

When I run this, I get the following error:

Index exceeds matrix dimensions.

Error in ft_redefinetrial (line 209)
    data.trial{i} = data.trial{i}(:, begsample(i):endsample(i));

So I looked into ft_redefinetrials a bit, and it seems like when minlength
is defined, the trials themselves are removed, but the begample/endsample
vector are not cut to contain only the relevant trials. For now I moved to
a 2-step solution (first removing trials, than realigning), but it seems
like this could have a relatively simple fix. Suggestions?

Thanks!
Nir Ofir
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From v.piai.research at gmail.com  Fri Sep 15 12:45:01 2017
From: v.piai.research at gmail.com (Vitoria Piai)
Date: Fri, 15 Sep 2017 12:45:01 +0200
Subject: [FieldTrip] Postdoc position: Magnetoencephalography and
 Tractography applied to Language in Neurological Populations
Message-ID: <CADAXq1RZuTfHM=9iQjWAU7HUSdM_WpnbNZ6OcVuXFEZ8_sXWTg@mail.gmail.com>

*3-year postdoctoral position on the topic of magnetoencephalography and
tractography applied to language in neurological populations*


We are looking for a postdoctoral candidate with demonstrable experience in
analysis of structural imaging and tractography to strengthen our research
group. Our group aims at integrating brain measures with high temporal
resolution, obtained using magnetoencephalography, with measures of
structural connectivity to better understand language function in healthy
and neurological populations. Ongoing projects include studying chronic
stroke and brain tumour patients.


More information on
https://www.languageininteraction.nl/jobs/postdoc-position-388.html
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From jan.schoffelen at donders.ru.nl  Fri Sep 15 15:46:58 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Fri, 15 Sep 2017 13:46:58 +0000
Subject: [FieldTrip] Fwd: Question for Fieldtrip
References: <4b2644a5.aff0.15e8581f6ac.Coremail.zhangwenjia2732@126.com>
Message-ID: <420905E5-98A4-4C9C-96C4-1C8A28325786@donders.ru.nl>



Begin forwarded message:

From: 张文嘉 <zhangwenjia2732 at 126.com<mailto:zhangwenjia2732 at 126.com>>
Subject: Question for Fieldtrip
Date: 15 September 2017 at 14:27:20 GMT+2
To: <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>>



Dear Fieldtrip expert,

I am Wenjia from NYU Shanghai. I am doing timefrequency analysis with frildtrip.
I have a question that I cannot solve. And, I am wondering whether you could help me.

Specifically, I want to import the EGI data that has been preprocessed (after segmentation but no average) into fieldtrip, and further do timefrequency analysis.
I donot know how to do this.

I have found a script like following:
    cfg = [];
    cfg.triggertype = 'Stimulus';
    cfg.prestim = 1.0; %1.0s before the onset
    cfg.poststim = 2.0;  %2.0s after the onset
    cfg.inputfile = sprintf('s02_32_tf');
    cfg.triggercode = 'S 32';
    data_32 = read_analyzer_data(cfg);
"s02_32_tf" is the name that I exported from EGI system, then included 3 files: asc, vhdr and vmrk.
However an error poped up: Undefined function 'read_analyzer_data' for input arguments of type 'struct'.
Any advice are appreciated. Thank you very much.

--
Wenjia
NYU Shanghai













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From m.chait at ucl.ac.uk  Mon Sep 18 13:01:41 2017
From: m.chait at ucl.ac.uk (Chait, Maria)
Date: Mon, 18 Sep 2017 11:01:41 +0000
Subject: [FieldTrip] Research Assistant Position at the UCL Ear Institute
Message-ID: <AM3PR01MB109105F3944F78ECA01E0AB4A8630@AM3PR01MB1091.eurprd01.prod.exchangelabs.com>

I would appreciate your help in forwarding the advert below to relevant members of your department.



We are seeking to appoint an enthusiastic and motivated Research Assistant and Laboratory Manager to provide essential support for ongoing research aimed at developing transformative treatments for deafness and hearing problems. The post is funded for 12 months (with a possibility of extension for up to 5 years). The post will involve collection and analysis of audiometry as well as behavioral (psychophysics), eye tracking and EEG data in humans. The postholder will also be expected to contribute to the induction and direction of other research staff and students.  The UCL Ear Institute, located in the heart of London,  provides state-of-the-art research facilities across a wide range of disciplines and is one of the foremost centres for hearing, speech and language-related research within Europe.



Applicants should hold a 1st class, or upper 2nd (or equivalent) BSc or MSc degree in an engineering or Neuroscience-related subject. Previous experience with neuroscience research, functional brain imaging and/or acoustics is desirable.

More information and a link to the application site are in the following link: http://www.jobs.ac.uk/job/BEH401/research-assistant-in-auditory-neuroscience



Closing Date: 15 October 2017


Maria Chait PhD
m.chait at ucl.ac.uk<mailto:m.chait at ucl.ac.uk>
Professor in Auditory Cognitive Neuroscience
Lab site: http://www.ucl.ac.uk/ear/research/chaitlab/
UCL Ear Institute
332 Gray's Inn Road
London WC1X 8EE
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From hweeling.lee at gmail.com  Mon Sep 18 13:57:51 2017
From: hweeling.lee at gmail.com (Hwee Ling Lee)
Date: Mon, 18 Sep 2017 13:57:51 +0200
Subject: [FieldTrip] Normalizing log-transformed EEG power
Message-ID: <CABG7z0Sqe5vdMU8X_Kjp7aQVRn-y-rRGxevpKzHEnpZLs00Grw@mail.gmail.com>

Dear all,


I have a question regarding how to compute z-scores for log-transformed EEG
power across all events separately for each electrode and frequency. I have
searched everywhere on how to implement this in the fieldrip environment,
however, I will be very grateful if someone can help me out on this.

Thanks!

Cheers,
Hweeling
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From lxykh0700073 at outlook.com  Tue Sep 19 03:51:59 2017
From: lxykh0700073 at outlook.com (Xinyi Li)
Date: Tue, 19 Sep 2017 01:51:59 +0000
Subject: [FieldTrip] simple main effects and permutation
Message-ID: <HK2PR06MB07057B48729B2D6F77CC0A0581600@HK2PR06MB0705.apcprd06.prod.outlook.com>

Hi all,


I have a mixed design and my hypothesis is about simple main effects. So for example, I have two groups of people, and each person experience the same 2x2 factorial design with conditions A1, A2, B1, B2. And my hypotheses are something like the simple main effect of A within group 1 and condition B1.


My questions:

1) Can I just subset the data to include only the group and condition I want (B1 & group 1) and do a t-test between condition A1 & A2 after subsetting? If I understand correctly, this approach will bias the standard error of the estimates? But I'm not sure if this matters in a permutation framework, and I also don't know if it's a common practice to do this in EEG analysis?

2) Alternatively, I can run a full mixed ANOVA model and then the simple main effect in R to get the test statistics I want. But for this approach I'm not sure how I should perform the permutation since fieldtrip doesn't support a mixed design. Should I only permute A1 & A2 within condition B1 and group 1? Or should I permute everything within both factors A & B? And what about the group labels?


Any suggestions? Thanks!


Xinyi
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From da401 at kent.ac.uk  Tue Sep 19 06:25:36 2017
From: da401 at kent.ac.uk (D.Abdallah)
Date: Tue, 19 Sep 2017 04:25:36 +0000
Subject: [FieldTrip] Question about MVPA topographic map
In-Reply-To: <1504785200819.38370@kent.ac.uk>
References: <1504785200819.38370@kent.ac.uk>
Message-ID: <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>


Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.

I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING

%Reading the data

cfg = [];

cfg.dataset = filename1;

cfg.reref ='yes';

cfg.channel = {'Cz','PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};

cfg.refchannel ='Cz';

cfg.demean ='yes';

data_eeg1 = ft_preprocessing(cfg);

%Segmenting data

cfg.trialdef.eventtype ='?';

Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;

cfg.trialdef.poststim = 0.6;

cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype ='STATUS';

cfg.trialdef.eventvalue = [100];

stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];

FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];

VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers

stimulusTrigger = 100;

faceResponseTrigger = 1;

vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing

[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';

FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing

[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);

hdr = ft_read_header(cfg.dataset);

event = ft_read_event(cfg.dataset);

VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';

Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);

Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP

FaceTrials.reref ='no';

FaceTrials.keeptrials ='yes';% classifiers operate on individual trials

FaceTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only

FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP

VaseTrials.reref ='no';

VaseTrials.keeptrials ='yes';% classifiers operate on individual trials

VaseTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only

VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA

cfg.layout ='biosemi64.lay';

cfg.method ='crossvalidate';

cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];

cfg.nfolds = 4;

cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy''binomial''contingency'};

stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);

stat.statistic.contingency

%Plot MVPA Results

stat.mymodel = stat.model{2}.primal;

cfg.parameter ='mymodel';

cfg.xlim = [-0.1 0.6];

cfg.comments ='';

cfg.colorbar ='yes';

cfg.interplimits='electrodes';

ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.

We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).

Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,

Diane Abdallah




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From jan.schoffelen at donders.ru.nl  Tue Sep 19 07:47:49 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 05:47:49 +0000
Subject: [FieldTrip] Question about MVPA topographic map
In-Reply-To: <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>
References: <1504785200819.38370@kent.ac.uk>
 <DAC1436B66E867F0.8B343E8D-FCE4-4492-86B8-1788699484E9@mail.outlook.com>
Message-ID: <A9C685DD-18DD-4F2F-99F6-99BA89CA43A4@donders.ru.nl>

Hi Diane,

First of all, I would recommend to share figures not as a matlab-figure, but as a screenshot bitmap or so. This saves people who are reading your mail a lot of overhead if they want to look at it, because they don’t need to start a matlab session etc.

Your topographical image looks ‘different’ from the one on the wiki because the distribution of your electrodes is more around the whole ‘rim’ of the head. The colored plane that shows up within the circle is the consequence of a spatial interpolation (in 2D) of the data points represented at the locations of the electrodes. For this reason also, there’s no need to be very explicit about the meaning of the x and y axes: they represent space.

Best wishes and good luck,

Jan-Mathijs



On 19 Sep 2017, at 06:25, D.Abdallah <da401 at kent.ac.uk<mailto:da401 at kent.ac.uk>> wrote:


Dear all,



I've had a bit of trouble understanding the results that I get when using the ft_topoplotER.
I have run on matlab R2014a the MVPA tutorial on fieldtrip: http://www.fieldtriptoolbox.org/tutorial/multivariateanalysis and tried to understand the resulting topographic map but wasn't able to because there is no proper legend that explains where the x and y axes are and they represent.



The experiment that my supervisor and I conducted is meant to look at the pattern of activity in the brain (using EEG) in a switch vs. non-switch task of Rubin's Face-Vase ambiguous stimulus. In order to study that we are using MVPA.



This is the code we are running on one of the subjects that we collected:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%PREPROCESSING
%Reading the data
cfg = [];
cfg.dataset = filename1;
cfg.reref ='yes';
cfg.channel = {'Cz','PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};
cfg.refchannel ='Cz';
cfg.demean ='yes';
data_eeg1 = ft_preprocessing(cfg);

%Segmenting data
cfg.trialdef.eventtype ='?';
Dummy = ft_definetrial(cfg);

cfg.trialdef.prestim = 0.1;
cfg.trialdef.poststim = 0.6;
cfg.baselinewindow = [-0.1 0];

cfg.trialdef.eventtype ='STATUS';
cfg.trialdef.eventvalue = [100];
stimulusTrigger = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [1];
FaceTrials = ft_definetrial(cfg);

cfg.trialdef.eventvalue = [2];
VaseTrials = ft_definetrial(cfg);

%Definitions of Triggers
stimulusTrigger = 100;
faceResponseTrigger = 1;
vaseResponseTrigger = 2;

%Define Face Trials and Conduct Preprocessing
[trlFaces, eventFaces] = ft_trialfun_BasedOnResp(FaceTrials,stimulusTrigger,faceResponseTrigger);
hdr = ft_read_header(cfg.dataset);
event = ft_read_event(cfg.dataset);
FaceData = ft_preprocessing(FaceTrials);

FaceTrigger = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).value]';
FaceSample = [eventFaces(strcmp(cfg.trialdef.eventtype, {eventFaces.type})).sample]';

Facepretrig = -round(cfg.trialdef.prestim * hdr.Fs);
Faceposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Define Vase Trials and Conduct Preprocessing
[trlVase, eventVase] = ft_trialfun_BasedOnResp(VaseTrials,stimulusTrigger,vaseResponseTrigger);
hdr = ft_read_header(cfg.dataset);
event = ft_read_event(cfg.dataset);
VaseData = ft_preprocessing(VaseTrials);

VaseTrigger = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).value]';
Vasesample = [eventVase(strcmp(cfg.trialdef.eventtype, {eventVase.type})).sample]';

Vasepretrig = -round(cfg.trialdef.prestim * hdr.Fs);
Vaseposttrig = round(cfg.trialdef.poststim * hdr.Fs);

%Calculate Face ERP
FaceTrials.reref ='no';
FaceTrials.keeptrials ='yes';% classifiers operate on individual trials
FaceTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only
FaceERP = ft_timelockanalysis(FaceTrials,FaceData);

%Calculate Vase ERP
VaseTrials.reref ='no';
VaseTrials.keeptrials ='yes';% classifiers operate on individual trials
VaseTrials.channel = {'PO9','PO7','PO3','PO','PO4','PO8''PO10','O1','Oz','O2','O9','O10'};% occipital channels only
VaseERP = ft_timelockanalysis(VaseTrials,VaseData);

%MVPA
cfg.layout ='biosemi64.lay';
cfg.method ='crossvalidate';
cfg.design = [ones(size(FaceERP.trial,1),1); 2*ones(size(VaseERP.trial,1),1)];
cfg.nfolds = 4;
cfg.latency = [-0.1 0.6];

cfg.statistic = {'accuracy''binomial''contingency'};
stat = ft_timelockstatistics (cfg, FaceERP,VaseERP);
stat.statistic.contingency

%Plot MVPA Results
stat.mymodel = stat.model{2}.primal;
cfg.parameter ='mymodel';
cfg.xlim = [-0.1 0.6];
cfg.comments ='';
cfg.colorbar ='yes';
cfg.interplimits='electrodes';
ft_topoplotER(cfg,stat);



Attached is the resulting topographic map. We found a very weird pattern that doesn't seem to show what we are expecting. It seems as though there might be a glitch or a step we missed.
We came to the conclusion after running figure(imagesc(stat.mymodel)) in order to understand the topographical map and found a completely different pattern (see second attached Imagesc subject 8 file).
Why are the patterns very different?



Moreover, when we ran the MVPA fieldtrip tutorial, the topographical map showed a proper pattern of activity (see tutorial topographic map).



All the best,
Diane Abdallah




<Imagesc Subject 8.fig><Subject 8 Topographical map.fig><tutorial topographic map.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From jan.schoffelen at donders.ru.nl  Tue Sep 19 08:02:53 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 06:02:53 +0000
Subject: [FieldTrip] Using ft_redefinetrial with minlength and
 begsample/endsample option
In-Reply-To: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>
References: <CAKiBOHkwDaCOzmUM989mhsxHVcx4KLFdLtHm0nOi+hReyW-k5w@mail.gmail.com>
Message-ID: <6728EEE0-D0EF-4A9D-AAEA-50A61E765FC9@donders.ru.nl>

Dear Nir Ofir,

Thanks for reporting this. It seems that you have also identified a possible solution, which would be to do the ‘too short trial removal’, only after the realignment of the time axis of the trials.
I think the best way to proceed would be that you try to implement this fix in your own local version of the FieldTrip git repository, and initiate a pull request once you are sure it works well. We will then review the suggested fix, and merge it into  Fieldtrip’s code base, so that everyone can benefit from your efforts.

Best wishes,

Jan-Mathijs


> On 14 Sep 2017, at 14:10, Nir Ofir <nirofir2 at gmail.com> wrote:
> 
> Hi fieldtrip users,
> 
> I have a data structure containing MEG trials which are aligned to stimulus onset. I now want to realign them to the target onset, as well as removing trials which are too short. I thought the easiest way to do this would be to use ft_redefinetrial in the following way:
> 
> offset = dat.trialinfo(:,5); % this column contains the duration of the stimulus-target intervel
> cfg = [];
> cfg.minlength = -dat.time{1}(1)+cfgx.pretarget+0.5; % prestim defined by dat sructure + 0.5 s ERF + cfgx.pretarget
> cfg.begsample = round((offset - cfgx.pretarget)*1000);
> cfg.endample = round(offset*1000);
> dat = ft_redefinetrial(cfg, dat);
> 
> When I run this, I get the following error:
> 
> Index exceeds matrix dimensions.
> 
> Error in ft_redefinetrial (line 209)
>     data.trial{i} = data.trial{i}(:, begsample(i):endsample(i));
> 
> So I looked into ft_redefinetrials a bit, and it seems like when minlength is defined, the trials themselves are removed, but the begample/endsample vector are not cut to contain only the relevant trials. For now I moved to a 2-step solution (first removing trials, than realigning), but it seems like this could have a relatively simple fix. Suggestions?
> 
> Thanks!
> Nir Ofir
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip




From isac.sehlstedt at psy.gu.se  Tue Sep 19 08:07:41 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Tue, 19 Sep 2017 06:07:41 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
Message-ID: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,

I have conducted a EEG experiement and am currently in a wedge. The PCA-function used in matlab ( i.e. pca() )  gives me the latent and coeff values that I want to use for further analysis. Sadly, I have cannot figure out how to perform a group level analysis using the matlab function and later "unmix" the group analysis to the subject level.

The a group analysis ft_componentanalysis function is easier to "unmix" thanks to its description of how to do so. However, I have not found a way to extract the latent and coefficient variables from the variables included in the comp-structure.

My question is:
Can you extract the latent and coefficient variables from the ft_componentanalysis results?

Alternatively:
Is it possible to extract the subject level latent and coefficient variables using the matlab function pca() ?

Very best,
Isac
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From jan.schoffelen at donders.ru.nl  Tue Sep 19 08:57:52 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 19 Sep 2017 06:57:52 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>
References: <D5E67F71.18C56%isac.sehlstedt@psy.gu.se>
Message-ID: <3197AF78-5B87-4F7C-A587-FFD8E8FF2071@donders.ru.nl>

Hi Isac,

My question is:
Can you extract the latent and coefficient variables from the ft_componentanalysis results?

I’d say that the ‘latent variables’ are in the comp.trial field, and the coefficients are in comp.topo


Alternatively:
Is it possible to extract the subject level latent and coefficient variables using the matlab function pca() ?

I don’t know.

Best wishes,
Jan-Mathijs



Very best,
Isac
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From jean-michel.badier at univ-amu.fr  Tue Sep 19 15:52:53 2017
From: jean-michel.badier at univ-amu.fr (Jean-Michel Badier)
Date: Tue, 19 Sep 2017 15:52:53 +0200
Subject: [FieldTrip] Open positions at INS, Marseille. France
Message-ID: <fc154009-2f7a-ab63-68de-9cb58d5d9617@univ-amu.fr>


  /Post-doctoral position in the Theoretical Neuroscience Group - INS,
  Marseille, France/

*Summary*

The Theoretical Neuroscience Group (Head: Viktor Jirsa) is seeking to 
fill a post-doctoral position in the context of the project EPINOV to 
work on statistical & dynamical modeling of seizure propagation using 
personalized brain modeling and neuroinformatics approaches on a cohort 
of hundreds of epilepsy patients. EPINOV is one of 10 large-scale 
projects selected in the 3rd round of French scientific excellence 
program «RHU» managed by the National Research Agency (ANR). The aim of 
the EPINOV project is to significantly improve presurgical 
interpretation, guide surgical strategies and translate computational 
tools into clinical routine of personalized medicine. We use individual 
MRI scans to reconstruct brain anatomy and connectivity, which are 
combined with a neural mass model and fit using the Bayesian modeling 
software Stan to individuals’ intracranial electrophysiology data 
(stereotactic EEG), validated by clinical data from other modalities, 
such as MEG, fMRI, and semiology.

*Responsibilities*

• Scale up statistical models “vertically” to handle more data and 
higher resolution anatomy, using model comparison techniques to evaluate 
the advantage of different model structures

• Scale out models “horizontally”, performing coherent, reliable 
inference across a large cohort of patients using dedicated, on-site HPC 
resources

• Develop routines to evaluate and visualize inference results, making 
them amenable to clinical interpretation

• Integrate developed code into existing code bases and pipelines

*Qualification *

• Highly motivated to work on an interdisciplinary project and 
collaborate with the various members of the consortium.

• PhD degree in computational neuroscience, mathematical or statistical 
modeling, machine learning or equivalent level of knowledge.

• Significant, demonstrable experience in data fitting (Bayesian 
modeling, Dynamical Causal Modeling (DCM), Monte Carlo, etc) will be 
highly appreciated.

• Experience with working in a Linux/HPC environment

• Programming in a numerically oriented language (R, Python, MATLAB)

• Familiarity with Git, unit testing, Docker/VMs is a plus

*The Theoretical Neuroscience group *

We are a multi-national team interested in understanding the mechanisms 
underlying the spatiotemporal organization of large-scale brain 
networks. Our work comprises mathematical and computational modeling of 
large-scale network dynamics and human brain imaging data, the 
development of neuroinformatics tools for studying large-scale brain 
networks applied to concrete functions, dysfunctions (epilepsy, 
dementia) and aging.

*Terms of salary and employment *

A 12-month renewable contract will be established. Salary will depend on 
the diploma and experience. Operating language in the laboratory is 
English. Applications including a cover letter, curriculum vitae and the 
names of two referees should be sent by September 30th 2017 to: Dr. 
Irene Yujnovsky at irene.yujnovsky at univ-amu.fr 
<mailto:irene.yujnovsky at univ-amu.fr>

More information about the INS and the Theoretical Neurosciences Group 
can be found at: http://ins.univ-amu.fr <http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>


<http://ins.univ-amu.fr/>*Clinical data manager for national consortium 
on epilepsy surgery - **Aix-Marseille Université *

*Marseille, FRANCE*

**

**

A position for an experienced clinical data manager is open to create 
and maintain an epileptic patient database including registration, 
normalization and security issues and to ensure the communication with 
the key partners in the academic, clinical and industry sectors with the 
aim of generating individual Virtual Patient models using The Virtual 
Brain (TVB) platform as framework (see 
http://www.thevirtualbrain.org).//This database will be generated in the 
context of the EPINOV (/Improving EPilepsy surgery management and 
progNOsis using Virtual brain technology) /projectled by Professor 
Fabrice Bartolomei 
(http://fr.ap-hm.fr/service/neurophysiologie-clinique-hopital-timone) 
funded by the RHU programme.

*Qualification*

**

Candidates must be highly motivated to work on an interdisciplinary 
project and collaborate with the various members of the consortium. They 
should have a degree in biomedical engineering, medical informatics or 
equivalent level of knowledge. Candidates must possess a solid 
experience in management of clinical and/or research data and 
programming (C, MATLAB, Python). Experience with neuroimaging data 
(stereotactic EEG, MRI, DTI, MEG, EEG), clinical trials, 
neuroinformatics and its standard formats (for instance DICOM, XNAT, 
BIDS), machine learning and Big Data would be considered an advantage.

*The EPINOV project and consortium *

We are a national consortium composed of clinicians, researchers and 
industrial partners interested in improving epilepsy surgical prognosis 
using large–scale brain modelling based on individual epileptic patient 
data. A prospective, randomized multicenter trial will be conducted with 
subjects suffering from drug-resistant epilepsy. The clinical trial will 
systematically evaluate the added value of personalized brain modelling 
in the surgical decision making.

*Terms of salary and employment*

Salary will depend on the diploma and previous experience. Operating 
languages in the consortium are both French and English.

Applications including a cover letter, curriculum vitae and the names of 
two referees should be sent by October 31st 2017 to: *Dr. Irene 
Yujnovsky* at irene.yujnovsky at univ-amu.fr 
<mailto:irene.yujnovsky at univ-amu.fr>


-- 
Jean michel Badier /- UMR S 1106 Institut de Neurosciences des Systèmes/
Aix-Marseille Université - Laboratoire MEG - TIMONE - 27 Boulevard Jean 
Moulin - 13005 Marseille
Tél: +33(0)4 91 38 55 62 - Fax : +33(0)4 91 78 99 14
Site : http://www.univ-amu.fr - Email : jean-michel.badier at univ-amu.fr 
<mailto:jean-michel.badier at univ-amu.fr>
/Afin de respecter l'environnement, merci de n'imprimer cet email que si 
nécessaire./
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From johnnguyen.education at gmail.com  Tue Sep 19 18:41:34 2017
From: johnnguyen.education at gmail.com (John Nguyen)
Date: Tue, 19 Sep 2017 12:41:34 -0400
Subject: [FieldTrip] Source analysis and sensor space differences
Message-ID: <CACzaKkHpp+LMKBLdjcbFooe5w0tddrGVeUgR1AFSU5n8Ya5BRA@mail.gmail.com>

Hi All,

My Name is John Nguyen and I am working at the Reinhart Cognitive
Neuroscience Lab at Boston University. I have been using Fieldtrip (Version
4/10/17)​​ for several months now and have decided to tackle
SourceAnalysis. After a few weeks of struggling with it, I find myself
still far from the goal post.

A milestone I'm trying to achieve is plotting activity in the visual cortex
due to a visual stimulus onset. This activity is easily reflected in my
sensor-level plots but, in the source-level, it's projection is more than a
bit wonky [image link, Negative sensor potential ,1.09-1.23s, following
visual stimulus followed by positive sensor potential at 1.22-1.48s neither
of which are present in source space.]
[https://drive.google.com/file/d/0B2UdTHvTeS9NNWV6dGJKZ2JWbk0/
view?usp=sharing]

In my current code I am using Fieldtrip templates to minimize error on my
end as much as possible.
"elec = ft_read_sens('standard_1020.elc');
load('standard_bem.mat','vol')
load('standard_sourcemodel3d8mm.mat','sourcemodel')
mri = ft_read_mri('single_subj_T1_1mm.nii');"

My Sourceanalysis is timelocked LCMV with a relative baseline change at
times -0.2 to 0.0s. I also preformed a relative baseline change on my
sensor-level data because I was wondering if the disparity was a baseline
issue. I've reached a dead-end and would appreciate any help. I've attached
links to my script
[https://drive.google.com/file/d/0B2UdTHvTeS9NT05uWUk4T0hDUjQ/
view?usp=sharing]

and data (no rereference, no artifact reject)
[https://drive.google.com/file/d/0B2UdTHvTeS9NSl8yaW9fcXZWSjQ/
view?usp=sharing]

and sensor cap layout.
[https://drive.google.com/file/d/0B2UdTHvTeS9NSGJXZUszOHBUbGM/
view?usp=sharing]


Best regards,
John Nguyen
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From isac.sehlstedt at psy.gu.se  Wed Sep 20 09:15:56 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Wed, 20 Sep 2017 07:15:56 +0000
Subject: [FieldTrip] Computing pca variables (i.e. Latent,
 and coefficient variables) after ft_componentanalysis
Message-ID: <D5E7D932.19177%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,


This is a follow-up question to a previous question with the same mail-topic.


I have included my code below to show what I am doing (in case I have made errors) and print screens (which are also attached) of the variables I get after the ft_componentanalysis that I get.


Sadly, I cannot see any variable named comp.trial (see Unknown.tiff, or Unknown-1.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see Unknown.tiff-2) . Why am I not getting that in ft? Is it possible to get that using ft?


Very Best,

Isac



----------------- The code -----------------


clear all; close all;


%% Load

load('averages_for_ft.mat')




%% define layout

cfg = [];

cfg.elec=PreOdd_ft{1, 1}.elec;

cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)

layout = ft_prepare_layout(cfg)




%% Make the computations


% Dummy varibles

Cond1 = [];

Cond2 = [];

theDiff = [];

theDiff_ft = {};



%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions

    curr_Cond2 = Cond2_ft{i}.avg;

    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions

    cfg = [];

    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});

    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math

    cfg  = [];

    cfg.operation = 'subtract';

    cfg.parameter = 'avg';

    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);

    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions

    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.

    Cond2 = [Cond2 curr_Cond2];

    Cond1 = [Cond1 curr_Cond1];

    theDiff = [theDiff curr_difference_avg];

end




%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};

dummy_Cond2.avg = Cond2;

dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};

dummy_Cond1.avg = Cond1;

dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};

dummy_theDiff.avg = theDiff;

dummy_theDiff.time = 1:1:size(theDiff,2);




%% Run the PCA



cfg = [];

cfg.method  = 'pca';

cfg.layout = layout;


Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);

Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);

theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);




%% Revert back to subject level


cfgCond2 = [];

cfgCond2.unmixing = Cond2_comp.unmixing;

cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];

cfgCond1.unmixing = Cond1_comp.unmixing;

cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];

cfgtheDiff.unmixing = theDiff_comp.unmixing;

cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)

    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});

    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});

    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );

end



----------------- The variables/results  -----------------




[X]



[X]


[X]
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From litvak.vladimir at gmail.com  Wed Sep 20 12:56:01 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Wed, 20 Sep 2017 11:56:01 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
Message-ID: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results
when doing TF decomposition compared to computing a spectrum for the same
time window. I'm not sure I got to the bottom of it yet but one thing I
found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
differently by increasing padding. For short padding the results are
similar but with increasing padding there are differences both in offset of
the spectrum and its overall shape. See attached images where the top one
shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as
well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper',
'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
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From r.oostenveld at donders.ru.nl  Wed Sep 20 16:26:37 2017
From: r.oostenveld at donders.ru.nl (Robert Oostenveld)
Date: Wed, 20 Sep 2017 16:26:37 +0200
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
Message-ID: <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com> wrote:
> 
> Dear Fieldtrippers,
> 
> I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero. 
> 
> Is this a bug or a feature?
> 
> Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.
> 
> Thanks,
> 
> Vladimir
> 
> 
> -------------------------------------
> 
> pad = 0.5;%1%10
> 
> 
> freqoi = 5:45;
> timwin = 0.4+0*freqoi;
> 
> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
> 
> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
> 
> figure;
> subplot(2,1,1)
> plot(freqoi, s1);
> subplot(2,1,2);
> plot(freqoi, s1-s1(1));
> %%
> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
> 
> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
> 
> subplot(2,1,1)
> hold on
> plot(freqoi, s2, 'r');
> subplot(2,1,2)
> hold on
> plot(freqoi, s2-s2(1), 'r');
> <pad0_5.png><pad1.png><pad10.png>_______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Wed Sep 20 17:03:52 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Wed, 20 Sep 2017 17:03:52 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
Message-ID: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper
segmentation for which changing individual brain/scalp thresholds works in
many cases but not all, e.g. when the scalp 'bleeds' into some noise
outside of the head. Also, changing parameters in spm8 for normalization,
such as number of iterations (directly in in spm_normalize, since FT does
not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from
outsides of the head 'bleed' onto the scalp, thereby preventing optimal
scalp-segmentation and thereby normalization. Others have an inappropriate
contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved
preprocessing procedures. However, it does not seem supported in FT yet.
Does anyone have experience with this, and can perhaps share how they
extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
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From hamedtaheri at yahoo.com  Wed Sep 20 20:00:47 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Wed, 20 Sep 2017 18:00:47 +0000 (UTC)
Subject: [FieldTrip] Splitting EEG
References: <1779089334.6211483.1505930447659.ref@mail.yahoo.com>
Message-ID: <1779089334.6211483.1505930447659@mail.yahoo.com>

Hello Fieldtrip users,
I have a continues EEG data ( 80 seconds) and I would like to analyze some part of it.For instance, I want to analyse seconds 20 to 25 ( 5 seconds).Would you please let me know how can I select my times of interest.I've written a simple code but I don't know how can I split the data. 
cfg    = [];
cfg.dataset = '........  .eeg';
data_org                = ft_preprocessing(cfg); %Original Data

% Step1:  Filtering Row Data
cfg                        = [];
cfg.bpfilter            = 'yes';
cfg.bpfreq             = [1 30];
data_Filtered        = ft_preprocessing(cfg,data_org); 
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From sarang at cfin.au.dk  Wed Sep 20 22:22:51 2017
From: sarang at cfin.au.dk (Sarang S. Dalal)
Date: Wed, 20 Sep 2017 20:22:51 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
Message-ID: <0A0286C7-606F-4B30-B8F4-6689EAAD9620@cfin.au.dk>

Hi Stephen,

We have a pipeline that can use either SPM8 or SPM12 to perform both segmentation and normalization, though perhaps in a way that’s different from the official FieldTrip tutorials. Have a look at:
https://github.com/meeg-cfin/nemolab/blob/master/basics/nemo_mriproc.m

ft_volumesegment should use whichever SPM is in your path (be careful about fieldtrip/external/spm8!), and (according to my memory) SPM12 sometimes can succeed where SPM8 doesn’t provide good segmentations.

Note that for the normalization in SPM12, our script defines “/OldNorm/T1.nii” as the template, which indeed seems to give results equivalent to SPM8. I think you could change this to SPM12’s default template if you prefer.

NB: we use MRI coordinates as the base coordinate system in our pipelines, so MEG/EEG is transformed to MRI, rather than MRI to MEG/EEG.

Cheers,
Sarang


On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at GMAIL.COM<mailto:stephen.whitmarsh at GMAIL.COM>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From cornelia.quaedflieg at uni-hamburg.de  Wed Sep 20 22:51:57 2017
From: cornelia.quaedflieg at uni-hamburg.de (Conny Quaedflieg)
Date: Wed, 20 Sep 2017 22:51:57 +0200
Subject: [FieldTrip] Splitting EEG
In-Reply-To: <1779089334.6211483.1505930447659@mail.yahoo.com>
References: <1779089334.6211483.1505930447659.ref@mail.yahoo.com>
 <1779089334.6211483.1505930447659@mail.yahoo.com>
Message-ID: <20170920205155.9C930B5309@mailhost.uni-hamburg.de>

Dear Hamed, 

You can use ft_select data with cfg.latency 
See
http://www.fieldtriptoolbox.org/reference/ft_selectdata 

best 
Conny

Van: Hamed Taheri
Verzonden: woensdag 20 september 2017 20:12
Aan: fieldtrip at science.ru.nl
Onderwerp: [FieldTrip] Splitting EEG

Hello Fieldtrip users,

I have a continues EEG data ( 80 seconds) and I would like to analyze some part of it.
For instance, I want to analyse seconds 20 to 25 ( 5 seconds).
Would you please let me know how can I select my times of interest.
I've written a simple code but I don't know how can I split the data. 

cfg    = [];
cfg.dataset = '........  .eeg';
data_org                = ft_preprocessing(cfg); %Original Data

% Step1:  Filtering Row Data
cfg                        = [];
cfg.bpfilter            = 'yes';
cfg.bpfreq             = [1 30];
data_Filtered        = ft_preprocessing(cfg,data_org); 

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From jan.schoffelen at donders.ru.nl  Thu Sep 21 09:09:06 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 07:09:06 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
Message-ID: <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>

Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).

Best,
Jan-Mathijs

On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From litvak.vladimir at gmail.com  Thu Sep 21 11:17:11 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 10:17:11 +0100
Subject: [FieldTrip] MEG technician post at UCL
Message-ID: <CACq1Bh+rMEWNX-LiMp6gBWeCiQB9dkyXAmsw5=Jn9STB3nHrTA@mail.gmail.com>

*Senior MEG Research Technician*



Applications are invited for a Senior Research Technician in the Wellcome
Centre for Human Neuroimaging, UCL Institute of Neurology.



The Centre houses an Electroencephalography (EEG) system, two
Magnetoencephalography (MEG) systems - a CTF 275 channel Omega System and
an Optically Pumped Magnetometer (OPM) System -  and Magnetic Resonance
Imaging (MRI) facilities - two 3T Siemens Prisma scanners and a 3T Siemens
Trio.  The successful candidate will be responsible for coordinating and
maintaining an efficient MEG and EEG service for a range of different
research projects. This role will be constantly evolving as new and
alternative technologies are incorporated into the functional imaging
department.





*Applicants are required to have:*

·         Experience of EEG/MEG or similar electrophysiological recording
methods (e.g. cardiology/audiology) within a clinical or research
environment.

·         A commitment to academic research.

·         MEG experience is not essential as training will be provided.



*Salary - UCL Grade 7:*

£34,653 to £41,864 inclusive of London Allowance.

The post is available immediately and is funded until Nov 2021 in the first
instance.



Applications through UCL's online recruitment – www.ucl.ac.uk/hr/jobs where
you can download a job description and person specification using ref:
1671016.



Informal enquiries to Elaine Williams:  elaine.williams at ucl.ac.uk . If you
have any queries regarding the application process, please contact Samantha
Robinson, HR Officer, Institute of Neurology, 23 Queen Square, London, WC1N
3BG
<https://maps.google.com/?q=23+Queen+Square,+London,+WC1N+3BG&entry=gmail&source=g>
(email: ion.hradmin at ucl.ac.uk).



*Closing date:  26th September 2017*

*Taking Action for Equality*
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From jan.schoffelen at donders.ru.nl  Thu Sep 21 11:53:05 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 09:53:05 +0000
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
Message-ID: <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>

Hi to all who’s reading along,

Perhaps the two cases will become more similar once the ‘timwin’ is increased in length for the mtmconvol case….

Best wishes,

JM

On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl<mailto:r.oostenveld at donders.ru.nl>> wrote:

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
<pad0_5.png><pad1.png><pad10.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From litvak.vladimir at gmail.com  Thu Sep 21 12:29:12 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 11:29:12 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
Message-ID: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>

Hi Jan-Mathijs,

Yes, you are right about Robert's example. But if you do:

pad = 10;

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
'hanning', 'pad', pad);

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');


You will see the problem that I'm talking about. We discussed with Robert
yesterday and this is indeed 'a feature' which has to do with the fact that
the outputs of mtmfft and mtmconvol have different units. The former is
spectral density whereas the latter is spectral power.

Here is what Robert wrote me:


the units of computations (also here) are a known and long-standing issue.
I know for a long time that the two have different scaling, but did not
think about it for a long time. I recall something like this: To compare
TFRs over frequencies, you don't want the bandwidth to affect the estimate.
Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect
those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with
conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral
resolution over which the signal(and noise) spreads is different. If you
were to compute the TFR in V^2/Hz, the same V at 20Hz would have a
different value, because the length of the wavelet affects the 1/Hz.
something related (but nevertheless different) applies to the mtmfft: if
you want to estimate broadband activity in a window of 1 second or a window
of 2 seconds, you would get different spectral resolutions. The nyquist is
the same, but the power gets distributed over more bins between 0 and
Fnyquist/2. That would cause the values to appear smaller in the 2-s case.
Hence we compute spectral density, which somehow normalizes for this. I
never found a really clear explanation, but google got me this
https://dsp.stackexchange.com/questions/33957/what-is-the-difference-between-the-psd-and-the-power-spectrum
what confuses me is that power (or variance) is already normalized, i.e.
sum of squared values divided by N. So we have energy (which increases with
length), power (which does not increase with length), and power density


So one issue is that most people don't know about this including me and
possibly you. I think a good solution would be to add an option to specify
the output units for all the methods as there might be quite subtle
considerations for choosing one over the other as Robert suggests.

Vladimir

On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi to all who’s reading along,
>
> Perhaps the two cases will become more similar once the ‘timwin’ is
> increased in length for the mtmconvol case….
>
> Best wishes,
>
> JM
>
> On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl>
> wrote:
>
> Hi Vladimir,
>
> I suggest that you first start with a simpler case, like this
>
> fsample = 1000;
> time = (1:1000)/fsample;
> dat = randn(size(time));
>
> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
> 'hanning');
>
> power1 = abs(spectrum1).^2;
> power1 = squeeze(power1);
>
> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);
>
> power2 = abs(spectrum2).^2;
> power2 = squeeze(power2);
>
> figure
> plot(freqoi1, power1);
> hold on
> plot(freqoi2, power2, 'r');
>
> Note that these are not the same (albeit similar), which I had expected…
>
> best
> Robert
>
>
>
> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com>
> wrote:
>
> Dear Fieldtrippers,
>
> I'm looking into an issue of one of SPM users who gets different results
> when doing TF decomposition compared to computing a spectrum for the same
> time window. I'm not sure I got to the bottom of it yet but one thing I
> found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
> differently by increasing padding. For short padding the results are
> similar but with increasing padding there are differences both in offset of
> the spectrum and its overall shape. See attached images where the top one
> shows original spectra and the bottom one aligns the lowermost bin to zero.
>
> Is this a bug or a feature?
>
> Below is the script that produces these plots. I could provide the data as
> well but this could probably be reproduced with any data.
>
> Thanks,
>
> Vladimir
>
>
> -------------------------------------
>
> pad = 0.5;%1%10
>
>
> freqoi = 5:45;
> timwin = 0.4+0*freqoi;
>
> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time,
> 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>
> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
>
> figure;
> subplot(2,1,1)
> plot(freqoi, s1);
> subplot(2,1,2);
> plot(freqoi, s1-s1(1));
> %%
> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
> 'hanning', 'freqoi', freqoi,...
>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>
> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
>
> subplot(2,1,1)
> hold on
> plot(freqoi, s2, 'r');
> subplot(2,1,2)
> hold on
> plot(freqoi, s2-s2(1), 'r');
> <pad0_5.png><pad1.png><pad10.png>__________________________
> _____________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From litvak.vladimir at gmail.com  Thu Sep 21 12:34:23 2017
From: litvak.vladimir at gmail.com (Vladimir Litvak)
Date: Thu, 21 Sep 2017 11:34:23 +0100
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
 <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
Message-ID: <CACq1BhKab0TFFr=eX1peo_w2PPBuq5tGuCzBh9muB7Dyvm=R_A@mail.gmail.com>

Another thing that I noticed is that in the mtmconvol case padding is added
to the entire trial, not to the short window over which FFT is actually
computed. This might be because you actually use a wavelet which moves
along the data (I didn't check that). Anyhow right now this doesn't make
much difference because padding doesn't affect mtmconvol in such a dramatic
way as mtmfft. However, if you do allow specifying the units as power
rather than density then the way things are now mtmconvol and mtmfft with
the same padding would not be equivalent.

Vladimir

On Thu, Sep 21, 2017 at 11:29 AM, Vladimir Litvak <litvak.vladimir at gmail.com
> wrote:

> Hi Jan-Mathijs,
>
> Yes, you are right about Robert's example. But if you do:
>
> pad = 10;
>
> fsample = 1000;
> time = (1:1000)/fsample;
> dat = randn(size(time));
>
> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
> 'hanning', 'pad', pad);
>
> power1 = abs(spectrum1).^2;
> power1 = squeeze(power1);
>
> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);
>
> power2 = abs(spectrum2).^2;
> power2 = squeeze(power2);
>
> figure
> plot(freqoi1, power1);
> hold on
> plot(freqoi2, power2, 'r');
>
>
> You will see the problem that I'm talking about. We discussed with Robert
> yesterday and this is indeed 'a feature' which has to do with the fact that
> the outputs of mtmfft and mtmconvol have different units. The former is
> spectral density whereas the latter is spectral power.
>
> Here is what Robert wrote me:
>
>
> the units of computations (also here) are a known and long-standing issue.
> I know for a long time that the two have different scaling, but did not
> think about it for a long time. I recall something like this: To compare
> TFRs over frequencies, you don't want the bandwidth to affect the estimate.
> Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect
> those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with
> conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral
> resolution over which the signal(and noise) spreads is different. If you
> were to compute the TFR in V^2/Hz, the same V at 20Hz would have a
> different value, because the length of the wavelet affects the 1/Hz.
> something related (but nevertheless different) applies to the mtmfft: if
> you want to estimate broadband activity in a window of 1 second or a window
> of 2 seconds, you would get different spectral resolutions. The nyquist is
> the same, but the power gets distributed over more bins between 0 and
> Fnyquist/2. That would cause the values to appear smaller in the 2-s case.
> Hence we compute spectral density, which somehow normalizes for this. I
> never found a really clear explanation, but google got me this
> https://dsp.stackexchange.com/questions/33957/what-is-the-
> difference-between-the-psd-and-the-power-spectrum
> what confuses me is that power (or variance) is already normalized, i.e.
> sum of squared values divided by N. So we have energy (which increases with
> length), power (which does not increase with length), and power density
>
>
> So one issue is that most people don't know about this including me and
> possibly you. I think a good solution would be to add an option to specify
> the output units for all the methods as there might be quite subtle
> considerations for choosing one over the other as Robert suggests.
>
> Vladimir
>
> On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi to all who’s reading along,
>>
>> Perhaps the two cases will become more similar once the ‘timwin’ is
>> increased in length for the mtmconvol case….
>>
>> Best wishes,
>>
>> JM
>>
>> On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl>
>> wrote:
>>
>> Hi Vladimir,
>>
>> I suggest that you first start with a simpler case, like this
>>
>> fsample = 1000;
>> time = (1:1000)/fsample;
>> dat = randn(size(time));
>>
>> [spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper',
>> 'hanning');
>>
>> power1 = abs(spectrum1).^2;
>> power1 = squeeze(power1);
>>
>> [spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time,
>> 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);
>>
>> power2 = abs(spectrum2).^2;
>> power2 = squeeze(power2);
>>
>> figure
>> plot(freqoi1, power1);
>> hold on
>> plot(freqoi2, power2, 'r');
>>
>> Note that these are not the same (albeit similar), which I had expected…
>>
>> best
>> Robert
>>
>>
>>
>> On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com>
>> wrote:
>>
>> Dear Fieldtrippers,
>>
>> I'm looking into an issue of one of SPM users who gets different results
>> when doing TF decomposition compared to computing a spectrum for the same
>> time window. I'm not sure I got to the bottom of it yet but one thing I
>> found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected
>> differently by increasing padding. For short padding the results are
>> similar but with increasing padding there are differences both in offset of
>> the spectrum and its overall shape. See attached images where the top one
>> shows original spectra and the bottom one aligns the lowermost bin to zero.
>>
>> Is this a bug or a feature?
>>
>> Below is the script that produces these plots. I could provide the data
>> as well but this could probably be reproduced with any data.
>>
>> Thanks,
>>
>> Vladimir
>>
>>
>> -------------------------------------
>>
>> pad = 0.5;%1%10
>>
>>
>> freqoi = 5:45;
>> timwin = 0.4+0*freqoi;
>>
>> [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time,
>> 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
>>     'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>>
>> s1 = squeeze(mean(mean(abs(spectrum), 4), 2));
>>
>> figure;
>> subplot(2,1,1)
>> plot(freqoi, s1);
>> subplot(2,1,2);
>> plot(freqoi, s1-s1(1));
>> %%
>> [spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper',
>> 'hanning', 'freqoi', freqoi,...
>>     'tapsmofrq', 1, 'verbose', 0, 'pad', pad);
>>
>> s2 = squeeze(mean(mean(abs(spectrum), 2), 1));
>>
>> subplot(2,1,1)
>> hold on
>> plot(freqoi, s2, 'r');
>> subplot(2,1,2)
>> hold on
>> plot(freqoi, s2-s2(1), 'r');
>> <pad0_5.png><pad1.png><pad10.png>___________________________
>> ____________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
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From jan.schoffelen at donders.ru.nl  Thu Sep 21 12:36:30 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 21 Sep 2017 10:36:30 +0000
Subject: [FieldTrip] Padding with mtmfft and mtmconvol
In-Reply-To: <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
References: <CACq1BhK_Jo7Hg1CMPiff7KZb6sbYA4-jXrjD2W5Qm-sVPs8PgQ@mail.gmail.com>
 <28E8B0F3-716D-4B19-83CF-88633BAE0B5D@donders.ru.nl>
 <54500427-7820-4544-970C-3C77C71739DA@donders.ru.nl>
 <CACq1Bh+8tWUxTjYXcZR_V4yOa6aKthaH1Qt42RVq11tVb_QWhQ@mail.gmail.com>
Message-ID: <612489C6-6EFD-4D56-A9FC-189A180CC961@donders.ru.nl>

Don’t worry Vladimir,

Robert and I have discussed these issues at length on several occasions in the past ;)
Best wishes,
JM

On 21 Sep 2017, at 12:29, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Hi Jan-Mathijs,

Yes, you are right about Robert's example. But if you do:

pad = 10;

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning', 'pad', pad);

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.99, 'pad', pad);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');


You will see the problem that I'm talking about. We discussed with Robert yesterday and this is indeed 'a feature' which has to do with the fact that the outputs of mtmfft and mtmconvol have different units. The former is spectral density whereas the latter is spectral power.

Here is what Robert wrote me:

the units of computations (also here) are a known and long-standing issue. I know for a long time that the two have different scaling, but did not think about it for a long time. I recall something like this: To compare TFRs over frequencies, you don't want the bandwidth to affect the estimate. Shorter wavelets have a larger bandwidth, hence the 1/Hz would affect those. E.g. imagine a 10Hz and a 20Hz sine wave, and do a TFR with conventional wavelets: at 20Hz the wavelet is 2x shorter, so the spectral resolution over which the signal(and noise) spreads is different. If you were to compute the TFR in V^2/Hz, the same V at 20Hz would have a different value, because the length of the wavelet affects the 1/Hz.
something related (but nevertheless different) applies to the mtmfft: if you want to estimate broadband activity in a window of 1 second or a window of 2 seconds, you would get different spectral resolutions. The nyquist is the same, but the power gets distributed over more bins between 0 and Fnyquist/2. That would cause the values to appear smaller in the 2-s case. Hence we compute spectral density, which somehow normalizes for this. I never found a really clear explanation, but google got me this https://dsp.stackexchange.com/questions/33957/what-is-the-difference-between-the-psd-and-the-power-spectrum
what confuses me is that power (or variance) is already normalized, i.e. sum of squared values divided by N. So we have energy (which increases with length), power (which does not increase with length), and power density


So one issue is that most people don't know about this including me and possibly you. I think a good solution would be to add an option to specify the output units for all the methods as there might be quite subtle considerations for choosing one over the other as Robert suggests.

Vladimir

On Thu, Sep 21, 2017 at 10:53 AM, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi to all who’s reading along,

Perhaps the two cases will become more similar once the ‘timwin’ is increased in length for the mtmconvol case….

Best wishes,

JM

On 20 Sep 2017, at 16:26, Robert Oostenveld <r.oostenveld at donders.ru.nl<mailto:r.oostenveld at donders.ru.nl>> wrote:

Hi Vladimir,

I suggest that you first start with a simpler case, like this

fsample = 1000;
time = (1:1000)/fsample;
dat = randn(size(time));

[spectrum1,ntaper1,freqoi1] = ft_specest_mtmfft(dat, time, 'taper', 'hanning');

power1 = abs(spectrum1).^2;
power1 = squeeze(power1);

[spectrum2,ntaper2,freqoi2,timeoi2] = ft_specest_mtmconvol(dat, time, 'taper', 'hanning', 'timeoi', mean(time), 'timwin', 0.5);

power2 = abs(spectrum2).^2;
power2 = squeeze(power2);

figure
plot(freqoi1, power1);
hold on
plot(freqoi2, power2, 'r');

Note that these are not the same (albeit similar), which I had expected…

best
Robert



On 20 Sep 2017, at 12:56, Vladimir Litvak <litvak.vladimir at gmail.com<mailto:litvak.vladimir at gmail.com>> wrote:

Dear Fieldtrippers,

I'm looking into an issue of one of SPM users who gets different results when doing TF decomposition compared to computing a spectrum for the same time window. I'm not sure I got to the bottom of it yet but one thing I found is that ft_specest_mtmfft and ft_specest_mtmconvol are affected differently by increasing padding. For short padding the results are similar but with increasing padding there are differences both in offset of the spectrum and its overall shape. See attached images where the top one shows original spectra and the bottom one aligns the lowermost bin to zero.

Is this a bug or a feature?

Below is the script that produces these plots. I could provide the data as well but this could probably be reproduced with any data.

Thanks,

Vladimir


-------------------------------------

pad = 0.5;%1%10


freqoi = 5:45;
timwin = 0.4+0*freqoi;

[spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(data, time, 'taper', 'hanning', 'timeoi', 1.2, 'freqoi', freqoi,...
    'timwin', timwin, 'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s1 = squeeze(mean(mean(abs(spectrum), 4), 2));

figure;
subplot(2,1,1)
plot(freqoi, s1);
subplot(2,1,2);
plot(freqoi, s1-s1(1));
%%
[spectrum,ntaper,freqoi] = ft_specest_mtmfft(data, time, 'taper', 'hanning', 'freqoi', freqoi,...
    'tapsmofrq', 1, 'verbose', 0, 'pad', pad);

s2 = squeeze(mean(mean(abs(spectrum), 2), 1));

subplot(2,1,1)
hold on
plot(freqoi, s2, 'r');
subplot(2,1,2)
hold on
plot(freqoi, s2-s2(1), 'r');
<pad0_5.png><pad1.png><pad10.png>_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Thu Sep 21 14:36:43 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 14:36:43 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
Message-ID: <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in
/external), to use SPM12 for segmentation of my template and my subject
MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
improvement over 8 when it comes to brain-segmentation, which now does not
require individual treatments anymore. It also outputs more compartments
which gives me a little bit more to work with when dealing with scans that
have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to
spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in
ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!
Stephen



On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi Stephen,
>
> Please note that FT now has full support for SPM12, both using the
> old-style segmentation, and the new one (the latter yielding 6 tissue
> types).
>
> Best,
> Jan-Mathijs
>
> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
> wrote:
>
> Dear all,
>
> I having some problems in normalizing MRIs for my study. Some have
> improper segmentation for which changing individual brain/scalp thresholds
> works in many cases but not all, e.g. when the scalp 'bleeds' into some
> noise outside of the head. Also, changing parameters in spm8 for
> normalization, such as number of iterations (directly in in spm_normalize,
> since FT does not pass these parameters) improves the transformation.
>
> However, some scans I cannot deal with, either because they have noise
> from outsides of the head 'bleed' onto the scalp, thereby preventing
> optimal scalp-segmentation and thereby normalization. Others have an
> inappropriate contrast MRI sequence.
>
> Some fMRI researchers advised me to use SPM12, because of its improved
> preprocessing procedures. However, it does not seem supported in FT yet.
> Does anyone have experience with this, and can perhaps share how they
> extracted the transformation matrix from the resulting nifti's?
>
> Thanks,
> Stephen
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From hgould at memphis.edu  Thu Sep 21 15:56:38 2017
From: hgould at memphis.edu (Herbert J Gould (hgould))
Date: Thu, 21 Sep 2017 13:56:38 +0000
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>,
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
Message-ID: <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>

I have retired please remove me from the mail list

Herbert Jay Gould
Professor Emeritus
The University of Memphis



Sent from my Verizon Wireless 4G LTE smartphone


-------- Original message --------
From: Stephen Whitmarsh
Date:09/21/2017 7:43 AM (GMT-06:00)
To: FieldTrip discussion list
Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!
Stephen



On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).

Best,
Jan-Mathijs

On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

Thanks,
Stephen
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip


_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From a.stolk8 at gmail.com  Thu Sep 21 17:00:20 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Thu, 21 Sep 2017 08:00:20 -0700
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
Message-ID: <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>

Hey Stephen,

Look for discussions regarding spm12 and also dartel on bugzilla. It's been a while but as far as I can remember ft_volumenormalize is the only function now that has not been integrated. Reason being that it wasnt straightforward to house the dartel procedure under a single function, so this is ongoing work still. You can however use spm12's coregistration function with ft_volumerealign (for rigid body transformations), which Im using quite a bit and never let me down (and is much faster than before). But that wouldnt work for normalization to template space though (use spm8).

Best

> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu> wrote:
> 
> I have retired please remove me from the mail list 
> 
> Herbert Jay Gould
> Professor Emeritus
> The University of Memphis
> 
> 
> 
> Sent from my Verizon Wireless 4G LTE smartphone
> 
> 
> -------- Original message --------
> From: Stephen Whitmarsh
> Date:09/21/2017 7:43 AM (GMT-06:00)
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization
> 
> Dear Sarang and Jan-Mathijs,
> 
> Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.
> 
> Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12. 
> 
> In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.
> 
> Best wishes and thanks again!
> Stephen
> 
> 
> 
>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl> wrote:
>> Hi Stephen,
>> 
>> Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).
>> 
>> Best,
>> Jan-Mathijs
>> 
>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com> wrote:
>>> 
>>> Dear all,
>>> 
>>> I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.
>>> 
>>> However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.
>>> 
>>> Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's? 
>>> 
>>> Thanks,
>>> Stephen
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> 
>> 
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> 
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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From zhangwenjia2732 at 126.com  Thu Sep 21 17:30:13 2017
From: zhangwenjia2732 at 126.com (=?GBK?B?1cXOxLzO?=)
Date: Thu, 21 Sep 2017 23:30:13 +0800 (CST)
Subject: [FieldTrip] Reading data too slow
Message-ID: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>





Dear all,


I have some problems in reading data into fieldtrip.
Specifically, I used EGI system to record EEG data and preprocessed them with Brainvison analyzer 
Then, I exported the preprocessed data into generic data format, making 3 files: .eeg, .vhdr and vmrk.
Last, I used ft_definetrial and ft_preprocessing to read these data into FieldTrip.
However, the reading is very very slow.


I tried to make only 2 channels left and tried methods as follow:
http://www.fieldtriptoolbox.org/faq/reading_is_slow_can_i_write_my_raw_data_to_a_more_efficient_file_format
But, they all did not work.


Does anyone know what I am doing wrong? Any advice very appreciated. 
Thank you


--

Wenjia
NYU Shanghai
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From stephen.whitmarsh at gmail.com  Thu Sep 21 17:52:31 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 17:52:31 +0200
Subject: [FieldTrip] Reading data too slow
In-Reply-To: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>
References: <21cb46ef.bd6f.15ea50f8baa.Coremail.zhangwenjia2732@126.com>
Message-ID: <CAFrxm=yXm=k-AAaNjKgz0pGABc6pnYyP8hCgPQFy0j=e_3BUyg@mail.gmail.com>

Hi Wenjia,

It's impossible to give specific advice with no extra information.

How slow?
Is the data read at all?
Any error messages?
What script are you exactly running and what is the output?
See:
http://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community

I would also check your computer resources (CPU and memory) during loading
to see if you are running into a memory/CPU problem specific for your
system.
Finally, I would start with no filters. They sometimes take a while.

Cheers,
Stephen



On 21 September 2017 at 17:30, 张文嘉 <zhangwenjia2732 at 126.com> wrote:

>
>
> Dear all,
>
> I have some problems in reading data into fieldtrip.
> Specifically, I used EGI system to record EEG data and preprocessed them
> with Brainvison analyzer
> Then, I exported the preprocessed data into generic data format, making 3
> files: .eeg, .vhdr and vmrk.
> Last, I used ft_definetrial and ft_preprocessing to read these data into
> FieldTrip.
> However, the reading is very very slow.
>
> I tried to make only 2 channels left and tried methods as follow:
> http://www.fieldtriptoolbox.org/faq/reading_is_slow_can_i_
> write_my_raw_data_to_a_more_efficient_file_format
> But, they all did not work.
>
> Does anyone know what I am doing wrong? Any advice very appreciated.
> Thank you
>
> --
> Wenjia
> NYU Shanghai
>
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From stephen.whitmarsh at gmail.com  Thu Sep 21 18:20:47 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Thu, 21 Sep 2017 18:20:47 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
Message-ID: <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>

Hi Arjen,

Thanks, and good to hear you've not been let down yet. It might be the fact
that I have some bad quality MRIs to deal with. However... does this
problem (see attached) ring a bell for anyone?:

Brain segmentation is proper, and co-registration with polhemus  head-shape
as well, but inverse warp to MNI result in a tilted grid. Linear vs.
non-linear transformation gives the same result. Other subjects going
through the same procedure work fine, except two others wherein I
identified it as a problem in segmenting the scalp and therefor the first
step of the normalization. This one looks absolutely fine in every other
regard, however.

I'm stumped...

    cfg                        = [];
    cfg.spmversion          = 'spm12';
    cfg.grid.warpmni        = 'yes';
    cfg.grid.template       = template_grid;
    cfg.grid.nonlinear      = 'yes';
    cfg.mri                 = mri_realigned;
    cfg.grid.unit           = 'mm';
    subject_grid            = ft_prepare_sourcemodel(cfg);

Cheers,
Stephen


On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> Hey Stephen,
>
> Look for discussions regarding spm12 and also dartel on bugzilla. It's
> been a while but as far as I can remember ft_volumenormalize is the only
> function now that has not been integrated. Reason being that it wasnt
> straightforward to house the dartel procedure under a single function, so
> this is ongoing work still. You can however use spm12's coregistration
> function with ft_volumerealign (for rigid body transformations), which Im
> using quite a bit and never let me down (and is much faster than before).
> But that wouldnt work for normalization to template space though (use spm8).
>
> Best
>
> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu>
> wrote:
>
> I have retired please remove me from the mail list
>
> Herbert Jay Gould
> Professor Emeritus
> The University of Memphis
>
>
>
> Sent from my Verizon Wireless 4G LTE smartphone
>
>
> -------- Original message --------
> From: Stephen Whitmarsh
> Date:09/21/2017 7:43 AM (GMT-06:00)
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
> normalization
>
> Dear Sarang and Jan-Mathijs,
>
> Thanks a lot. I am now able (after updating FT, which now includes SPM12
> in /external), to use SPM12 for segmentation of my template and my subject
> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
> improvement over 8 when it comes to brain-segmentation, which now does not
> require individual treatments anymore. It also outputs more compartments
> which gives me a little bit more to work with when dealing with scans that
> have bad delineation of the scalp for normalization.
>
> Pleas note that defaults seems to differ - some FT functions default to
> spm8, others to spm12.
>
> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
> 'spm12'. In other words the cfg.spmversion is not passed along.
>
> Best wishes and thanks again!
> Stephen
>
>
>
> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi Stephen,
>>
>> Please note that FT now has full support for SPM12, both using the
>> old-style segmentation, and the new one (the latter yielding 6 tissue
>> types).
>>
>> Best,
>> Jan-Mathijs
>>
>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
>> wrote:
>>
>> Dear all,
>>
>> I having some problems in normalizing MRIs for my study. Some have
>> improper segmentation for which changing individual brain/scalp thresholds
>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>> noise outside of the head. Also, changing parameters in spm8 for
>> normalization, such as number of iterations (directly in in spm_normalize,
>> since FT does not pass these parameters) improves the transformation.
>>
>> However, some scans I cannot deal with, either because they have noise
>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>> optimal scalp-segmentation and thereby normalization. Others have an
>> inappropriate contrast MRI sequence.
>>
>> Some fMRI researchers advised me to use SPM12, because of its improved
>> preprocessing procedures. However, it does not seem supported in FT yet.
>> Does anyone have experience with this, and can perhaps share how they
>> extracted the transformation matrix from the resulting nifti's?
>>
>> Thanks,
>> Stephen
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From a.stolk8 at gmail.com  Thu Sep 21 18:45:46 2017
From: a.stolk8 at gmail.com (Arjen Stolk)
Date: Thu, 21 Sep 2017 09:45:46 -0700
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
Message-ID: <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>

First thought is a registration of brain outline to skull (instead of
brain), although at closer inspection the shift seems overall just a bit
too large for that. You could try calculating the normalization parameters
on skullstripped volumes (unless you want to keep non-brain tissue).

On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
stephen.whitmarsh at gmail.com> wrote:

> Hi Arjen,
>
> Thanks, and good to hear you've not been let down yet. It might be the
> fact that I have some bad quality MRIs to deal with. However... does this
> problem (see attached) ring a bell for anyone?:
>
> Brain segmentation is proper, and co-registration with polhemus
>  head-shape as well, but inverse warp to MNI result in a tilted grid.
> Linear vs. non-linear transformation gives the same result. Other subjects
> going through the same procedure work fine, except two others wherein I
> identified it as a problem in segmenting the scalp and therefor the first
> step of the normalization. This one looks absolutely fine in every other
> regard, however.
>
> I'm stumped...
>
>     cfg                        = [];
>     cfg.spmversion          = 'spm12';
>     cfg.grid.warpmni        = 'yes';
>     cfg.grid.template       = template_grid;
>     cfg.grid.nonlinear      = 'yes';
>     cfg.mri                 = mri_realigned;
>     cfg.grid.unit           = 'mm';
>     subject_grid            = ft_prepare_sourcemodel(cfg);
>
> Cheers,
> Stephen
>
>
> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>
>> Hey Stephen,
>>
>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>> been a while but as far as I can remember ft_volumenormalize is the only
>> function now that has not been integrated. Reason being that it wasnt
>> straightforward to house the dartel procedure under a single function, so
>> this is ongoing work still. You can however use spm12's coregistration
>> function with ft_volumerealign (for rigid body transformations), which Im
>> using quite a bit and never let me down (and is much faster than before).
>> But that wouldnt work for normalization to template space though (use spm8).
>>
>> Best
>>
>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <hgould at memphis.edu>
>> wrote:
>>
>> I have retired please remove me from the mail list
>>
>> Herbert Jay Gould
>> Professor Emeritus
>> The University of Memphis
>>
>>
>>
>> Sent from my Verizon Wireless 4G LTE smartphone
>>
>>
>> -------- Original message --------
>> From: Stephen Whitmarsh
>> Date:09/21/2017 7:43 AM (GMT-06:00)
>> To: FieldTrip discussion list
>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>> normalization
>>
>> Dear Sarang and Jan-Mathijs,
>>
>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>> in /external), to use SPM12 for segmentation of my template and my subject
>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>> improvement over 8 when it comes to brain-segmentation, which now does not
>> require individual treatments anymore. It also outputs more compartments
>> which gives me a little bit more to work with when dealing with scans that
>> have bad delineation of the scalp for normalization.
>>
>> Pleas note that defaults seems to differ - some FT functions default to
>> spm8, others to spm12.
>>
>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>
>> Best wishes and thanks again!
>> Stephen
>>
>>
>>
>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>> jan.schoffelen at donders.ru.nl> wrote:
>>
>>> Hi Stephen,
>>>
>>> Please note that FT now has full support for SPM12, both using the
>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>> types).
>>>
>>> Best,
>>> Jan-Mathijs
>>>
>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com>
>>> wrote:
>>>
>>> Dear all,
>>>
>>> I having some problems in normalizing MRIs for my study. Some have
>>> improper segmentation for which changing individual brain/scalp thresholds
>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>> noise outside of the head. Also, changing parameters in spm8 for
>>> normalization, such as number of iterations (directly in in spm_normalize,
>>> since FT does not pass these parameters) improves the transformation.
>>>
>>> However, some scans I cannot deal with, either because they have noise
>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>> optimal scalp-segmentation and thereby normalization. Others have an
>>> inappropriate contrast MRI sequence.
>>>
>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>> Does anyone have experience with this, and can perhaps share how they
>>> extracted the transformation matrix from the resulting nifti's?
>>>
>>> Thanks,
>>> Stephen
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From sarang at cfin.au.dk  Thu Sep 21 19:30:25 2017
From: sarang at cfin.au.dk (Sarang S. Dalal)
Date: Thu, 21 Sep 2017 17:30:25 +0000
Subject: [FieldTrip] Using fixed orientations for men-source estimation
In-Reply-To: <71D8A67A81D69A4CB5BE2B979021C26ECAFFE748@esen3.imed.uni-magdeburg.de>
References: <71D8A67A81D69A4CB5BE2B979021C26ECAFFE748@esen3.imed.uni-magdeburg.de>
Message-ID: <1506015025.9072.22.camel@cfin.au.dk>

Hi Christian,

I had flagged your email to follow-up later but lost track of it -- it seems you didn't get a response yet, but I hope mine is still useful!

The strategy that the 'fixedori' implements for the beamformer variants (and sLORETA) are not based on the anatomical normal, but rather the direction that maximizes the theoretical SNR (adaptively determined from the signal characteristics). This optimal direction is dependent on the particular weight calculation formula for each source localization variant.

Therefore, a similar SNR optimization strategy for minimum norm would actually require a different formula than you see used for the others. It's simple enough to implement if you know what that formula is. :-) It is likely to be contained in the book by Sekihara & Nagarajan (2008), if you'd like to have a go at it yourself.

That said, min-norm is often (or perhaps usually) performed with the solution space constrained to gray matter voxels, and the orientations defined to be normal to the cortical surface. If you independently have a way to obtain anatomically derived orientations, then you can manually provide them in lf.ori. (Or maybe there is a FieldTrip function that could obtain these normals from the MRI segmentation procedure?)

Cheers,
Sarang



On Tue, 2017-08-01 at 11:11 +0000, christian.merkel at med.ovgu.de wrote:

Hello,


I am running ft_sourceanalysis and am wondering why I can restrict the parameter-estimation in LCMV and sLORETA by setting the parameter 'fixedori' but not when using MNE. Shouldn't one be able to also just use the normal direction of each source position here as well? Can I just apply the same logic in the script 'minimumnormestimate' to change the field 'lf.ori' as, for example, in 'ft_sloreta' or would this be problematic down the line?

Thank You,

Christian

_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Fri Sep 22 10:09:11 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 10:09:11 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
 <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
Message-ID: <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>

Hi Arjen,

Indeed, I do not think there is a problem with segmentation of brain/skull,
as can be seen on the image. Stripping some skin of two subjects
(thresholding the 'soft_tissue' probability output of SPM12, then removing
it) with a similar problem of solved the rotation, but resulted in too
small grids... On this subject I attached this procedure has no effect.

However, at least for those other subjects normalization on skullstripped,
or rather, scalpstripped MRIs might do the trick. As I understand it,
however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
with (affine) transformation based on the scalp first, after which it
optimizes it based on brain segmentation. I would not know how to therefor
do normalization without scalp. In fact, it expects a full volumetric
image, not a (pre-)segmented one.

Cheers,
Stephen

On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:

> First thought is a registration of brain outline to skull (instead of
> brain), although at closer inspection the shift seems overall just a bit
> too large for that. You could try calculating the normalization parameters
> on skullstripped volumes (unless you want to keep non-brain tissue).
>
> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com> wrote:
>
>> Hi Arjen,
>>
>> Thanks, and good to hear you've not been let down yet. It might be the
>> fact that I have some bad quality MRIs to deal with. However... does this
>> problem (see attached) ring a bell for anyone?:
>>
>> Brain segmentation is proper, and co-registration with polhemus
>>  head-shape as well, but inverse warp to MNI result in a tilted grid.
>> Linear vs. non-linear transformation gives the same result. Other subjects
>> going through the same procedure work fine, except two others wherein I
>> identified it as a problem in segmenting the scalp and therefor the first
>> step of the normalization. This one looks absolutely fine in every other
>> regard, however.
>>
>> I'm stumped...
>>
>>     cfg                        = [];
>>     cfg.spmversion          = 'spm12';
>>     cfg.grid.warpmni        = 'yes';
>>     cfg.grid.template       = template_grid;
>>     cfg.grid.nonlinear      = 'yes';
>>     cfg.mri                 = mri_realigned;
>>     cfg.grid.unit           = 'mm';
>>     subject_grid            = ft_prepare_sourcemodel(cfg);
>>
>> Cheers,
>> Stephen
>>
>>
>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>
>>> Hey Stephen,
>>>
>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>> been a while but as far as I can remember ft_volumenormalize is the only
>>> function now that has not been integrated. Reason being that it wasnt
>>> straightforward to house the dartel procedure under a single function, so
>>> this is ongoing work still. You can however use spm12's coregistration
>>> function with ft_volumerealign (for rigid body transformations), which Im
>>> using quite a bit and never let me down (and is much faster than before).
>>> But that wouldnt work for normalization to template space though (use spm8).
>>>
>>> Best
>>>
>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>> hgould at memphis.edu> wrote:
>>>
>>> I have retired please remove me from the mail list
>>>
>>> Herbert Jay Gould
>>> Professor Emeritus
>>> The University of Memphis
>>>
>>>
>>>
>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>
>>>
>>> -------- Original message --------
>>> From: Stephen Whitmarsh
>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>> To: FieldTrip discussion list
>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>> normalization
>>>
>>> Dear Sarang and Jan-Mathijs,
>>>
>>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>>> in /external), to use SPM12 for segmentation of my template and my subject
>>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>> require individual treatments anymore. It also outputs more compartments
>>> which gives me a little bit more to work with when dealing with scans that
>>> have bad delineation of the scalp for normalization.
>>>
>>> Pleas note that defaults seems to differ - some FT functions default to
>>> spm8, others to spm12.
>>>
>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>
>>> Best wishes and thanks again!
>>> Stephen
>>>
>>>
>>>
>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>> jan.schoffelen at donders.ru.nl> wrote:
>>>
>>>> Hi Stephen,
>>>>
>>>> Please note that FT now has full support for SPM12, both using the
>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>> types).
>>>>
>>>> Best,
>>>> Jan-Mathijs
>>>>
>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>
>>>> Dear all,
>>>>
>>>> I having some problems in normalizing MRIs for my study. Some have
>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>> since FT does not pass these parameters) improves the transformation.
>>>>
>>>> However, some scans I cannot deal with, either because they have noise
>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>> inappropriate contrast MRI sequence.
>>>>
>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>> Does anyone have experience with this, and can perhaps share how they
>>>> extracted the transformation matrix from the resulting nifti's?
>>>>
>>>> Thanks,
>>>> Stephen
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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From stephen.whitmarsh at gmail.com  Fri Sep 22 13:01:25 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 13:01:25 +0200
Subject: [FieldTrip] SPM12 for segmentation and (inverse) normalization
In-Reply-To: <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>
References: <CAFrxm=xnBQyyT6Oop9vCydkJJByKMMmiZn-4g+wdcUjpbTZbPA@mail.gmail.com>
 <2D212529-D5FF-4803-8A21-3A22F290A77B@donders.ru.nl>
 <CAFrxm=yyeqNgaYSPB2zySOJanY1ogobxqjwg-XSUuZMCLFhSSQ@mail.gmail.com>
 <udjegnn035qi2xabx8rc1qe1.1506002194456@email.android.com>
 <A8F481B7-A250-45DB-9104-101F25DAC733@gmail.com>
 <CAFrxm=z_pggMkY3B3xSTYagnK55XUbh0FurQVr=D-SfOJDNWuw@mail.gmail.com>
 <CAAiwptRDjJZdp33t3XOk-WWnXW+d7_5yRUU3K2QXgZxL6MrZEg@mail.gmail.com>
 <CAFrxm=xn8VaOmyCqz+PBdi1bKuoLKHRmzaCNmDbaB_NYRLHDdQ@mail.gmail.com>
Message-ID: <CAFrxm=yCDxWGxk6tusWb8aN09i8yujxpq8BgkVUcE9n0peSGJQ@mail.gmail.com>

Hi Arjen, Jan-Mathijs, et. al.,

It seems the rotation was caused by a bug my side. The segmentation using
SPM12 solved problems caused by low quality MRIS.
Thanks!
Stephen


On 22 September 2017 at 10:09, Stephen Whitmarsh <
stephen.whitmarsh at gmail.com> wrote:

> Hi Arjen,
>
> Indeed, I do not think there is a problem with segmentation of
> brain/skull, as can be seen on the image. Stripping some skin of two
> subjects (thresholding the 'soft_tissue' probability output of SPM12, then
> removing it) with a similar problem of solved the rotation, but resulted in
> too small grids... On this subject I attached this procedure has no effect.
>
> However, at least for those other subjects normalization on skullstripped,
> or rather, scalpstripped MRIs might do the trick. As I understand it,
> however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
> with (affine) transformation based on the scalp first, after which it
> optimizes it based on brain segmentation. I would not know how to therefor
> do normalization without scalp. In fact, it expects a full volumetric
> image, not a (pre-)segmented one.
>
> Cheers,
> Stephen
>
> On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>
>> First thought is a registration of brain outline to skull (instead of
>> brain), although at closer inspection the shift seems overall just a bit
>> too large for that. You could try calculating the normalization parameters
>> on skullstripped volumes (unless you want to keep non-brain tissue).
>>
>> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
>> stephen.whitmarsh at gmail.com> wrote:
>>
>>> Hi Arjen,
>>>
>>> Thanks, and good to hear you've not been let down yet. It might be the
>>> fact that I have some bad quality MRIs to deal with. However... does this
>>> problem (see attached) ring a bell for anyone?:
>>>
>>> Brain segmentation is proper, and co-registration with polhemus
>>>  head-shape as well, but inverse warp to MNI result in a tilted grid.
>>> Linear vs. non-linear transformation gives the same result. Other subjects
>>> going through the same procedure work fine, except two others wherein I
>>> identified it as a problem in segmenting the scalp and therefor the first
>>> step of the normalization. This one looks absolutely fine in every other
>>> regard, however.
>>>
>>> I'm stumped...
>>>
>>>     cfg                        = [];
>>>     cfg.spmversion          = 'spm12';
>>>     cfg.grid.warpmni        = 'yes';
>>>     cfg.grid.template       = template_grid;
>>>     cfg.grid.nonlinear      = 'yes';
>>>     cfg.mri                 = mri_realigned;
>>>     cfg.grid.unit           = 'mm';
>>>     subject_grid            = ft_prepare_sourcemodel(cfg);
>>>
>>> Cheers,
>>> Stephen
>>>
>>>
>>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>>
>>>> Hey Stephen,
>>>>
>>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>>> been a while but as far as I can remember ft_volumenormalize is the only
>>>> function now that has not been integrated. Reason being that it wasnt
>>>> straightforward to house the dartel procedure under a single function, so
>>>> this is ongoing work still. You can however use spm12's coregistration
>>>> function with ft_volumerealign (for rigid body transformations), which Im
>>>> using quite a bit and never let me down (and is much faster than before).
>>>> But that wouldnt work for normalization to template space though (use spm8).
>>>>
>>>> Best
>>>>
>>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>>> hgould at memphis.edu> wrote:
>>>>
>>>> I have retired please remove me from the mail list
>>>>
>>>> Herbert Jay Gould
>>>> Professor Emeritus
>>>> The University of Memphis
>>>>
>>>>
>>>>
>>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>>
>>>>
>>>> -------- Original message --------
>>>> From: Stephen Whitmarsh
>>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>>> To: FieldTrip discussion list
>>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>>> normalization
>>>>
>>>> Dear Sarang and Jan-Mathijs,
>>>>
>>>> Thanks a lot. I am now able (after updating FT, which now includes
>>>> SPM12 in /external), to use SPM12 for segmentation of my template and my
>>>> subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>>> require individual treatments anymore. It also outputs more compartments
>>>> which gives me a little bit more to work with when dealing with scans that
>>>> have bad delineation of the scalp for normalization.
>>>>
>>>> Pleas note that defaults seems to differ - some FT functions default to
>>>> spm8, others to spm12.
>>>>
>>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>>
>>>> Best wishes and thanks again!
>>>> Stephen
>>>>
>>>>
>>>>
>>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>>> jan.schoffelen at donders.ru.nl> wrote:
>>>>
>>>>> Hi Stephen,
>>>>>
>>>>> Please note that FT now has full support for SPM12, both using the
>>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>>> types).
>>>>>
>>>>> Best,
>>>>> Jan-Mathijs
>>>>>
>>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>>
>>>>> Dear all,
>>>>>
>>>>> I having some problems in normalizing MRIs for my study. Some have
>>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>>> since FT does not pass these parameters) improves the transformation.
>>>>>
>>>>> However, some scans I cannot deal with, either because they have noise
>>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>>> inappropriate contrast MRI sequence.
>>>>>
>>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>>> Does anyone have experience with this, and can perhaps share how they
>>>>> extracted the transformation matrix from the resulting nifti's?
>>>>>
>>>>> Thanks,
>>>>> Stephen
>>>>> _______________________________________________
>>>>> fieldtrip mailing list
>>>>> fieldtrip at donders.ru.nl
>>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>>
>>>>>
>>>>>
>>>>> _______________________________________________
>>>>> fieldtrip mailing list
>>>>> fieldtrip at donders.ru.nl
>>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>>
>>>> _______________________________________________
>>>> fieldtrip mailing list
>>>> fieldtrip at donders.ru.nl
>>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>>
>>>
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>
>
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From hamedtaheri at yahoo.com  Fri Sep 22 13:53:44 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Fri, 22 Sep 2017 11:53:44 +0000 (UTC)
Subject: [FieldTrip] Artifact Rejection Problem
References: <258456736.7597235.1506081224221.ref@mail.yahoo.com>
Message-ID: <258456736.7597235.1506081224221@mail.yahoo.com>

Hello Dear Fieldtrip users,
I have an EEG signal which I want to do artifact rejection on it.I've recorded the EEG during watching a video clip. 
My signal is 100 seconds and I've selected 50 seconds.I can find EOG artifact but I can't reject it. Would you please let me know how can I do it.


cfg  = [];
cfg.dataset   = 'myfile.eeg';  %BrainVision Recoreder EEG

cfg.trialdef.triallength   = inf;
cfg.trialdef.ntrials         = inf;
cfg   = ft_definetrial(cfg); trl     = cfg.trl;
data_org = ft_preprocessing(cfg);

%Select 30sec of data
cfg.latency        = 'all';
cfg.latency     = [0 30]; %start point and end point
cfg.avgovertime = 'no';
cfg.nanmean     = 'no';
data_s = ft_selectdata(cfg, data_org);
[cfg, artifact] = ft_artifact_eog(cfg,data_s);clean_data = ft_rejectartifact(cfg,data_s);
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From stephen.whitmarsh at gmail.com  Fri Sep 22 14:12:11 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 22 Sep 2017 14:12:11 +0200
Subject: [FieldTrip] Warnings on CentOS break code?
Message-ID: <CAFrxm=yerQMk1i4DMm1XJp9JEGyO6Q5hf7_qBLLqQcRZmrym1w@mail.gmail.com>

Hi there,

Since upgrading to the latest FT version, some warnings throw an error
because FT cannot determine it's in a CentOS distro. At least, that's what
I think it is?

Am I missing something?

Best,
Stephen

cfg = [];
cfg.artfctdef               = artdef_MEG{ipart};
cfg.artfctdef.reject        = 'partial';
cfg.artfctdef.minaccepttim  = 3;
data{ipart}                 = ft_rejectartifact(cfg,data{ipart});

results in:

Error using ft_platform_supports (line 134)
unsupported value for first argument: html

Error in ft_notification (line 376)
          if ft_platform_supports('html')

Error in ft_warning (line 63)
  ft_notification(varargin{:});

Error in getdimord>warning_dimord_could_not_be_determined (line 621)
  ft_warning('%s\n\n%s', msg,content);

Error in getdimord (line 572)
  warning_dimord_could_not_be_determined(field,data);

Error in ft_selectdata (line 201)
  dimord{i} = getdimord(varargin{1}, datfield{i});

Error in WANDER_common_filter_DICS (line 85)
    hdr = ft_selectdata(cfg,hdr);

134     error('unsupported value for first argument: %s', what);
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From hamedtaheri at yahoo.com  Fri Sep 22 19:06:53 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Fri, 22 Sep 2017 17:06:53 +0000 (UTC)
Subject: [FieldTrip] Artifact Removing Problem
References: <28989140.7825054.1506100013966.ref@mail.yahoo.com>
Message-ID: <28989140.7825054.1506100013966@mail.yahoo.com>

Hi all
I've tried to remove EOG, jump and muscle artifact from my EEG.I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts. ( No filter, remove) . I want to filter my signal no remove some part of mt signal. 
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.Could you please help me what is my wrong?
Best Regards,Hamed
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From hamedtaheri at yahoo.com  Sun Sep 24 17:37:43 2017
From: hamedtaheri at yahoo.com (Hamed Taheri)
Date: Sun, 24 Sep 2017 15:37:43 +0000 (UTC)
Subject: [FieldTrip] Artifact Rejection Problem
References: <383387083.4716793.1506267463295.ref@mail.yahoo.com>
Message-ID: <383387083.4716793.1506267463295@mail.yahoo.com>

Hi all
I've tried to remove EOG, jump and muscle artifact from my EEG.I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts. I want to filter my signal no remove some part of my signal. 
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.Could you please help me what is my wrong?
cfg.artfctdef.eog.artifact = artifact_EOG; 
cfg.artfctdef.jump.artifact = artifact_jump;
cfg.artfctdef.muscle.artifact = artifact_muscle;
cfg.artfctdef.reject = 'complet' ;
data_no_artifacts = ft_rejectartifact(cfg,data_int);


Best Regards,Hamed

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From mailtome.2113 at gmail.com  Mon Sep 25 03:23:35 2017
From: mailtome.2113 at gmail.com (Arti Abhishek)
Date: Mon, 25 Sep 2017 11:23:35 +1000
Subject: [FieldTrip] Question regarding clusterplot
Message-ID: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>

Dear list,

I am trying to plot significant clusters from the cluster based permutation
test on the ERPs. I want to plot the p values on a binary fashion (p<.05).
I just don't want to highlight the electrodes, but I want to interpolate
the p values and plot topography (just like ERP topography, but in abinary
fashion). I was wondering whether there is a way to do it?

Thanks,
Arti
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From tzvetan.popov at uni-konstanz.de  Mon Sep 25 06:29:05 2017
From: tzvetan.popov at uni-konstanz.de (Tzvetan Popov)
Date: Mon, 25 Sep 2017 06:29:05 +0200
Subject: [FieldTrip] Question regarding clusterplot
In-Reply-To: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>
References: <CA+N_CJrAbCrm7eumKWtaqGYQVrUi_H8aJ7SXg22AyoQ5s_bzHA@mail.gmail.com>
Message-ID: <BCD13C5C-6A8F-4931-BF0A-4BC1E4ABCF05@uni-konstanz.de>

Hi Arti,

you could specify cfg.parameter = ‘mask’; instead of ‘avg’ which is the default.

best
tzvetan

> Am 25.09.2017 um 03:23 schrieb Arti Abhishek <mailtome.2113 at gmail.com>:
> 
> Dear list,
> 
> I am trying to plot significant clusters from the cluster based permutation test on the ERPs. I want to plot the p values on a binary fashion (p<.05). I just don't want to highlight the electrodes, but I want to interpolate the p values and plot topography (just like ERP topography, but in abinary fashion). I was wondering whether there is a way to do it?
> 
> Thanks,
> Arti
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip




From simeon.wong at sickkids.ca  Mon Sep 25 16:45:04 2017
From: simeon.wong at sickkids.ca (Simeon Wong)
Date: Mon, 25 Sep 2017 14:45:04 +0000
Subject: [FieldTrip] Artifact Rejection Problem
In-Reply-To: <383387083.4716793.1506267463295@mail.yahoo.com>
References: <383387083.4716793.1506267463295.ref@mail.yahoo.com>,
 <383387083.4716793.1506267463295@mail.yahoo.com>
Message-ID: <YTXPR0101MB1456A96D8CE2B437CDEFE9A3FD7A0@YTXPR0101MB1456.CANPRD01.PROD.OUTLOOK.COM>

Hi Hamed,

I believe ft_rejectartefact does not offer an option to simply remove any artefact. Removing artefacts is actually a non-trivial process that you can approach from several different ways.

Try using ft_componentanalysis to apply ICA to remove eye blinks and some eye movement. I'm not too sure about muscle artefact in EEG but perhaps setting a bandpass filter from 1-30 Hz may help attenuate most of it. You probably don't need to worry about jump artifacts since that typically applies only to MEG.


Regards,
Simeon Wong
________________________________
From: fieldtrip-bounces at science.ru.nl <fieldtrip-bounces at science.ru.nl> on behalf of Hamed Taheri <hamedtaheri at yahoo.com>
Sent: September 24, 2017 11:37:43 AM
To: FieldTrip Discussion List
Subject: [FieldTrip] Artifact Rejection Problem

Hi all

I've tried to remove EOG, jump and muscle artifact from my EEG.
I can find the artifact but when I use ft_rejectartifact, it removes some parts of EEG that contaminated by artifacts.
I want to filter my signal no remove some part of my signal.
When I use artifact removing in EEGLab or BrainStorm just artifact removed no the contaminated part of EEG.
Could you please help me what is my wrong?

cfg.artfctdef.eog.artifact = artifact_EOG;
cfg.artfctdef.jump.artifact = artifact_jump;
cfg.artfctdef.muscle.artifact = artifact_muscle;
cfg.artfctdef.reject = 'complet' ;
data_no_artifacts = ft_rejectartifact(cfg,data_int);


Best Regards,
Hamed


________________________________

This e-mail may contain confidential, personal and/or health information(information which may be subject to legal restrictions on use, retention and/or disclosure) for the sole use of the intended recipient. Any review or distribution by anyone other than the person for whom it was originally intended is strictly prohibited. If you have received this e-mail in error, please contact the sender and delete all copies.



From bqrosen at ucsd.edu  Mon Sep 25 22:05:39 2017
From: bqrosen at ucsd.edu (Burke Rosen)
Date: Mon, 25 Sep 2017 13:05:39 -0700
Subject: [FieldTrip] ft_combineplanar 'svd' method
Message-ID: <C10A6011-0A3E-419C-8396-E238EFA66428@ucsd.edu>

Hello,

What is the principle behind the ‘svd’ and ‘absvd’ methods for ft_combineplanar?
And/or is there a citation which introduces these methods?

Thank you,

Burke Rosen







From michak at is.umk.pl  Mon Sep 25 23:44:00 2017
From: michak at is.umk.pl (=?UTF-8?Q?Micha=C5=82_Komorowski?=)
Date: Mon, 25 Sep 2017 23:44:00 +0200
Subject: [FieldTrip] AAL Surface plot - weird black spots
In-Reply-To: <CAEJSOqhQCVyPfR1woRC69a8s1cgJimyfe3GBEuc38Uds9cVRbg@mail.gmail.com>
References: <CAEJSOqhQCVyPfR1woRC69a8s1cgJimyfe3GBEuc38Uds9cVRbg@mail.gmail.com>
Message-ID: <CAEJSOqg7wr4xn1b19sr1TKbOLMvpKAj+nFnZR4QONf_=_rFZHw@mail.gmail.com>

Dear Fieldtrippers,

I found the solution and it is simple. Just make sure that you have in your
config follwing lines:

cfg.projmethod = 'project'
cfg.projvec = [0 5]

Have a nice plots !


Michał Komorowski, MSc
Nicolaus Copernicus University in Toruń
Faculty of Physics, Astronomy and Informatics
Department of Informatics

2017-07-31 14:15 GMT+02:00 Michał Komorowski <michak at is.umk.pl>:

> Dear Fieldtrippers,
>
> I am trying to reproduce brain surface pictures from this paper (Fig.5) :
> http://journals.plos.org/plosbiology/article?id=10.
> 1371/journal.pbio.1002498
>
> I wonder why I get weid black spots in surface plot (e.g. occipital area).
> What should I do to get those nice picures from link above?
> What I am doing wrong (code below)?
>
> Code for generating erroneous pictures (see attachment):
>
> mrifile             = [FieldtripPath 'template/anatomy/single_subj_
> T1.nii']
>
> mri = ft_read_mri(mrifile)
> mri.coordsys = 'mni'; % to prevent manual fixing of coordsys
>
> atlaspath       = [FieldtripPath 'template/atlas/aal/ROI_MNI_V4.nii'];
> atlas           = ft_read_atlas(atlaspath)
> atlas.anatomy   = mri.anatomy;
>
> cfg                     = [];
> cfg.method              = 'surface';
> cfg.projmethod          = 'project';
> cfg.camlight            = 'yes';
> %cfg.surffile = [FieldtripPath 'template/anatomy/surface_pial_left.mat'];
> % uncomment to project half brain
> cfg.locationcoordinates = 'voxel';
> cfg.cmap                = jet(116);
> cfg.cmap                = [[0,0,0]; cfg.cmap]
> cfg.funcolormap         = cfg.cmap;
> cfg.funparameter        = 'tissue';
> cfg.atlas               = atlaspath;
> ft_sourceplot(cfg, atlas)
>
>
> % check fit anatomy to atlas
> cfg                     = [];
> cfg.method              = 'ortho';
> cfg.locationcoordinates = 'voxel';
> cfg.cmap                = jet(116);
> cfg.cmap                = [[0,0,0]; cfg.cmap] % color map
> cfg.funcolormap         = cfg.cmap;
> cfg.funparameter        = 'tissue';
> ft_sourceplot(cfg, atlas)
>
> Best wishes.
>
> Michał Komorowski
>
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From nirofir2 at gmail.com  Tue Sep 26 15:12:30 2017
From: nirofir2 at gmail.com (Nir Ofir)
Date: Tue, 26 Sep 2017 16:12:30 +0300
Subject: [FieldTrip] Variable Number of Tapers in 'mtmfft' Frequency Analysis
Message-ID: <CAKiBOH=U2m3JYMrpitfbLi-bagc+yu0fw1yK2tJ7Lj8YoLZuuA@mail.gmail.com>

Hi Fieldtrip users,

ft_freqanalysis (FT version 20170404) does not allow using a variable
number of tapers in 'mtmfft' mode (lines 462-465 display a warning and keep
only the first element of cfg.tapsmofrq), but it seems like
ft_specest_mtmfft does have an implementation of a variable number of
tapers (lines 286-348). It also seems like mtm_specest_mtmconvol, which
allows variable number of tapers, calls ft_specest_mtmfft. So 2 questions:
1. Is the variable number of tapers option used in mtmfft in some other way?
2. What is the reason for not allowing a variable number of tapers in
mtmfft generally?

Thanks!
Nir Ofir
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From bog.louisa at gmail.com  Wed Sep 27 22:01:53 2017
From: bog.louisa at gmail.com (Louisa Bogaerts)
Date: Wed, 27 Sep 2017 23:01:53 +0300
Subject: [FieldTrip] reading in and preprocessing gtec_mat data
Message-ID: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>

Hello everyone,



In the lab or Dr. Landau we recently started using a *g.tech EEG setup* and
*Simulink* record the data. We used the newest version of Fieldtrip to try
analyze the data.



Simulink outputs the data as a .mat file (an example here:
https://www.dropbox.com/s/6xgio9w81qx94bq/example.mat?dl=0), and according
to the fieldtrip documentation this data format should now be supported: e.g.,
https://github.com/fieldtrip/fieldtrip/blob/master/fileio/ft_read_data.m,
lines 274-276:

if any(strcmp(dataformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat',

'gtec_hdf5', 'mega_neurone'}))



However, it seems that multiple Fieldtrip functions are “looking” for a
header file that is not found.

-   When reading in the data with ft_read_data() we get the following error
messages (whereas simply loading them with load() works fine):

Error using ft_notification (line 340)

unsupported header format "matlab"



Error in ft_error (line 39)

  ft_notification(varargin{:});



Error in ft_read_header (line 2325)

        ft_error('unsupported header format "%s"', headerformat);



Error in ft_read_data (line 200)

  hdr = ft_read_header(filename, 'headerformat', headerformat, 'chanindx',

  chanindx, 'checkmaxfilter', checkmaxfilter);



- The same error messages show when using ft_preprocessing().



Does anyone have experience reading in and preprocessing gtech_mat data and
can he/she help us understand how to save the header info so that fieldtrip
can read it and recognise the data as gtec_mat?



Any help will be very much appreciated.



Louisa, Omri & Flor
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From isac.sehlstedt at psy.gu.se  Fri Sep 29 12:19:51 2017
From: isac.sehlstedt at psy.gu.se (Isac Sehlstedt)
Date: Fri, 29 Sep 2017 10:19:51 +0000
Subject: [FieldTrip] Follow up question: Computing pca variables (i.e.
 Latent, and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
References: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
Message-ID: <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>

Dear fieldtripers,


This is a kind reminder of a follow-up question to a previous question with the same mail-topic.


I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.


Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?


Pictures are found using this link:

https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0


Very Best,

Isac



————————————————— The code —————————————————


clear all; close all;


%% Load

load('averages_for_ft.mat')




%% define layout

cfg = [];

cfg.elec=PreOdd_ft{1, 1}.elec;

cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)

layout = ft_prepare_layout(cfg)




%% Make the computations


% Dummy varibles

Cond1 = [];

Cond2 = [];

theDiff = [];

theDiff_ft = {};



%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions

    curr_Cond2 = Cond2_ft{i}.avg;

    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions

    cfg = [];

    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});

    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math

    cfg  = [];

    cfg.operation = 'subtract';

    cfg.parameter = 'avg';

    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);

    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions

    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.

    Cond2 = [Cond2 curr_Cond2];

    Cond1 = [Cond1 curr_Cond1];

    theDiff = [theDiff curr_difference_avg];

end




%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};

dummy_Cond2.avg = Cond2;

dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};

dummy_Cond1.avg = Cond1;

dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};

dummy_theDiff.avg = theDiff;

dummy_theDiff.time = 1:1:size(theDiff,2);




%% Run the PCA



cfg = [];

cfg.method  = 'pca';

cfg.layout = layout;


Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);

Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);

theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);




%% Revert back to subject level


cfgCond2 = [];

cfgCond2.unmixing = Cond2_comp.unmixing;

cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];

cfgCond1.unmixing = Cond1_comp.unmixing;

cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];

cfgtheDiff.unmixing = theDiff_comp.unmixing;

cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)

    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});

    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});

    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );

end
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From jan.schoffelen at donders.ru.nl  Fri Sep 29 12:36:30 2017
From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Fri, 29 Sep 2017 10:36:30 +0000
Subject: [FieldTrip] Follow up question: Computing pca variables (i.e.
 Latent, and coefficient variables) after ft_componentanalysis
In-Reply-To: <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>
References: <D5F28712.197EE%isac.sehlstedt@psy.gu.se>
 <D5F3E8C2.19C7F%isac.sehlstedt@psy.gu.se>
Message-ID: <97B82971-ABD0-49F2-81C4-235AE4D09162@donders.ru.nl>

perhaps you may want to check the ‘avg’ field.

JM

On 29 Sep 2017, at 12:19, Isac Sehlstedt <isac.sehlstedt at psy.gu.se<mailto:isac.sehlstedt at psy.gu.se>> wrote:

Dear fieldtripers,

This is a kind reminder of a follow-up question to a previous question with the same mail-topic.

I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.

Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?

Pictures are found using this link:
https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0

Very Best,
Isac


————————————————— The code —————————————————

clear all; close all;

%% Load
load('averages_for_ft.mat')



%% define layout
cfg = [];
cfg.elec=PreOdd_ft{1, 1}.elec;
cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)
layout = ft_prepare_layout(cfg)



%% Make the computations

% Dummy varibles
Cond1 = [];
Cond2 = [];
theDiff = [];
theDiff_ft = {};


%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions
    curr_Cond2 = Cond2_ft{i}.avg;
    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions
    cfg = [];
    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});
    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math
    cfg  = [];
    cfg.operation = 'subtract';
    cfg.parameter = 'avg';
    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);
    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions
    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.
    Cond2 = [Cond2 curr_Cond2];
    Cond1 = [Cond1 curr_Cond1];
    theDiff = [theDiff curr_difference_avg];
end



%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};
dummy_Cond2.avg = Cond2;
dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};
dummy_Cond1.avg = Cond1;
dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};
dummy_theDiff.avg = theDiff;
dummy_theDiff.time = 1:1:size(theDiff,2);



%% Run the PCA



cfg = [];
cfg.method  = 'pca';
cfg.layout = layout;

Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);
Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);
theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);



%% Revert back to subject level

cfgCond2 = [];
cfgCond2.unmixing = Cond2_comp.unmixing;
cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];
cfgCond1.unmixing = Cond1_comp.unmixing;
cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];
cfgtheDiff.unmixing = theDiff_comp.unmixing;
cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)
    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});
    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});
    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );
end
_______________________________________________
fieldtrip mailing list
fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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From stephen.whitmarsh at gmail.com  Fri Sep 29 12:42:41 2017
From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh)
Date: Fri, 29 Sep 2017 12:42:41 +0200
Subject: [FieldTrip] reading in and preprocessing gtec_mat data
In-Reply-To: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>
References: <CAHtfx3_ne55YqqrWUhdizN+vqRD8pRpR6wDh-oDWyo5bo3Mmww@mail.gmail.com>
Message-ID: <CAFrxm=w7zoX0bberQ2t-tzAmCCRjy9YXM3noDsaapZ7OozM8WQ@mail.gmail.com>

Hi Louisa et al,

It seems that you are actually not trying to read data in the Gtec data
format, but that of simulink (which was saved as a mat file, as a matlab
file).
So, you should be able to just read your mat file and then put your data in
a fieldtrip data structure.

See: http://www.fieldtriptoolbox.org/faq/how_can_i_import_my_own_dataformat

Cheers,
Stephen










On 27 September 2017 at 22:01, Louisa Bogaerts <bog.louisa at gmail.com> wrote:

> Hello everyone,
>
>
>
> In the lab or Dr. Landau we recently started using a *g.tech EEG setup*
> and *Simulink* record the data. We used the newest version of Fieldtrip
> to try analyze the data.
>
>
>
> Simulink outputs the data as a .mat file (an example here:
> https://www.dropbox.com/s/6xgio9w81qx94bq/example.mat?dl=0), and
> according to the fieldtrip documentation this data format should now be
> supported: e.g., https://github.com/fieldtrip/f
> ieldtrip/blob/master/fileio/ft_read_data.m, lines 274-276:
>
> if any(strcmp(dataformat, {'bci2000_dat', 'eyelink_asc', 'gtec_mat',
>
> 'gtec_hdf5', 'mega_neurone'}))
>
>
>
> However, it seems that multiple Fieldtrip functions are “looking” for a
> header file that is not found.
>
> -   When reading in the data with ft_read_data() we get the following
> error messages (whereas simply loading them with load() works fine):
>
> Error using ft_notification (line 340)
>
> unsupported header format "matlab"
>
>
>
> Error in ft_error (line 39)
>
>   ft_notification(varargin{:});
>
>
>
> Error in ft_read_header (line 2325)
>
>         ft_error('unsupported header format "%s"', headerformat);
>
>
>
> Error in ft_read_data (line 200)
>
>   hdr = ft_read_header(filename, 'headerformat', headerformat, 'chanindx',
>
>   chanindx, 'checkmaxfilter', checkmaxfilter);
>
>
>
> - The same error messages show when using ft_preprocessing().
>
>
>
> Does anyone have experience reading in and preprocessing gtech_mat data
> and can he/she help us understand how to save the header info so that
> fieldtrip can read it and recognise the data as gtec_mat?
>
>
>
> Any help will be very much appreciated.
>
>
>
> Louisa, Omri & Flor
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
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