From tigoum at naver.com Wed Nov 2 08:59:45 2016 From: tigoum at naver.com (=?UTF-8?B?7JWI66+87Z2s?=) Date: Wed, 2 Nov 2016 16:59:45 +0900 (KST) Subject: [FieldTrip] =?utf-8?q?_ft=5Fsourceanalysis_with_specific_EEG_data?= In-Reply-To: References: Message-ID: <341aa5e8a982eed4ceab879e19952f@cweb26.nm.nhnsystem.com> 대용량 첨부파일 1개(166MB)대용량 첨부 파일은 30일간 보관 / 100회까지 다운로드 가능 brainvision_EEG.zip 166MB 다운로드 기간: 2016/11/02 ~ 2016/12/02 Hello? I am a graduate student in Korea university , Korea. I have a own data that are exported from brainvision analyzer.It is consisting of 3 dimension such as 5 second interval, 240 epoch, 32 channel. I hope so analyzing by ft_sourceanalysis().Then I search the getting started & User documentation on fieldtrip homepage. And I found related information as "networkanalysis", it explain the usage of ft_sourceanalysiswith example MATLAB code. BUT, it is constructed for MEG dataset only, so I do not trying for my own EEG data as mentioned before. In the attached file, It include "networkanalysis.m" and my own data file.The "networkanalysis.m" is written by me as referred from fieldtrip homepage:http://www.fieldtriptoolbox.org/tutorial/networkanalysis?s[]=networkanalysis I really need your help. I really hope so analyzing my data by ft_sourceanalysis & eLORETA option. Please help me.Thank you very much. Best regards.Min-Hee, Ahn -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: networkanalysis.zip Type: application/x-zip-compressed Size: 10283 bytes Desc: not available URL: From Ramirez_U at ukw.de Wed Nov 2 11:41:11 2016 From: Ramirez_U at ukw.de (Ramirez Pasos, ) Date: Wed, 2 Nov 2016 10:41:11 +0000 Subject: [FieldTrip] Smoothing before permutation test Message-ID: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> Dear Fieldtrippers, I have a set of subcortical LFP signals from 8 patients, which I have analyzed with fieldtrip in order to obtain event-related time-frequency plots. Unfortunately, I have only three repetitions for each of my 4 conditions, so there's a lot of noise in the subject-averages of time-frequency data. I'm interested in testing baseline vs activation for each condition as well as comparisons between my 4 conditions (A1A2, A1B1, B1B2, A2B2). Could anyone tell me their take on smoothing before permuting? Is that a valid procedure with such a small sample size? I've searched for literature discussing smoothing before permuting (mostly the Holmes papers), where much talk of smoothing refers to concepts such as "locally pooled variance" and "pseudo t-statistics," but I don't know how this fits with fieldtrip's cluster statistics functions. Any thoughts would be greatly appreciated! U. Ramirez University of Wuerzburg From david.m.groppe at gmail.com Wed Nov 2 14:43:18 2016 From: david.m.groppe at gmail.com (David Groppe) Date: Wed, 2 Nov 2016 09:43:18 -0400 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> Message-ID: Since permutation tests exploit correlations between variables to increase sensitivity, smoothing each trial will increase your sensitivity. cheers, -David On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, wrote: > Dear Fieldtrippers, > > I have a set of subcortical LFP signals from 8 patients, which I have > analyzed with fieldtrip in order to obtain event-related time-frequency > plots. Unfortunately, I have only three repetitions for each of my 4 > conditions, so there's a lot of noise in the subject-averages of > time-frequency data. I'm interested in testing baseline vs activation for > each condition as well as comparisons between my 4 conditions (A1A2, A1B1, > B1B2, A2B2). > > Could anyone tell me their take on smoothing before permuting? Is that a > valid procedure with such a small sample size? I've searched for literature > discussing smoothing before permuting (mostly the Holmes papers), where > much talk of smoothing refers to concepts such as "locally pooled variance" > and "pseudo t-statistics," but I don't know how this fits with fieldtrip's > cluster statistics functions. > > Any thoughts would be greatly appreciated! > > U. Ramirez > University of Wuerzburg > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Wed Nov 2 17:29:50 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Wed, 02 Nov 2016 16:29:50 +0000 Subject: [FieldTrip] Time course for MNE In-Reply-To: References: Message-ID: Hello, I would like to compute the time course in the source , so far the source has been constructed but source.avg.filter has 1x24024 cells which each cell is 2x241, which 241 is the number of MEG sensors. Right now I cant figure out how the time course for each source can be computed. I appreciate if anybody can help. Best Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From Ramirez_U at ukw.de Wed Nov 2 18:02:00 2016 From: Ramirez_U at ukw.de (Ramirez Pasos, ) Date: Wed, 2 Nov 2016 17:02:00 +0000 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de>, Message-ID: <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> Thank you David for your response! Just to be clear: by "smoothing" I'm not referring to the smoothing performed when using the ft_frequencyanalysis multitaper method, but some kind of spatial smoothing on the time-frequency matrix obtained via ft_frequencyanalysis - so that according to your suggestion, I would smooth each trial, then average trials for each subject/condition, and finally use these for statistical evaluation? Is there a reason why smoothing each trial might be preferable to smoothing each subject's trial average? Thank you so much in advance, Uri ________________________________ Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]" im Auftrag von "David Groppe [david.m.groppe at gmail.com] Gesendet: Mittwoch, 2. November 2016 14:43 An: FieldTrip discussion list Betreff: Re: [FieldTrip] Smoothing before permutation test Since permutation tests exploit correlations between variables to increase sensitivity, smoothing each trial will increase your sensitivity. cheers, -David On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, > wrote: Dear Fieldtrippers, I have a set of subcortical LFP signals from 8 patients, which I have analyzed with fieldtrip in order to obtain event-related time-frequency plots. Unfortunately, I have only three repetitions for each of my 4 conditions, so there's a lot of noise in the subject-averages of time-frequency data. I'm interested in testing baseline vs activation for each condition as well as comparisons between my 4 conditions (A1A2, A1B1, B1B2, A2B2). Could anyone tell me their take on smoothing before permuting? Is that a valid procedure with such a small sample size? I've searched for literature discussing smoothing before permuting (mostly the Holmes papers), where much talk of smoothing refers to concepts such as "locally pooled variance" and "pseudo t-statistics," but I don't know how this fits with fieldtrip's cluster statistics functions. Any thoughts would be greatly appreciated! U. Ramirez University of Wuerzburg _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From david.m.groppe at gmail.com Wed Nov 2 18:33:01 2016 From: david.m.groppe at gmail.com (David Groppe) Date: Wed, 2 Nov 2016 13:33:01 -0400 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> Message-ID: Smoothing a time-frequency matrix is just as valid. I would apply the smoothing to whatever it is you are plugging into the permutation test as an independent observation (in your case it sounds like trial averages). -D On Wed, Nov 2, 2016 at 1:02 PM, Ramirez Pasos, wrote: > Thank you David for your response! Just to be clear: by "smoothing" I'm > not referring to the smoothing performed when using the > ft_frequencyanalysis multitaper method, but some kind of spatial smoothing > on the time-frequency matrix obtained via ft_frequencyanalysis - so that > according to your suggestion, I would smooth each trial, then average > trials for each subject/condition, and finally use these for statistical > evaluation? Is there a reason why smoothing each trial might be preferable > to smoothing each subject's trial average? > > Thank you so much in advance, > Uri > ________________________________ > Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]" > im Auftrag von "David Groppe [david.m.groppe at gmail.com] > Gesendet: Mittwoch, 2. November 2016 14:43 > An: FieldTrip discussion list > Betreff: Re: [FieldTrip] Smoothing before permutation test > > Since permutation tests exploit correlations between variables to increase > sensitivity, smoothing each trial will increase your sensitivity. > cheers, > -David > > On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, Ramirez_U at ukw.de>> wrote: > Dear Fieldtrippers, > > I have a set of subcortical LFP signals from 8 patients, which I have > analyzed with fieldtrip in order to obtain event-related time-frequency > plots. Unfortunately, I have only three repetitions for each of my 4 > conditions, so there's a lot of noise in the subject-averages of > time-frequency data. I'm interested in testing baseline vs activation for > each condition as well as comparisons between my 4 conditions (A1A2, A1B1, > B1B2, A2B2). > > Could anyone tell me their take on smoothing before permuting? Is that a > valid procedure with such a small sample size? I've searched for literature > discussing smoothing before permuting (mostly the Holmes papers), where > much talk of smoothing refers to concepts such as "locally pooled variance" > and "pseudo t-statistics," but I don't know how this fits with fieldtrip's > cluster statistics functions. > > Any thoughts would be greatly appreciated! > > U. Ramirez > University of Wuerzburg > > > _______________________________________________ > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Thu Nov 3 17:27:40 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Thu, 3 Nov 2016 16:27:40 +0000 Subject: [FieldTrip] Permutation test with low amount of subjects participating in several sessions Message-ID: Hello Fieldtrip experts, currently I'm working on an intracranial dataset with low amount of subjects who participated repeatedly in an experiment with two different conditions. In detail: * LFP data from only three subjects. * Each subject participated several times in the same experiment (about 16 sessions per subject). * In every session subjects performed around 50 trials of condition A and around 50 trials of condition B I calculated time-frequency spectra (TFS) for the LFP's. To test if there are significant differences between the TFS(condition A) and TFS(condition B) I want to use the permutation test implemented in fieldtrip. Unfortunately I'm struggling with my little statistic knowledge, because of the low amount of subjects and the high repetition rate for every subject in multiple sessions. Here is what I have done so far, and it would be great if anyone could tell me if it is correct or totally bullshit. First I averaged over all trials, so I put one TFS for each condition and session in the statistic. The first row of the design matrix represent the repetition of the single subjects (in this case three) and the second row of the design matrix contains the two conditions A (as 1) and B (as 2). cfg = []; cfg.parameter = 'powspctrm'; cfg.numrandomization = 5000; cfg.method = 'montecarlo'; cfg.correctm = 'fdr'; cfg.alpha = 0.05; cfg.correcttail = 'prob'; cfg.ivar = 2; cfg.uvar = 1; cfg.statistic = 'ft_statfun_depsamplesT'; design = [1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]; cfg.design = design; stat = ft_freqstatistics(cfg, data_A, data_B); Thanks in advance! Iris -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Thu Nov 3 20:11:32 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 03 Nov 2016 19:11:32 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: References: Message-ID: Hello all, By using the data of HCP, and conduct eLORETA to compute the inverse problem, right now Id like to visualise the results to surface but due to not having any anatomical information saved in the matrix it does not show any mapping. Moreover, Id like to parcellate the cortex based on the AAL atlas, so I really appreciate if anybody can help me. Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 3 20:53:25 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 3 Nov 2016 19:53:25 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: References: Message-ID: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> Hi Mehdy, It is not clear to me what you want to achieve. It’s unclear what you mean with ‘the results’ to be visualized on ‘the surface’, and that you have no ‘anatomical information’ saved in ‘the matrix’, and that there is a lack of ‘any mapping’. All terms between the quotation signs (for the readers among us who understand Dutch: my daughter aptly calls these things ‘bovenkomma’tjes’) are not defined precisely enough by you to give pointers about what or whether anything is missing, or whether something goes wrong. In general, source-level data can be parcellated with ft_sourceparcellate, but only if your atlas is in the same space as your functional data. That is, there should be a one-to-one mapping between the source locations in your functional data, and the source locations in your atlas. If you want to use the AAL atlas, which is essentially defined as a volumetric image (probably at a voxel resolution of 1 or 2 mm), you need to interpolate/downsample this atlas onto your sourcemodel at the appropriate resolution .This would make most sense if your sourcemodel is also defined as a 3D grid, but it is not absolutely necessary. In order to interpolate the atlas onto your sourcemodel, you could use ft_sourceinterpolate (provided both atlas and sourcemodel are defined in the same coordinate system). Note that from your messages on this forum and on the HCP discussion list it is not clear to the reader what source model you used for the eLORETA. There is some information on the fieldtrip wiki that illustrates how to parcellate source reconstructed data http://www.fieldtriptoolbox.org/tutorial/networkanalysis In the example, it uses a surface-based parcellation, and parcellates a connectivity matrix. The function can also parcellate univariate data (e.g. time courses or power spectra), either or not defined on a 3D grid. Also, the HCP software+documentation that the MEG team released, and which accompanies the released data, might give you some pointers on how to do it. You could try and adapt the code provided to your own needs. Good luck, Jan-Mathijs J.M.Schoffelen Senior Researcher Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > On 03 Nov 2016, at 20:11, mehdy dousty wrote: > > Hello all, > By using the data of HCP, and conduct eLORETA to compute the inverse problem, right now Id like to visualise the results to surface but due to not having any anatomical information saved in the matrix it does not show any mapping. Moreover, Id like to parcellate the cortex based on the AAL atlas, so I really appreciate if anybody can help me. > Thanks > Mehdy > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From mehdy.dousty at gmail.com Thu Nov 3 21:14:51 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 03 Nov 2016 20:14:51 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> References: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> Message-ID: Hello, Thanks for the answer and sorry for my vague explanation. here is my code to compute the inverse problem by eLORETA using the provided MRI, DATA and Source model in HCP. % source localization for resting state HCP. load('100307_MEG_3-Restin_rmegpreproc.mat') ; % loading the data; load('100307_MEG_anatomy_headmodel.mat');% loading the headmodel tmp = load('100307_MEG_anatomy_sourcemodel_3d6mm.mat');% sourcemodel by 6mm individual_sourcemodel3d = tmp.sourcemodel3d; %% MRI individual_mri = ft_read_mri('T1w_acpc_dc_restore.nii.gz'); hcp_read_ascii('100307_MEG_anatomy_transform.txt'); individual_mri.transform = transform.vox07mm2bti; individual_mri.coordsys = 'bti'; %% converting to the same coordination unit individual_mri = ft_convert_units(individual_mri,'mm'); individual_sourcemodel3d = ft_convert_units(individual_sourcemodel3d,'mm'); headmodel = ft_convert_units(headmodel,'mm'); data.grad = ft_convert_units(data.grad,'mm'); %% leadfield matrix cfg = []; cfg.grid = individual_sourcemodel3d; cfg.headmodel = headmodel; cfg.grad = data.grad; cfg.channel = ft_channelselection('MEG',data.label); cfg.reducerank = 'no'; leadfield = ft_prepare_leadfield(cfg); %% timelcok cfg = []; cfg.covariance = 'yes'; cfg.covariancewindow = [0 1000]; cfg.keeptrials = 'yes'; timelockanalaysis = ft_timelockanalysis(cfg,data); %% Eloreta cfg = []; cfg.method = 'eloreta'; cfg.vol = headmodel; cfg.grid = leadfield; cfg.eloreta.lambda = 0.05; %Regularization parameters,cross-validation can be %used but as it resting state and we really dont know what the outupt looks %like then we have to use emprical numbers which cfg.mne.projectnoise = 'yes'; cfg.keepmom = 'yes'; %keep dipole moment cfg.mne.keepmom = 'yes'; cfg.senstype = 'meg'; cfg.keepfilter = 'yes'; cfg.eloreta.reducerank = 'no'; source_eloreta = ft_sourceanalysis (cfg, timelockanalaysis); so far , I compute the eLORETA, now I would like to project the data into 3d surface, like the way it was done for beamformer in http://www.fieldtriptoolbox.org/tutorial/plotting' and then make a parcellation based on some predefined atlases and then do the other analysis. I do appreciate for your helping. Thanks On Thu, Nov 3, 2016 at 1:53 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Mehdy, > > It is not clear to me what you want to achieve. It’s unclear what you mean > with ‘the results’ to be visualized on ‘the surface’, and that you have no > ‘anatomical information’ saved in ‘the matrix’, and that there is a lack of > ‘any mapping’. All terms between the quotation signs (for the readers among > us who understand Dutch: my daughter aptly calls these things > ‘bovenkomma’tjes’) are not defined precisely enough by you to give pointers > about what or whether anything is missing, or whether something goes wrong. > > In general, source-level data can be parcellated with ft_sourceparcellate, > but only if your atlas is in the same space as your functional data. That > is, there should be a one-to-one mapping between the source locations in > your functional data, and the source locations in your atlas. If you want > to use the AAL atlas, which is essentially defined as a volumetric image > (probably at a voxel resolution of 1 or 2 mm), you need to > interpolate/downsample this atlas onto your sourcemodel at the appropriate > resolution .This would make most sense if your sourcemodel is also defined > as a 3D grid, but it is not absolutely necessary. In order to interpolate > the atlas onto your sourcemodel, you could use ft_sourceinterpolate > (provided both atlas and sourcemodel are defined in the same coordinate > system). Note that from your messages on this forum and on the HCP > discussion list it is not clear to the reader what source model you used > for the eLORETA. > > There is some information on the fieldtrip wiki that illustrates how to > parcellate source reconstructed data > http://www.fieldtriptoolbox.org/tutorial/networkanalysis In the example, > it uses a surface-based parcellation, and parcellates a connectivity > matrix. The function can also parcellate univariate data (e.g. time courses > or power spectra), either or not defined on a 3D grid. > > Also, the HCP software+documentation that the MEG team released, and which > accompanies the released data, might give you some pointers on how to do > it. You could try and adapt the code provided to your own needs. > > Good luck, > > Jan-Mathijs > > > J.M.Schoffelen > Senior Researcher > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > > > > On 03 Nov 2016, at 20:11, mehdy dousty wrote: > > > > Hello all, > > By using the data of HCP, and conduct eLORETA to compute the inverse > problem, right now Id like to visualise the results to surface but due to > not having any anatomical information saved in the matrix it does not show > any mapping. Moreover, Id like to parcellate the cortex based on the AAL > atlas, so I really appreciate if anybody can help me. > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From marco.buiatti at gmail.com Fri Nov 4 11:08:10 2016 From: marco.buiatti at gmail.com (Marco Buiatti) Date: Fri, 4 Nov 2016 11:08:10 +0100 Subject: [FieldTrip] importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco On 16 August 2016 at 07:20, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Jeff, > Probably this is not yet implemented. Since it is all matlab-based it > should be pretty straightforward, once you know how the headmodel is > represented in the Brainstorm .mat file. > Once you have figured this out, it probably takes just a few lines of code > in ft_read_headshape. > > If you have an updated version of this function, you can easily contribute > it to the code-base for everyone’s use through git. > How this can be done, is shown here: http://www.fieldtriptoolbox.org/ > development/git > > Best, > Jan-Mathijs > > > > On 16 Aug 2016, at 02:05, K Jeffrey Eriksen wrote: > > Hi, > > I am trying to do some forward and inverse simulations using a 3-shell > BEM, and would prefer to import my model from Brainstorm where I have > already created it. I can find one example of importing the cortical mesh > from Brainstorm using ft_read_headshape with the ‘matlab’ format, but I > cannot find anything on how to import the Brainstorm BEM head model or > leadfield. > > Please point me to any further information on this topic, > -Jeff > > > _______________________________________________ > 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 > -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From cmuehl at gmail.com Fri Nov 4 11:37:08 2016 From: cmuehl at gmail.com (Christian Muehl) Date: Fri, 4 Nov 2016 10:37:08 +0000 Subject: [FieldTrip] Frontiers Research Topic: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures" Message-ID: <5568b746f06e44e198a78067481fe910@EXPRD02.hosting.ru.nl> *** Frontiers research topic - Call for Contributions *** We would like to invite contributions to the following research topic in Frontiers of Human Neuroscience: "Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures" Our objective is to publish a focused collection of open-access articles that represent the state of the art in detection and estimation of working memory and other cognitive functions based on neurophysiological signal classification and aimed at the application of such classified states in human-computer interaction. We specifically invite contributions that deal with the detection of cognitive states in complex scenarios as they are found in real world applications. Please refer to http://tinyurl.com/detectWM for more details and submission guidelines. * Please let us know if you are interested to contribute by replying to felix.putze at uni-bremen.de * Relevant Dates 31 January 2017 - Abstract 30 April 2017 - Manuscript * Topic Editors Felix Putze, University of Bremen, Germany Fabien Lotte, Inria Bordeaux Sud-Ouest, France Stephen Fairclough, Liverpool John Moores University, United Kingdom Christian Mühl, German Aerospace Center, Cologne, Germany * Topics of Interest Executive cognitive functions like working memory determine the success or failure of a wide variety of different cognitive tasks. Estimation of constructs like working memory load or memory capacity from neurophysiological or psychophysiological signals would enable adaptive systems to respond to cognitive states experienced by an operator and trigger responses designed to support task performance (e.g. by simplifying the exercises of a tutor system, or by shutting down distractions from the mobile phone). The determination of cognitive states like working memory load is also useful for automated testing/assessment, for usability evaluation and for tutoring applications. While there exists a huge body of research work on neural and physiological correlates of cognitive functions like working memory activity, fewer publications deal with the application of this research with respect to single-trial detection and real-time estimation of cognitive functions in complex, realistic scenarios. Single-trial classifiers based on brain activity measurements such as EEG, fNIRS or physiological signals such as EDA, ECG, BVP or Eyetracking have the potential to classify affective or cognitive states based upon short segments of data. For this purpose, signal processing and machine learning techniques need to be developed and transferred to real-world user interfaces. In this research topic, we are looking for: (1) studies in complex, realistic scenarios that specifically deal with cognitive states or cognitive processes (memory-related or other), (2) classification and estimation of cognitive states and processes like working memory activity, and (3) applications to Brain-Computer Interfaces and Human-Computer Interaction in general. Central open research questions which we would like to see approached in this research topic comprise: * How can working memory load be quantified with regression or classification models which are robust to perturbations common to realistic recording conditions and natural brain signal fluctuations? * How can detection and classification of cognitive states be used in Brain-Computer Interfaces (BCIs)? * How can multiple types of features or signal types be combined to achieve a more robust classification of working memory load? * How can working memory activity be differentiated from other types of cognitive or affective activity which co-vary with, but are not related to memory? * How well can insights from offline, average-analysis studies on memory activity be transferred to online, single-trial BCIs? * How can models of working memory load be calibrated to account for individual differences, for example in working memory capacity? * How can approaches from computational cognitive modeling be combined with physiological signals to assess memory processes? * How can working memory load be classified, for example according to modality (spatial memory, semantic memory, ...) or type of activity (encoding, retrieval, rehearsal, ...)? * How to design user-independent memory load estimators? Is that even feasible? * How can memory load estimators from a given context or modality be transferred to another modality and/or context? * How can working memory activity be classified to predict the outcome of the activity, for example by differentiating successful from failed encoding attempts? Additionally, we are also interested in other relevant submissions related to the research topic. We also sincerely invite manuscripts dealing with applications of memory-related interfaces (e.g. adaptive human-computer interfaces for memory-intensive tasks). Comprehensive review articles which critically reflect the state-of-the-art on a certain aspect of the topic are also welcome. With best regards, Felix Putze, Fabien Lotte, Stephen Fairclough, Christian Mühl. -------------- next part -------------- An HTML attachment was scrubbed... URL: From eriksenj at ohsu.edu Fri Nov 4 23:12:13 2016 From: eriksenj at ohsu.edu (K Jeffrey Eriksen) Date: Fri, 4 Nov 2016 22:12:13 +0000 Subject: [FieldTrip] importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Hi Marco, Sorry to say I was not successful, yet. I have shifted to other tasks at this point. I am using 256 channel EEG, and want to use the FreeSurfer cortical ribbon as my source model (I would prefer source/headmodels to be FEM as well). I found that a lot of FieldTrip is based on regular dipole grids, and also it is more oriented to MEG than EEG. I do not have the time right now to try to put all the pieces together in FieldTrip to do what I want, but might get back to it in a month or two. Sorry I could not be of more help to you right now. -Jeff From: Marco Buiatti [mailto:marco.buiatti at gmail.com] Sent: Friday, November 04, 2016 3:08 AM To: FieldTrip discussion list; K Jeffrey Eriksen Subject: Re: [FieldTrip] importing Brainstorm head and source models and leftfields Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco On 16 August 2016 at 07:20, Schoffelen, J.M. (Jan Mathijs) > wrote: Hi Jeff, Probably this is not yet implemented. Since it is all matlab-based it should be pretty straightforward, once you know how the headmodel is represented in the Brainstorm .mat file. Once you have figured this out, it probably takes just a few lines of code in ft_read_headshape. If you have an updated version of this function, you can easily contribute it to the code-base for everyone’s use through git. How this can be done, is shown here: http://www.fieldtriptoolbox.org/development/git Best, Jan-Mathijs On 16 Aug 2016, at 02:05, K Jeffrey Eriksen > wrote: Hi, I am trying to do some forward and inverse simulations using a 3-shell BEM, and would prefer to import my model from Brainstorm where I have already created it. I can find one example of importing the cortical mesh from Brainstorm using ft_read_headshape with the ‘matlab’ format, but I cannot find anything on how to import the Brainstorm BEM head model or leadfield. Please point me to any further information on this topic, -Jeff _______________________________________________ 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 -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.tadel at mcgill.ca Sat Nov 5 15:33:07 2016 From: francois.tadel at mcgill.ca (Francois Jean Tadel, Mr) Date: Sat, 5 Nov 2016 14:33:07 +0000 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Jeff, Marco: Good timing, I've been working on similar topics this week. I added processes in Brainstorm to use the forward models in FieldTrip, using ft_volumesegment, ft_prepare_headmodel and ft_prepare_leadfield. For now it is not possible to use the Brainstorm BEM surfaces to compute the leadfield with FieldTrip, but if you think this is useful, we could probably add this. Before the end of the month, I hope to have all the inverse models available as well. We will have the possibility to call them either with Brainstorm or FieldTrip forward solutions. I you want to help me with the debugging, or if you want to start working on the inverse/DICS part next week, it's all on github: https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_volumesegment.m https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_prepare_leadfield.m You could create a new process to call the FieldTrip function you want, there are already many other examples of wrappers available: process_ft_channelrepair.m process_ft_dipolefitting.m process_ft_scalpcurrentdensity.m process_ft_timelockstatistics.m process_ft_sourcestatistics.m process_ft_freqstatistics.m If you need help with the plugin API in Brainstorm: http://neuroimage.usc.edu/brainstorm/Tutorials/TutUserProcess Functions to convert Brainstorm files into FieldTrip structures: brainstorm3/toolbox/io/out_fieldtrip_*.m Cheers, Francois ________________________________ Hi Marco, Sorry to say I was not successful, yet. I have shifted to other tasks at this point. I am using 256 channel EEG, and want to use the FreeSurfer cortical ribbon as my source model (I would prefer source/headmodels to be FEM as well). I found that a lot of FieldTrip is based on regular dipole grids, and also it is more oriented to MEG than EEG. I do not have the time right now to try to put all the pieces together in FieldTrip to do what I want, but might get back to it in a month or two. Sorry I could not be of more help to you right now. -Jeff From: Marco Buiatti [mailto:marco.buiatti at gmail.com] Sent: Friday, November 04, 2016 3:08 AM To: FieldTrip discussion list; K Jeffrey Eriksen Subject: Re: [FieldTrip] importing Brainstorm head and source models and leftfields Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Sun Nov 6 00:51:09 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Sat, 05 Nov 2016 23:51:09 +0000 Subject: [FieldTrip] BTI to MNI coordination Message-ID: Hello all, The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Sun Nov 6 16:22:17 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Sun, 6 Nov 2016 15:22:17 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: Message-ID: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From mehdy.dousty at gmail.com Sun Nov 6 19:39:15 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Sun, 06 Nov 2016 18:39:15 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Message-ID: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < stan.vanpelt at donders.ru.nl> wrote: > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Sun Nov 6 21:02:17 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Sun, 6 Nov 2016 20:02:17 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Message-ID: <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> ft_convert_coorsys is not accurate. Don’t use it if you want an accurate conversion. Best, Jan-Mathijs On 06 Nov 2016, at 19:39, mehdy dousty > wrote: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) > wrote: Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From daniel.haehnke at tum.de Mon Nov 7 14:49:35 2016 From: daniel.haehnke at tum.de (=?utf-8?B?SMOkaG5rZSwgRGFuaWVs?=) Date: Mon, 7 Nov 2016 13:49:35 +0000 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes Message-ID: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> Dear FT community, I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. Method 1: 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. 3. For each trial shuffle (see 1.) average across subsamples (see 2.). 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. Method 2: 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. Despite the differences in the steps, there are also the following differences in the two methods. In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). Best wishes, Daniel -- Daniel Hähnke PhD student Technische Universität München Institute of Neuroscience Translational NeuroCognition Laboratory Biedersteiner Straße 29, Bau 601 80802 Munich Germany Email: daniel.haehnke at tum.de Phone: +49 89 4140 3356 From mehdy.dousty at gmail.com Mon Nov 7 17:48:25 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Mon, 07 Nov 2016 16:48:25 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> Message-ID: Thanks for the answer. I am going to volumelookup the predefined sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to the ROI_MNI_V4, as the atlas has the MNI coordination systema and the sourcemodel has the bti coordinations system and there is no transform function in the sourcmodel to convert bti to mni, what is your suggestion to do so? Thanks On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > ft_convert_coorsys is not accurate. Don’t use it if you want an accurate > conversion. > Best, > Jan-Mathijs > > On 06 Nov 2016, at 19:39, mehdy dousty wrote: > > Thanks for the answer. how accurate to use ft_convert_coordsys (source, > 'spm'), and if it is not, as there is no explanation on wiki unfortunately, > how can I make this conversion . > Thanks > Mehdy > > On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < > stan.vanpelt at donders.ru.nl> wrote: > > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Mon Nov 7 20:52:33 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 7 Nov 2016 19:52:33 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> Message-ID: <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> Mehdy, As I suggested in an earlier e-mail it’s most convenient to use ft_sourceinterpolate to interpolate the atlas onto the sourcemodel. To this end you can use the template sourcemodel that comes shipped with the HCP megconnectome software. Once you have managed to do this, you can directly look-up the anatomical labels of the dipole positions, because the dipole positions in the individual sourcemodels (in bti space) by construction coincide with the template after transformation into mni space. This is explained on page 30 of the HCP MEG manual. I suggest you to consult this in somewhat more detail. Best wishes, Jan-Mathijs On 07 Nov 2016, at 17:48, mehdy dousty > wrote: Thanks for the answer. I am going to volumelookup the predefined sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to the ROI_MNI_V4, as the atlas has the MNI coordination systema and the sourcemodel has the bti coordinations system and there is no transform function in the sourcmodel to convert bti to mni, what is your suggestion to do so? Thanks On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) > wrote: ft_convert_coorsys is not accurate. Don’t use it if you want an accurate conversion. Best, Jan-Mathijs On 06 Nov 2016, at 19:39, mehdy dousty > wrote: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) > wrote: Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Mon Nov 7 22:17:19 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Mon, 07 Nov 2016 21:17:19 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> Message-ID: Thank you very much. As you suggest after computing the inverse problem I interpolate the atlas to the sourcemodel by the below commands: atlas = ft_read_atlas('/home/mehdy/Desktop/EEG-1/fieldtrip-20160417/template/atlas/aal/ROI_MNI_V4.nii'); cfg = []; cfg.interpmethod = 'nearest'; cfg.parameter = 'tissue'; individual_sourcemodel3d1 = ft_sourceinterpolate(cfg,atlas,individual_sourcemodel3d); and then interpolate the result of source construction to the computed source model , I mean individual_sourcemodel3d1: cfg = []; cfg.parameter = 'pow'; source_eloreta_disc_int1 = ft_sourceinterpolate(cfg,source_eloreta,individual_sourcemodel3d1); now the are the data parcellated? if it is so, how can I get the parcellation labels? Thanks On Mon, Nov 7, 2016 at 12:59 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Mehdy, > > As I suggested in an earlier e-mail it’s most convenient to use > ft_sourceinterpolate to interpolate the atlas onto the sourcemodel. To this > end you can use the template sourcemodel that comes shipped with the HCP > megconnectome software. Once you have managed to do this, you can directly > look-up the anatomical labels of the dipole positions, because the dipole > positions in the individual sourcemodels (in bti space) by construction > coincide with the template after transformation into mni space. This is > explained on page 30 of the HCP MEG manual. I suggest you to consult this > in somewhat more detail. > > Best wishes, > Jan-Mathijs > > > > On 07 Nov 2016, at 17:48, mehdy dousty wrote: > > Thanks for the answer. I am going to volumelookup the predefined > sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to > the ROI_MNI_V4, as the atlas has the MNI coordination systema and the > sourcemodel has the bti coordinations system and there is no transform > function in the sourcmodel to convert bti to mni, what is your suggestion > to do so? > Thanks > > On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) < > jan.schoffelen at donders.ru.nl> wrote: > > ft_convert_coorsys is not accurate. Don’t use it if you want an accurate > conversion. > Best, > Jan-Mathijs > > On 06 Nov 2016, at 19:39, mehdy dousty wrote: > > Thanks for the answer. how accurate to use ft_convert_coordsys (source, > 'spm'), and if it is not, as there is no explanation on wiki unfortunately, > how can I make this conversion . > Thanks > Mehdy > > On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < > stan.vanpelt at donders.ru.nl> wrote: > > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From florian at brain.riken.jp Wed Nov 9 01:51:55 2016 From: florian at brain.riken.jp (Florian Gerard-Mercier) Date: Wed, 9 Nov 2016 09:51:55 +0900 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes In-Reply-To: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> Message-ID: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Dear Daniel, Did you get an answer already? I’m just wondering, what is the null hypothesis? Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions? Best, Florian Gerard-Mercier Lab. for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: 048 462 1111 and 7106 Mob: 080 3213 6851 > On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel wrote: > > Dear FT community, > > I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. > > I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. > > Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. > > Method 1: > 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. > 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. > 3. For each trial shuffle (see 1.) average across subsamples (see 2.). > 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. > > Method 2: > 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. > 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. > 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). > > Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. > > Despite the differences in the steps, there are also the following differences in the two methods. > In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. > Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. > > Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. > I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. > > It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). > > Best wishes, > > Daniel > -- > Daniel Hähnke > PhD student > > Technische Universität München > Institute of Neuroscience > Translational NeuroCognition Laboratory > Biedersteiner Straße 29, Bau 601 > 80802 Munich > Germany > > Email: daniel.haehnke at tum.de > Phone: +49 89 4140 3356 > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From daniel.haehnke at tum.de Wed Nov 9 14:30:04 2016 From: daniel.haehnke at tum.de (=?utf-8?B?SMOkaG5rZSwgRGFuaWVs?=) Date: Wed, 9 Nov 2016 13:30:04 +0000 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes In-Reply-To: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Message-ID: <4E9EA3F3-0456-4367-A17B-8ED076223E70@tum.de> Hi Florian, thanks for your reply! I haven’t had any replies yet. You’re right, it’s important to form a null hypothesis before doing statistical tests. Explicitly, my null hypothesis is that the PLV is the same for all conditions. For that I’d need to shuffle the condition labels of the spike phases across conditions. The z-scoring was meant as a normalisation of the spike-lfp-combinations, so I can pool combinations via averaging. Of course, the trial association shuffling implicitly also tests the null hypothesis of zero PLV. Best, Daniel On 9 Nov 2016, at 01:51, Florian Gerard-Mercier > wrote: Dear Daniel, Did you get an answer already? I’m just wondering, what is the null hypothesis? Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions? Best, Florian Gerard-Mercier Lab. for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: 048 462 1111 and 7106 Mob: 080 3213 6851 On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel > wrote: Dear FT community, I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. Method 1: 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. 3. For each trial shuffle (see 1.) average across subsamples (see 2.). 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. Method 2: 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. Despite the differences in the steps, there are also the following differences in the two methods. In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). Best wishes, Daniel -- Daniel Hähnke PhD student Technische Universität München Institute of Neuroscience Translational NeuroCognition Laboratory Biedersteiner Straße 29, Bau 601 80802 Munich Germany Email: daniel.haehnke at tum.de Phone: +49 89 4140 3356 _______________________________________________ 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From Darren.Price at mrc-cbu.cam.ac.uk Wed Nov 9 17:11:08 2016 From: Darren.Price at mrc-cbu.cam.ac.uk (Darren Price) Date: Wed, 9 Nov 2016 16:11:08 +0000 Subject: [FieldTrip] Open-Access Dataset: Cambridge Centre for Ageing and Neuroscience (CamCAN) Message-ID: Dear Fieldtrippers The Cambridge Centre for Ageing and Neuroscience (Cam-CAN; www.cam-can.org) is pleased to announce the release of raw data from the first wave of Phase II of the Cam-CAN cohort. These data include MRI, MEG and cognitive data from approximately 650 males and females uniformly distributed from 18 to 88 years of age. The sample is unique in its population-representativeness (e.g, relative to national census data) and the depth and breadth of neuroimaging and cognitive assessment. The MRI data (in NIFTI and BIDS format) include T1-weighted, T2-weighted and Diffusion-weighted 3T MRI images, plus 3 runs of BOLD-weighted images during 1) rest, 2) movie-watching and 3) an event-related sensorimotor task (with combined visual and auditory stimuli cueing a motor response); the MEG data (in FIF format) include 3 runs of 1) rest, 2) the same sensorimotor task as the fMRI and 3) a passive sensory task (with separate visual and auditory stimuli); the behavioural data include scores on tasks assessing a range of cognitive domains, such as fluid intelligence, memory, language, among others. For more information about the CamCAN project and data, see: Shafto et al. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology, 14(204). doi:10.1186/s12883-014-0204-1. Taylor et al. (2015). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage. 10.1016/j.neuroimage.2015.09.018. We hope to release more data (e.g, MT-weighted and preprocessed MRI/MEG data) in future. The data are provided freely after agreeing to minimal conditions, via this page: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ Darren ------------------------------------------------------- Dr. Darren Price Investigator Scientist and Cam-CAN Data Manager MRC Cognition & Brain Sciences Unit 15 Chaucer Road Cambridge, CB2 7EF England EMAIL: darren.price at mrc-cbu.cam.ac.uk URL: http://www.mrc-cbu.cam.ac.uk/people/darren.price TEL +44 (0)1223 355 294 x202 FAX +44 (0)1223 359 062 MOB +44 (0)7717822431 ------------------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From ph442 at cam.ac.uk Wed Nov 9 18:45:27 2016 From: ph442 at cam.ac.uk (parham hashemzadeh) Date: Wed, 09 Nov 2016 17:45:27 +0000 Subject: [FieldTrip] Literature or methods in Fieldtrip on estimating missing data. In-Reply-To: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Message-ID: Dear all I was wondering, if you know of any resources or techniques in dealing with missing data. Does fieldtrip have any routines . I am looking for better methods that extrapolate data points to where there are no sensors. Are there any studies or papers on extrapolation or estimating data at points where there are no sensors. Maybe some matlab routines. I would appreciate any help you are willing to provide on known literature. best regards parham From marco.buiatti at gmail.com Thu Nov 10 11:56:03 2016 From: marco.buiatti at gmail.com (Marco Buiatti) Date: Thu, 10 Nov 2016 11:56:03 +0100 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Dear Francois, this is really good news, thanks! I have tested the Process > Sources > Fieldtrip: ft_prepare_leadfield on different MEG data (always recorded from an Elekta 306 sensors system), and I get an error "cannot work on balanced gradiometer definition" (see the warnings below). I have tried to debug this but I am a bit lost since I am not very familiar with manipulating source data. I also have a basic question on the data to put in the process: my understanding is that I should feed the process with the segmented anatomy, that I identify with (for the default anatomy) the "Cortex_15002V" in the anatomy tab. However, I cannot drag it to the Process window (I understand that the sensor configuration is also needed). I therefore drag in the Process window any data associated with the subject. Is this correct? Does the program then automatically process the segmented anatomy? Can you please shed light on this, or advice me on how to debug it? Thanks a lot, Marco BST> FieldTrip install: C:\Users\marco.buiatti\Documents\software\fieldtrip-20161107 the input is volume data with dimensions [181 217 181] Converting the coordinate system from ctf to spm Rescaling NIFTI: slope = 0.00342945, intercept = 0 Smoothing by 0 & 8mm.. Coarse Affine Registration.. Fine Affine Registration.. performing the segmentation on the specified volume creating brainmask smoothing brainmask with a 5-voxel FWHM kernel thresholding brainmask at a relative threshold of 0.500 the call to "ft_volumesegment" took 52 seconds Warning: assuming that planar MEG channel units are T/m > In ft_chanunit at 173 In ft_datatype_sens at 392 In ft_datatype_sens at 158 In ft_checkconfig at 232 In utilities\private\ft_preamble_trackconfig at 37 In ft_preamble at 56 In ft_prepare_headmodel at 148 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 Warning: please specify cfg.method='projectmesh', 'iso2mesh' or 'isosurface' > In ft_prepare_mesh at 137 In ft_prepare_headmodel at 348 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 Warning: using 'projectmesh' as default > In ft_prepare_mesh at 138 In ft_prepare_headmodel at 348 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 triangulating the outer boundary of compartment 1 (brain) with 3000 vertices the call to "ft_prepare_mesh" took 1 seconds the call to "ft_prepare_headmodel" took 1 seconds *************************************************************************** ** Error: [process_ft_prepare_leadfield] Sources > FieldTrip: ft_prepare_leadfield ** Line 228: ft_plot_sens (line 228) ** cannot work with balanced gradiometer definition ** ** Call stack: ** >ft_plot_sens.m at 228 ** >process_ft_prepare_leadfield.m>Run at 255 ** >process_ft_prepare_leadfield.m at 24 ** >bst_process.m>Run at 229 ** >bst_process.m at 36 ** >panel_process1.m>RunProcess at 141 ** >panel_process1.m at 27 ** >gui_brainstorm.m>CreateWindow/ProcessRun_Callback at 707 ** >bst_call.m at 28 ** >gui_brainstorm.m>@(h,ev)bst_call(@ProcessRun_Callback) at 261 ** ** ** File: 150505/@raw19900812VTTN_01run4/data_0raw_19900812VTTN_01run4.mat ** *************************************************************************** [image: Inline images 2] On 5 November 2016 at 15:33, Francois Jean Tadel, Mr < francois.tadel at mcgill.ca> wrote: > Jeff, Marco: > > > Good timing, I've been working on similar topics this week. I added > processes in Brainstorm to use the forward models in FieldTrip, using > ft_volumesegment, ft_prepare_headmodel and ft_prepare_leadfield. For now it > is not possible to use the Brainstorm BEM surfaces to compute the leadfield > with FieldTrip, but if you think this is useful, we could probably add this. > > > Before the end of the month, I hope to have all the inverse models > available as well. We will have the possibility to call them either with > Brainstorm or FieldTrip forward solutions. > > > I you want to help me with the debugging, or if you want to start working > on the inverse/DICS part next week, it's all on github: > https://github.com/brainstorm-tools/brainstorm3/blob/master/ > toolbox/process/functions/process_ft_volumesegment.m > https://github.com/brainstorm-tools/brainstorm3/blob/master/ > toolbox/process/functions/process_ft_prepare_leadfield.m > > You could create a new process to call the FieldTrip function you want, > there are already many other examples of wrappers available: > process_ft_channelrepair.m > process_ft_dipolefitting.m > process_ft_scalpcurrentdensity.m > process_ft_timelockstatistics.m > process_ft_sourcestatistics.m > process_ft_freqstatistics.m > > If you need help with the plugin API in Brainstorm: > http://neuroimage.usc.edu/brainstorm/Tutorials/TutUserProcess > > Functions to convert Brainstorm files into FieldTrip structures: > brainstorm3/toolbox/io/out_fieldtrip_*.m > > Cheers, > Francois > > > ------------------------------ > > Hi Marco, > > Sorry to say I was not successful, yet. I have shifted to other tasks at > this point. I am using 256 channel EEG, and want to use the FreeSurfer > cortical ribbon as my source model (I would prefer source/headmodels to be > FEM as well). I found that a lot of FieldTrip is based on regular dipole > grids, and also it is more oriented to MEG than EEG. I do not have the > time right now to try to put all the pieces together in FieldTrip to do > what I want, but might get back to it in a month or two. > > Sorry I could not be of more help to you right now. > > -Jeff > > > > From: Marco Buiatti [mailto:marco.buiatti at gmail.com > ] > Sent: Friday, November 04, 2016 3:08 AM > To: FieldTrip discussion list; K Jeffrey Eriksen > Subject: Re: [FieldTrip] importing Brainstorm head and source models and > leftfields > > Dear all, > > I have Jeff's same question. I have anatomies ready in Brainstorm: > segmented data imported, co-registration with MEG sensor space, computation > of head model with overlapping spheres. Now I would like to import all this > into Fieldtrip for source reconstruction of oscillatory sources (DICS). > What's the best way to do this? > > And is there any difference between Brainstorm and Fieldtrip in these > steps that I should be aware of? > > Jeff, did you manage to solve the problem? > > Thanks a lot, > > Marco > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 53275 bytes Desc: not available URL: From mehdy.dousty at gmail.com Thu Nov 10 19:03:31 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 10 Nov 2016 18:03:31 +0000 Subject: [FieldTrip] Extracting time course of specific region in source level Message-ID: Hi all, I am working on source reconstruction of HCP MEG data by using eLORETA. The inverse problem is computed and the data are projected to the MNI by sourceinterpolate, so I would like to extract all the time courses which deal with specific ROI. I searched the mailing list and mostly they just provide information on the beamformer, so Id be grateful if anybody could possibly give me some hints about it. Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From amitjaiswal.elect at gmail.com Fri Nov 11 09:51:49 2016 From: amitjaiswal.elect at gmail.com (amit kumar Jaiswal) Date: Fri, 11 Nov 2016 10:51:49 +0200 Subject: [FieldTrip] checkinput_mex Error Message-ID: Hi everyone, I am using dipole fitting with .fif MEG data and while using ICP my Centos workstation is coming with an error: Undefined function '*checkinput_mex*' for input arguments of type 'cell' . I tried to get this function but couldn't. I checked that the same script is running fine on windows machine. What is the solution?? -- *Thanks & Regards......* *Amit Kumar Jaiswal* *Researcher @ChildBrain Project* *Elekta Oy, Helsinki (Finland)* *Call/Whatsapp: +358-405222805* *Important facts to be careful:* *** 800 million people (1 of every 9) in the world don't get clean & safe water. ** Save water daily and plant trees on every occasion.** **Save earth, Save lives.* -------------- next part -------------- An HTML attachment was scrubbed... URL: From deefje.meijer at gmail.com Mon Nov 14 12:35:05 2016 From: deefje.meijer at gmail.com (David Meijer) Date: Mon, 14 Nov 2016 11:35:05 +0000 Subject: [FieldTrip] PhD position, Neurocomputational Linguistics, Uni Bham / Google Research London Message-ID: On behalf of Prof Uta Noppeney. *PhD position in Neurocomputational Linguistics* *University of Birmingham in collaboration with Google Research London* Language comprehension is critical for effective interactions in our social world. In order to understand ‘who does what to whom’ in natural language processing, the brain needs to assign a syntactic structure to every sentence – a process coined ‘syntactic parsing’. This interdisciplinary project will combine expertise from human neuroscience (University of Birmingham) and computational linguistics (Google Research London) to determine the neural mechanisms underlying sentence comprehension in the human brain and advance parsing algorithms in machines. To study natural language processing and the underlying neural mechanisms in humans, we will measure eye movements, behavioural (psychophysics) and electrophysiological responses (EEG/fMRI) in participants reading natural sentences from syntactically annotated corpora. We will employ advanced machine learning algorithms to characterize the computational operations and neural mechanisms underlying syntactic processing in the human brain. Conversely, the insights obtained from human neuroimaging (EEG/fMRI) and eye tracking will provide critical constraints on the parameters and algorithms used in machine. The PhD position is designed to involve a 3 month internship at Google Research London. The Computational Cognitive Neuroimaging Group (Uta Noppeney) in collaboration with Google Research London (Bernd Bohnet, Ryan McDonald) is seeking an enthusiastic PhD candidate with strong analytical and quantitative abilities. Applicants should have a background in computational linguistics, neuroscience, computer science, psychology, physics or related areas. Prior experience in statistical analysis and/or machine learning would be an advantage. The Computational Cognitive Neuroimaging Lab is based at the Department of Psychology and the Computational Neuroscience and Cognitive Robotics Centre of the University of Birmingham, UK. The centre provides an excellent multidisciplinary, interactive and collaborative research environment combining expertise in cognitive neuroimaging, psychophysics and computational neuroscience. The psychology department was rated 5th in the UK research assessment exercise. http://www.birmingham.ac.uk/schools/psychology/research/labs/comp-cog-neuro/index.aspx http://www.birmingham.ac.uk/research/activity/cncr/index.aspx Applications will be considered until 8th January 2017. The starting date is Sept/Oct 2017. iCASE students must fulfil the MIBTP entry requirements and will join the MIBTP cohort for the taught modules and masterclasses during the first term. They will remain as an integral part of the MIBTP cohort and take part in the core networking activities and transferable skills training. For further information, please contact u.noppeney at bham.ac.uk. Check eligibility and apply here: https://www2.warwick.ac.uk/fac/cross_fac/mibtp/pgstudy/phd_opportunities/application/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.tadel at mcgill.ca Tue Nov 15 21:33:42 2016 From: francois.tadel at mcgill.ca (=?UTF-8?Q?Fran=c3=a7ois_Tadel?=) Date: Tue, 15 Nov 2016 15:33:42 -0500 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields Message-ID: Hi Marco, > I have tested the Process > Sources > Fieldtrip: ft_prepare_leadfield on > different MEG data (always recorded from an Elekta 306 sensors system), and > I get an error "cannot work on balanced gradiometer definition" For some reason, ft_plot_sens doesn't want to get passed both the gradiometers and the magnetometers at once. Just run the process without the option "Display sensor/MRI registration" and you won't get this error any more. > I also have a basic question on the data to put in the process: my > understanding is that I should feed the process with the segmented anatomy, > that I identify with (for the default anatomy) the "Cortex_15002V" in the > anatomy tab. However, I cannot drag it to the Process window (I understand > that the sensor configuration is also needed). I therefore drag in the > Process window any data associated with the subject. Is this correct? Does > the program then automatically process the segmented anatomy? Yes, this is what you should do: select some recordings for the subject, and it will get the files it needs automatically from the database. Right now, this process does the following: - If the segmented masks produced previously with ft_volumesegment are available (called "mask_innerskull", "mask_outerskull" and "mask_scalp") it uses them. - Otherwise it uses the selected volume (displayed in green), passes it to ft_volumesegment for segmentation, then uses the segmented volumes without storing them anywhere. https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_prepare_leadfield.m#L153 Cheers, Francois -- François Tadel, MSc MEG / McConnell Brain Imaging Center / MNI / McGill University 3801 rue University, Montreal, QC H3A2B4, Canada -------------- next part -------------- An HTML attachment was scrubbed... URL: From hbharadw at purdue.edu Tue Nov 15 22:06:51 2016 From: hbharadw at purdue.edu (Bharadwaj, Hari M) Date: Tue, 15 Nov 2016 21:06:51 +0000 Subject: [FieldTrip] PhD student openings in auditory neuroscience at Purdue University Message-ID: <1479244011788.63656@purdue.edu> [Apologies for cross posting] Two PhD student positions are available at the Systems Neuroscience of Auditory Perception Lab at Purdue University. We study the neural mechanisms of auditory perception in complex multi-source environments (e.g., crowded restaurants or busy streets) using a combination of human neuroimaging techniques (EEG, MRI, otoacoustic emissions), behavioral listening experiments, and computational modeling. In addition we are interested in the effects of overexposure to loud sounds and early aging on the auditory system, and how that affects perception. A notable capability of the lab is the ability to non-invasively measure, and to model physiological responses at different levels of the auditory pathway from the cochlea to the cortex. Please visit https://engineering.purdue.edu/SNAPLab for more information about the lab, the facilities, and the vibrant intellectual environment at Purdue with a large community of hearing researchers and neuroscientists. Applications can be submitted through the Weldon School of Biomedical Engineering, or the Department of Speech, Language, and Hearing Sciences as described here. Informal enquiries about the positions can be directed to Hari Bharadwaj (hbharadwaj at purdue.edu). Preference will be given to applications received by December 15, 2016. -- Hari M. Bharadwaj, Ph.D. Assistant Professor of Speech, Language, and Hearing Sciences Assistant Professor of Biomedical Engineering Lyles-Porter Hall Purdue University 715 Clinic Drive, Room 3162 West Lafayette, IN 47907 (765) 496-2249 hbharadwaj at purdue.edu http://engineering.purdue.edu/SNAPLab -------------- next part -------------- An HTML attachment was scrubbed... URL: From mailtome.2113 at gmail.com Wed Nov 16 05:09:12 2016 From: mailtome.2113 at gmail.com (Arti Abhishek) Date: Wed, 16 Nov 2016 15:09:12 +1100 Subject: [FieldTrip] Questions about time frequency analysis of EEG data Message-ID: Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Wed Nov 16 08:58:21 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Wed, 16 Nov 2016 07:58:21 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: References: Message-ID: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From Simon.VanEyndhoven at esat.kuleuven.be Wed Nov 16 14:07:22 2016 From: Simon.VanEyndhoven at esat.kuleuven.be (Simon Van Eyndhoven) Date: Wed, 16 Nov 2016 14:07:22 +0100 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp Message-ID: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Dear all, I recently started using FieldTrip in order to simulate pseudo-realistic EEG recordings from e.g. a set of dipoles located in the brain. Therefore, I tried to follow the tutorial on the construction of a BEM head model based on an anatomical MRI scan and an electrode set: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . The problem is that I don't succeed to reproduce the head model that is distributed as a template in the FieldTrip toolbox: '/template/headmodel/standard_bem.mat'. I start from the template MRI scan in FieldTrip ('standard_mri.mat') and use the following code (taken from the tutorial). --- % Load MRI data and electrode setup mri = importdata('standard_mri.mat'); elec = ft_read_sens('standard_1020.elc') % Segment the MRI volume cfg = []; cfg.output = {'brain','skull','scalp'}; segmentedmri = ft_volumesegment(cfg, mri); % Create a mesh cfg = []; cfg.tissue = {'brain','skull','scalp'}; cfg.numvertices = [1500 1000 500]; bnd = ft_prepare_mesh(cfg,segmentedmri); % Construct the headmodel cfg = []; cfg.conductivity = [0.3300 0.0041 0.3300]; cfg.method = 'dipoli'; vol = ft_prepare_headmodel(cfg, bnd); --- The following error is thrown: "Fatal error in dipoli: during computation of B-matrix; vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of /tmp/tp39371164882184732_2.tri" I've tried to create meshes with different numbers of vertices (e.g. the combination from the website tutorial 'numvertices = [3000 2000 1000]' produces the same error): for some combinations this problem does not occur, luckily. However, I wonder how the template model (that used the aforementioned number of vertices) got created successfully; I want to make sure that I work in the correct way and don't forget something. Can anyone shed some light on this issue, or hint what might be wrong/missing in the implementation above, compared to the template results that are provided in the FieldTrip distribution? Moreover, I've tried to create the head model using the 'bemcp' method as well. As a test, I placed a dipole close to the scalp surface, near a particular electrode. It is hence expected that the recorded amplitudes are highest for electrodes in the vicinity of this electrode. This effect is correctly reproduced when performing the computation of the forward problem using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' method). By contrast, the output of the forward problem using the bemcp-based head model shows a topographic distribution with many positive 'peaks' and negative 'troughs', spread kind of randomly over the scalp surface. Since neither the segmentation step nor meshing step changed (only the final step, namely the creation of the volume conduction model), I have no clue why this problem occurs. I've looked around in the discussion list's archives for possible causes: - it is reported that the bemcp method does not behave well if the dipole is located (very) close to the skull: I ruled this out for my problem by varying this distance - one user (Debora Desideri) reported a problem with the bemcp-method that appears to be very similar to mine, but there was no reply to her problem... - another user (John Richards) calls for caution when using the bemcp-method, stating that it behaves poorly sometimes Has anyone experienced similar issues when using the 'becmp' method and maybe found an explanation for this? Thanks in advance for any help! Best regards, -- Simon Van Eyndhoven PhD researcher Stadius Center for Dynamical Systems, Signal Processing and Data Analytics KU Leuven, Dept. Electrical Engineering (ESAT) email: simon.vaneyndhoven at esat.kuleuven.be From litvak.vladimir at gmail.com Wed Nov 16 16:14:08 2016 From: litvak.vladimir at gmail.com (Vladimir Litvak) Date: Wed, 16 Nov 2016 15:14:08 +0000 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Message-ID: Dear Simon, I tested this before and now re-tested again using the versions of FT and BEMCP which come with SPM and should be quite recent. For SPM cortical mesh where we take special measures to make sure that it's far enough from the boundary the correlation coefficients between BEMCP and 3-spheres model are all above 0.9. You say you varied the depth but have you varied it enough? What 'enough' is depends on the density of your head meshes. For SPM meshes it was more than 6mm from the boundary. The difference between different BEM methods is how close you can get to the boundary without breaking. BEMCP is the simplest method which is not very good in this respect. dipoli or OpenMEEG will also break but closer to the surface. Best, Vladimir On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven < Simon.VanEyndhoven at esat.kuleuven.be> wrote: > Dear all, > > I recently started using FieldTrip in order to simulate pseudo-realistic > EEG recordings from e.g. a set of dipoles located in the brain. > Therefore, I tried to follow the tutorial on the construction of a BEM > head model based on an anatomical MRI scan and an electrode set: > http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . > > The problem is that I don't succeed to reproduce the head model that is > distributed as a template in the FieldTrip toolbox: > '/template/headmodel/standard_bem.mat'. I start from the template MRI > scan in FieldTrip ('standard_mri.mat') and use the following code (taken > from the tutorial). > > --- > % Load MRI data and electrode setup > mri = importdata('standard_mri.mat'); > elec = ft_read_sens('standard_1020.elc') > > % Segment the MRI volume > cfg = []; > cfg.output = {'brain','skull','scalp'}; > segmentedmri = ft_volumesegment(cfg, mri); > > % Create a mesh > cfg = []; > cfg.tissue = {'brain','skull','scalp'}; > cfg.numvertices = [1500 1000 500]; > bnd = ft_prepare_mesh(cfg,segmentedmri); > > % Construct the headmodel > cfg = []; > cfg.conductivity = [0.3300 0.0041 0.3300]; > cfg.method = 'dipoli'; > vol = ft_prepare_headmodel(cfg, bnd); > --- > > The following error is thrown: > > "Fatal error in dipoli: during computation of B-matrix; > vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of > /tmp/tp39371164882184732_2.tri" > > I've tried to create meshes with different numbers of vertices (e.g. the > combination from the website tutorial 'numvertices = [3000 2000 1000]' > produces the same error): for some combinations this problem does not > occur, luckily. However, I wonder how the template model (that used the > aforementioned number of vertices) got created successfully; I want to make > sure that I work in the correct way and don't forget something. > > Can anyone shed some light on this issue, or hint what might be > wrong/missing in the implementation above, compared to the template results > that are provided in the FieldTrip distribution? > > Moreover, I've tried to create the head model using the 'bemcp' method as > well. As a test, I placed a dipole close to the scalp surface, near a > particular electrode. It is hence expected that the recorded amplitudes are > highest for electrodes in the vicinity of this electrode. This effect is > correctly reproduced when performing the computation of the forward problem > using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' > method). By contrast, the output of the forward problem using the > bemcp-based head model shows a topographic distribution with many positive > 'peaks' and negative 'troughs', spread kind of randomly over the scalp > surface. Since neither the segmentation step nor meshing step changed (only > the final step, namely the creation of the volume conduction model), I have > no clue why this problem occurs. I've looked around in the discussion > list's archives for possible causes: > - it is reported that the bemcp method does not behave well if the dipole > is located (very) close to the skull: I ruled this out for my problem by > varying this distance > - one user (Debora Desideri) reported a problem with the bemcp-method that > appears to be very similar to mine, but there was no reply to her problem... > - another user (John Richards) calls for caution when using the > bemcp-method, stating that it behaves poorly sometimes > > Has anyone experienced similar issues when using the 'becmp' method and > maybe found an explanation for this? > > Thanks in advance for any help! > > Best regards, > > -- > Simon Van Eyndhoven > > PhD researcher > Stadius Center for Dynamical Systems, Signal Processing and Data Analytics > KU Leuven, Dept. Electrical Engineering (ESAT) > > email: simon.vaneyndhoven at esat.kuleuven.be > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Simon.VanEyndhoven at esat.kuleuven.be Thu Nov 17 10:34:56 2016 From: Simon.VanEyndhoven at esat.kuleuven.be (Simon Van Eyndhoven) Date: Thu, 17 Nov 2016 10:34:56 +0100 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Message-ID: <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> Hello Vladimir, Thank you for the swift response. I made sure to place the source at varying distances from the skull, to preclude any effect because of this. More precisely, I tried the following approach: -- elec = ft_read_sens('standard_1020.elc') chan_name = 'F8'; chan_index = find(strcmp(elec.label,chan_name)); chan_pos = elec.elecpos(chan_index,:); [...] cfg.dip.pos = 0.3*chan_pos; % I tried distances ranging from 0.3*chan_pos to 0.9*chan_pos -- For any distance from the skull the model yielded a nonsensical output as described earlier. This leads me to believe that this distance is not the determining factor here... Best regards, -- Simon Van Eyndhoven PhD researcher Stadius Center for Dynamical Systems, Signal Processing and Data Analytics KU Leuven, Dept. Electrical Engineering (ESAT) email: simon.vaneyndhoven at esat.kuleuven.be On 2016-11-16 16:14, Vladimir Litvak wrote: > Dear Simon, > > I tested this before and now re-tested again using the versions of FT and BEMCP which come with SPM and should be quite recent. For SPM cortical mesh where we take special measures to make sure that it's far enough from the boundary the correlation coefficients between BEMCP and 3-spheres model are all above 0.9. You say you varied the depth but have you varied it enough? What 'enough' is depends on the density of your head meshes. For SPM meshes it was more than 6mm from the boundary. The difference between different BEM methods is how close you can get to the boundary without breaking. BEMCP is the simplest method which is not very good in this respect. dipoli or OpenMEEG will also break but closer to the surface. > > Best, > > Vladimir > > On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven wrote: > >> Dear all, >> >> I recently started using FieldTrip in order to simulate pseudo-realistic EEG recordings from e.g. a set of dipoles located in the brain. >> Therefore, I tried to follow the tutorial on the construction of a BEM head model based on an anatomical MRI scan and an electrode set: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem [1] . >> >> The problem is that I don't succeed to reproduce the head model that is distributed as a template in the FieldTrip toolbox: '/template/headmodel/standard_bem.mat'. I start from the template MRI scan in FieldTrip ('standard_mri.mat') and use the following code (taken from the tutorial). >> >> --- >> % Load MRI data and electrode setup >> mri = importdata('standard_mri.mat'); >> elec = ft_read_sens('standard_1020.elc') >> >> % Segment the MRI volume >> cfg = []; >> cfg.output = {'brain','skull','scalp'}; >> segmentedmri = ft_volumesegment(cfg, mri); >> >> % Create a mesh >> cfg = []; >> cfg.tissue = {'brain','skull','scalp'}; >> cfg.numvertices = [1500 1000 500]; >> bnd = ft_prepare_mesh(cfg,segmentedmri); >> >> % Construct the headmodel >> cfg = []; >> cfg.conductivity = [0.3300 0.0041 0.3300]; >> cfg.method = 'dipoli'; >> vol = ft_prepare_headmodel(cfg, bnd); >> --- >> >> The following error is thrown: >> >> "Fatal error in dipoli: during computation of B-matrix; >> vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of /tmp/tp39371164882184732_2.tri" >> >> I've tried to create meshes with different numbers of vertices (e.g. the combination from the website tutorial 'numvertices = [3000 2000 1000]' produces the same error): for some combinations this problem does not occur, luckily. However, I wonder how the template model (that used the aforementioned number of vertices) got created successfully; I want to make sure that I work in the correct way and don't forget something. >> >> Can anyone shed some light on this issue, or hint what might be wrong/missing in the implementation above, compared to the template results that are provided in the FieldTrip distribution? >> >> Moreover, I've tried to create the head model using the 'bemcp' method as well. As a test, I placed a dipole close to the scalp surface, near a particular electrode. It is hence expected that the recorded amplitudes are highest for electrodes in the vicinity of this electrode. This effect is correctly reproduced when performing the computation of the forward problem using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' method). By contrast, the output of the forward problem using the bemcp-based head model shows a topographic distribution with many positive 'peaks' and negative 'troughs', spread kind of randomly over the scalp surface. Since neither the segmentation step nor meshing step changed (only the final step, namely the creation of the volume conduction model), I have no clue why this problem occurs. I've looked around in the discussion list's archives for possible causes: >> - it is reported that the bemcp method does not behave well if the dipole is located (very) close to the skull: I ruled this out for my problem by varying this distance >> - one user (Debora Desideri) reported a problem with the bemcp-method that appears to be very similar to mine, but there was no reply to her problem... >> - another user (John Richards) calls for caution when using the bemcp-method, stating that it behaves poorly sometimes >> >> Has anyone experienced similar issues when using the 'becmp' method and maybe found an explanation for this? >> >> Thanks in advance for any help! >> >> Best regards, >> >> -- >> Simon Van Eyndhoven >> >> PhD researcher >> Stadius Center for Dynamical Systems, Signal Processing and Data Analytics >> KU Leuven, Dept. Electrical Engineering (ESAT) >> >> email: simon.vaneyndhoven at esat.kuleuven.be >> >> _______________________________________________ >> fieldtrip mailing list >> fieldtrip at donders.ru.nl >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip [2] > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip Links: ------ [1] http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem [2] https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From litvak.vladimir at gmail.com Thu Nov 17 11:05:15 2016 From: litvak.vladimir at gmail.com (Vladimir Litvak) Date: Thu, 17 Nov 2016 10:05:15 +0000 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> Message-ID: Hi Simon, Then it might be something else. As I said, I checked yesterday in the latest SPM and it looks OK but there might be some code differences. We don't update our bemcp version every time we update the core FT. The approach Robert used and he might still have the script for it is to make a mesh for a single sphere and compare the bemcp solution for it with the analytical one. Vladimir On Thu, Nov 17, 2016 at 9:34 AM, Simon Van Eyndhoven < Simon.VanEyndhoven at esat.kuleuven.be> wrote: > Hello Vladimir, > > Thank you for the swift response. I made sure to place the source at > varying distances from the skull, to preclude any effect because of this. > More precisely, I tried the following approach: > > -- > elec = ft_read_sens('standard_1020.elc') > chan_name = 'F8'; > chan_index = find(strcmp(elec.label,chan_name)); > chan_pos = elec.elecpos(chan_index,:); > > [...] > > > cfg.dip.pos = 0.3*chan_pos; % I tried distances ranging from > 0.3*chan_pos to 0.9*chan_pos > -- > > For any distance from the skull the model yielded a nonsensical output as > described earlier. This leads me to believe that this distance is not the > determining factor here... > > Best regards, > > -- > Simon Van Eyndhoven > > PhD researcher > Stadius Center for Dynamical Systems, Signal Processing and Data Analytics > KU Leuven, Dept. Electrical Engineering (ESAT) > > email: simon.vaneyndhoven at esat.kuleuven.be > > On 2016-11-16 16:14, Vladimir Litvak wrote: > > Dear Simon, > > I tested this before and now re-tested again using the versions of FT and > BEMCP which come with SPM and should be quite recent. For SPM cortical mesh > where we take special measures to make sure that it's far enough from the > boundary the correlation coefficients between BEMCP and 3-spheres model are > all above 0.9. You say you varied the depth but have you varied it enough? > What 'enough' is depends on the density of your head meshes. For SPM meshes > it was more than 6mm from the boundary. The difference between different > BEM methods is how close you can get to the boundary without breaking. > BEMCP is the simplest method which is not very good in this respect. dipoli > or OpenMEEG will also break but closer to the surface. > > Best, > > Vladimir > > On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven < > Simon.VanEyndhoven at esat.kuleuven.be> wrote: > >> Dear all, >> >> I recently started using FieldTrip in order to simulate pseudo-realistic >> EEG recordings from e.g. a set of dipoles located in the brain. >> Therefore, I tried to follow the tutorial on the construction of a BEM >> head model based on an anatomical MRI scan and an electrode set: >> http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . >> >> The problem is that I don't succeed to reproduce the head model that is >> distributed as a template in the FieldTrip toolbox: >> '/template/headmodel/standard_bem.mat'. I start from the template MRI >> scan in FieldTrip ('standard_mri.mat') and use the following code (taken >> from the tutorial). >> >> --- >> % Load MRI data and electrode setup >> mri = importdata('standard_mri.mat'); >> elec = ft_read_sens('standard_1020.elc') >> >> % Segment the MRI volume >> cfg = []; >> cfg.output = {'brain','skull','scalp'}; >> segmentedmri = ft_volumesegment(cfg, mri); >> >> % Create a mesh >> cfg = []; >> cfg.tissue = {'brain','skull','scalp'}; >> cfg.numvertices = [1500 1000 500]; >> bnd = ft_prepare_mesh(cfg,segmentedmri); >> >> % Construct the headmodel >> cfg = []; >> cfg.conductivity = [0.3300 0.0041 0.3300]; >> cfg.method = 'dipoli'; >> vol = ft_prepare_headmodel(cfg, bnd); >> --- >> >> The following error is thrown: >> >> "Fatal error in dipoli: during computation of B-matrix; >> vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of >> /tmp/tp39371164882184732_2.tri" >> >> I've tried to create meshes with different numbers of vertices (e.g. the >> combination from the website tutorial 'numvertices = [3000 2000 1000]' >> produces the same error): for some combinations this problem does not >> occur, luckily. However, I wonder how the template model (that used the >> aforementioned number of vertices) got created successfully; I want to make >> sure that I work in the correct way and don't forget something. >> >> Can anyone shed some light on this issue, or hint what might be >> wrong/missing in the implementation above, compared to the template results >> that are provided in the FieldTrip distribution? >> >> Moreover, I've tried to create the head model using the 'bemcp' method as >> well. As a test, I placed a dipole close to the scalp surface, near a >> particular electrode. It is hence expected that the recorded amplitudes are >> highest for electrodes in the vicinity of this electrode. This effect is >> correctly reproduced when performing the computation of the forward problem >> using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' >> method). By contrast, the output of the forward problem using the >> bemcp-based head model shows a topographic distribution with many positive >> 'peaks' and negative 'troughs', spread kind of randomly over the scalp >> surface. Since neither the segmentation step nor meshing step changed (only >> the final step, namely the creation of the volume conduction model), I have >> no clue why this problem occurs. I've looked around in the discussion >> list's archives for possible causes: >> - it is reported that the bemcp method does not behave well if the dipole >> is located (very) close to the skull: I ruled this out for my problem by >> varying this distance >> - one user (Debora Desideri) reported a problem with the bemcp-method >> that appears to be very similar to mine, but there was no reply to her >> problem... >> - another user (John Richards) calls for caution when using the >> bemcp-method, stating that it behaves poorly sometimes >> >> Has anyone experienced similar issues when using the 'becmp' method and >> maybe found an explanation for this? >> >> Thanks in advance for any help! >> >> Best regards, >> >> -- >> Simon Van Eyndhoven >> >> PhD researcher >> Stadius Center for Dynamical Systems, Signal Processing and Data Analytics >> KU Leuven, Dept. Electrical Engineering (ESAT) >> >> email: simon.vaneyndhoven at esat.kuleuven.be >> >> _______________________________________________ >> 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.augusti at unsw.edu.au Fri Nov 18 00:59:05 2016 From: f.augusti at unsw.edu.au (f.augusti at unsw.edu.au) Date: Thu, 17 Nov 2016 23:59:05 +0000 Subject: [FieldTrip] hdr format correction Message-ID: Hi, I have some problems with the correct format of the reader. My data has been recorded in poly32 format and read by tms_read function. My header contains the following fields, but for some reason it's not being read from ft_definetrials and no event type are recognised: data.hdr.filename= 2048 data.hdr.path=67 data.hdr.chantype= 66x1 cell data.hdr.chanunit=1x67 cell data.hdr.label=68x1 cell data.hdr.nTrials=1 data.hdr.nSamplesPre=0 data.hdr.nSamples =776220 data.hdr.nChans=67 data.hdr.Fs=2048 >>cfg = []; cfg.dataset = 'FAonNC16112016.mat'; cfg.trialdef.eventtype = '?'; ft_definetrial(cfg); evaluating trialfunction 'ft_trialfun_general' Error using ft_read_header (line 2248) unsupported header format "matlab" Error in ft_trialfun_general (line 78) hdr = ft_read_header(cfg.headerfile, 'headerformat', cfg.headerformat); Error in ft_definetrial (line 177) [trl, event] = feval(cfg.trialfun, cfg); -------------- next part -------------- An HTML attachment was scrubbed... URL: From cecile.issard at etu.parisdescartes.fr Fri Nov 18 15:42:26 2016 From: cecile.issard at etu.parisdescartes.fr (Cecile Issard) Date: Fri, 18 Nov 2016 14:42:26 +0000 Subject: [FieldTrip] samples present in multiple trials Message-ID: <83D31873-260A-4C54-B941-FDB4B63D3B6F@etu.parisdescartes.fr> Hello, I got this error message when I used ft_databrowser : Warning: samples present in multiple trials, using only the last occurence of each sample > In ft_fetch_data at 145 In ft_databrowser>redraw_cb at 1544 In ft_databrowser>keyboard_cb at 1311 When preprocessing, I used cfg.trialdef.prestim = 3; cfg.trialdef.poststim = 4; and cfg.padding=10; cfg.padtype=‘data’; If I understand well, 1.5 seconds of supplementary data are added before and after each trial before filtering, but removed after filtering. Do I get this error message because my trials overlap with padding ? If yes why is it still the case after the filters have been applied ? I intentionally programmed ISI of 3 to 4 s to avoid overlaps (without padding). Also how should I choose the duration of padding ? Best wishes, Cécile -- Cécile Issard PhD student Laboratoire Psychologie de la Perception - UMR8242 45 rue des Sts Pères 75270 Paris cedex 06 http://lpp.psycho.univ-paris5.fr/index.php 01.70.64.99.69 @CecileIcecile -------------- next part -------------- An HTML attachment was scrubbed... URL: From cwilling at bu.edu Fri Nov 18 17:10:04 2016 From: cwilling at bu.edu (Carly Rose Willing) Date: Fri, 18 Nov 2016 11:10:04 -0500 Subject: [FieldTrip] Units of ERPS Message-ID: <2561EEF9-7246-4342-B989-9FC6140094C5@bu.edu> Hello, When I was plotting an ERP using ft_singleplotER from some BVA EEG data, I was wondering what unit the y-axis should be in by default. I want the y-axis to be in terms of μV, and it is obvious from comparison with my BVA output that this is not just a matter of scale. Does anyone know how to code to change the scaling/units? Thank you in advance! Carly Rose Willing --- Carly Rose Willing Boston University 2018 Lab Assistant Laboratory of Visual Cognitive Neuroscience Department of Psychological and Brain Sciences -------------- next part -------------- An HTML attachment was scrubbed... URL: From cecile.issard at etu.parisdescartes.fr Fri Nov 18 22:15:28 2016 From: cecile.issard at etu.parisdescartes.fr (Cecile Issard) Date: Fri, 18 Nov 2016 21:15:28 +0000 Subject: [FieldTrip] add statistical dispersion on erp plots Message-ID: Hello, Is there a way to add a measure of statistical dispersion (standard error of the mean or standard deviation) around ERPs in ft_singleplotER or ft_multiplotER ? Best wishes, Cécile -- Cécile Issard PhD student Laboratoire Psychologie de la Perception - UMR8242 45 rue des Sts Pères 75270 Paris cedex 06 http://lpp.psycho.univ-paris5.fr/index.php 01.70.64.99.69 @CecileIcecile -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Mon Nov 21 13:17:48 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Mon, 21 Nov 2016 12:17:48 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Message-ID: Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From davide.tabarelli at unitn.it Tue Nov 22 10:08:04 2016 From: davide.tabarelli at unitn.it (Davide Tabarelli) Date: Tue, 22 Nov 2016 10:08:04 +0100 Subject: [FieldTrip] Elekta Neuromag SSP projectors Message-ID: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> Dear community, my name is Davide Tabarelli and I’m currently working in the MEG lab @ Center for Mind Brain Sciences (Trento). I’m writing to get some formation about SSP projectors saved in Elekta Neuromag fif files. I know how the MNE python pipeline works (loading projectors & application on request) but I didn’t find information on how Filedtrip deals with these SSP projectors when loading data. Are they automatically applied? Are they discarded? Are they stored somewhere? Thank you in advance for your help. Have a nice day. D. — Davide Tabarelli, Ph.D. Center for Mind Brain Sciences (CIMeC) University of Trento, Via delle Regole, 101 38123 Mattarello (TN) Italy From mona at sdsc.edu Wed Nov 23 23:52:27 2016 From: mona at sdsc.edu (Wong-Barnum, Mona) Date: Wed, 23 Nov 2016 22:52:27 +0000 Subject: [FieldTrip] shifting data time In-Reply-To: References: Message-ID: Hi Anne: Thank you so much for your thoughtful suggestion! I will give it a try. Yes, we would like to have all the time continuous because we need to make some analysis based on the entire duration. Happy Thanksgiving (if you are in the US)! cheers, Mona On Oct 28, 2016, at 1:36 AM, anne Hauswald > wrote: Hi Mona, if you do cfg=[]; all=ft_appenddata(cfg, data1, data2, data3, data4); you get among the other fields all= time: {1x4 cell} %assuming each dataset equals one trial, which is what you want to replace. you could try to create a matrix, based on your sampling rate with all the continuous time points, here just for illustration: all_timepoints=[0:0.025:9.975] %adapt to your sampling rate and length of data, be very sure the end of one file is the beginning of the next one. (This is giving you all points from 0 to 9.975 with steps of 0.025) trl_timepoints=mat2cell(all_timepoints, 1, [100 100 100 100]) %here I create 4 cell arrays, corresponding e.g. to 4 trials will result in trl_timepoints = [1x100 double] [1x100 double] [1x100 double] [1x100 double] with continuous time information (trial 1 starts at 0, trial 2 at 2.5, trials 3 at 5, and trial 4 at 7.5) of the same length. this you can then use to replace the original time information: all_data.time_old=all_data.time %if you want to keep the original time information all_data.time=trl_timepoints In general: are you really sure that you need the continuous time information? There are many processing and analyses steps that can be done on the appended time information… There might be more elegant ways to do this. Hope there is no typo and that it helps Best Anne ********************************************* Mona Wong Web & iPad Application Developer San Diego Supercomputer Center "To handle yourself, use your head; to handle others, use your heart." -- Eleanor Roosevelt ********************************************* -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Thu Nov 24 11:12:22 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Thu, 24 Nov 2016 10:12:22 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Message-ID: <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> Hi Iris, I assume that you want to compare if spectra are different between these conditions. In that case, I would advise you to take a look at the Fieldtrip TFR-statistics tutorial, http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq. it explains how you can use cluster-based permutation to look for differences between spectra, within or between subjects. In your case, instead of comparing a baseline with an activation epoch (as described in the tutorial), you compare your (baseline-corrected) conditions A and B. Best, Stan From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Steinmann, Iris Sent: maandag 21 november 2016 13:18 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Thu Nov 24 17:02:26 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Thu, 24 Nov 2016 16:02:26 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> Message-ID: Hi Stan, Thanks a lot for your answer! The permutation test you suggested is exactly what I have done. But it gives me “only” the time-frequency bins that show a significant difference between TFR(A) and TFR(B). To visualize which of the conditions has higher or lower activity in the significant time-frequency bins I want to show a subtraction plot (TFR(A) minus TFR(B)) So, I’m not sure if I understood your answer correctly but I’m still struggling with the same two questions: 1. Is there anything wrong with subtracting TFR’s after baseline correction? 2. Is it anyhow valid to perform a linear operation (subtraction) on squared data (TFR) Sorry for still bothering you with this topic, but if anyone has an easy explanation, I would really appreciate. Thanks! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Donnerstag, 24. November 2016 11:12 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Iris, I assume that you want to compare if spectra are different between these conditions. In that case, I would advise you to take a look at the Fieldtrip TFR-statistics tutorial, http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq. it explains how you can use cluster-based permutation to look for differences between spectra, within or between subjects. In your case, instead of comparing a baseline with an activation epoch (as described in the tutorial), you compare your (baseline-corrected) conditions A and B. Best, Stan From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Steinmann, Iris Sent: maandag 21 november 2016 13:18 To: FieldTrip discussion list > Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From sherrykhan78 at gmail.com Fri Nov 25 06:39:42 2016 From: sherrykhan78 at gmail.com (Sheraz Khan) Date: Fri, 25 Nov 2016 00:39:42 -0500 Subject: [FieldTrip] Two Postdoctoral positions at the Manoach Lab @ MGH / Harvard Medical School Message-ID: On Behalf of Dr. Manoach: [Apologies for cross posting] Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Clinical/Cognitive Neuroscience Research Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with cognitive neuroscience, neuroimaging methodology including MEG and/or EEG and data analysis, and is interested in developing research questions and optimizing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. Requirements: PhD (or MD) in cognitive neuroscience, psychology or a related discipline and a strong research background are required. The ideal candidate will have a background in MEG and/or EEG methodology; research experience with clinical populations; experience in task design and analysis for cognitive experiments; fluency with statistical analysis packages. A background in computational neuroscience and/or signal processing (e.g., in Matlab) are beneficial PS. Here they are, distribute however you see fit. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach >>. Stipend levels are in line with experience and NIH. A two-year commitment is required. Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Signal Processing/Computational Neuroscience/Methodological Innovation Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with MEG/EEG methodology and data analysis, comfortable with methodological innovation, and is interested in optimizing and developing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. This is an excellent opportunity for scientists with a strong interest in clinical multimodal neuroimaging research. Requirements: PhD in neuroscience, engineering or a related field and a strong research background are required. Ideal candidates would have extensive experience in data analysis and/or an engineering background, be proficient in Matlab/Python and be interested in methods development. The following are beneficial: experience with MEG/EEG data analysis/methodology; background in cognitive neuroscience, experimental psychology and sleep; interest in clinical applications. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach >>. Stipend levels are in line with experience and NIH. A two-year commitment is required. ------------------------- Sheraz Khan, M.Eng, Ph.D. Instructor in Neurology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School McGovern Institute for Brain Research Massachusetts Institute of Technology Tel: +1 617-643-5634 Fax: +1 617-948-5966 Email: sheraz at nmr.mgh.harvard.edu sheraz at mit.edu Web: http://sheraz.mit.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From roycox.roycox at gmail.com Fri Nov 25 18:52:54 2016 From: roycox.roycox at gmail.com (Roy Cox) Date: Fri, 25 Nov 2016 12:52:54 -0500 Subject: [FieldTrip] 2 postdoctoral positions (Clinical/Cognitive Neuroscience Research & Signal Processing/Computational Neuroscience/Methodological Innovation) Message-ID: hello, On behalf of Dr. Dara Manoach I'm posting these two postdoctoral opportunities on the role of sleep in memory consolidation in healthy and clinical populations. Best, Roy ------------------------------------------------------------------------------------------------------ Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Clinical/Cognitive Neuroscience Research Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with cognitive neuroscience, neuroimaging methodology including MEG and/or EEG and data analysis, and is interested in developing research questions and optimizing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. Requirements: PhD (or MD) in cognitive neuroscience, psychology or a related discipline and a strong research background are required. The ideal candidate will have a background in MEG and/or EEG methodology; research experience with clinical populations; experience in task design and analysis for cognitive experiments; fluency with statistical analysis packages. A background in computational neuroscience and/or signal processing (e.g., in Matlab) are beneficial PS. Here they are, distribute however you see fit. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach . Stipend levels are in line with experience and NIH. A two-year commitment is required. ----------------------------------------------------------------------------------------------- Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Signal Processing/Computational Neuroscience/Methodological Innovation Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with MEG/EEG methodology and data analysis, comfortable with methodological innovation, and is interested in optimizing and developing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. This is an excellent opportunity for scientists with a strong interest in clinical multimodal neuroimaging research. Requirements: PhD in neuroscience, engineering or a related field and a strong research background are required. Ideal candidates would have extensive experience in data analysis and/or an engineering background, be proficient in Matlab/Python and be interested in methods development. The following are beneficial: experience with MEG/EEG data analysis/methodology; background in cognitive neuroscience, experimental psychology and sleep; interest in clinical applications. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach . Stipend levels are in line with experience and NIH. A two-year commitment is required. -------------- next part -------------- An HTML attachment was scrubbed... URL: From weiyong.w.xu at jyu.fi Mon Nov 28 15:21:31 2016 From: weiyong.w.xu at jyu.fi (Xu, Weiyong) Date: Mon, 28 Nov 2016 14:21:31 +0000 Subject: [FieldTrip] MNE cortical sheet parcellation with AAL atlas Message-ID: Dear all, After MNE source analysis with the template MRI(Colin27), I want to do parcellation with the AAL atlas. So first I checked how well the template cortical sheet and AAL atlas fit with the following code: -------------------------------------------------------------- aal = ft_read_atlas('C:\MyTemp\Toolbox\fieldtrip\fieldtrip_git\template\atlas\aal\ROI_MNI_V4.nii'); mne_sourcemodel=ft_read_headshape('cortex_8196.surf.gii'); cfg = []; cfg.interpmethod = 'nearest'; cfg.parameter = 'tissue'; mne_sourcemodel_with_label = ft_sourceinterpolate(cfg, aal, mne_sourcemodel); disp(mne_sourcemodel_with_label.tissuelabel') for i=1:length(mne_sourcemodel_with_label.tissue) if mne_sourcemodel_with_label.tissue(i)==0; mne_sourcemodel_with_label.tissue(i)=200; end; end; ft_plot_mesh(mne_sourcemodel_with_label,'vertexcolor',mne_sourcemodel_with_label.tissue,'edgecolor','black') colorbar --------------------------------------------------------- The result looks like that the parcellation of the sulci are not very good. Also parts of the cerebellum are included after interpolation. So I want to ask if there are surface-based atlas available (preferably also based on the Colin27)? And also I noticed the freesurfer pipeline creates cortical parcellation such as the Destrieux atlas, could I somehow utilize this in creating surface-based atlas for my MNE source model? Thanks in advance. Best, Weiyong Xu Ph.D. student Department of Psychology University of Jyväskylä -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: parcellation_with_AAL.pdf Type: application/pdf Size: 454680 bytes Desc: parcellation_with_AAL.pdf URL: From t.schneider.uke at icloud.com Mon Nov 28 16:11:26 2016 From: t.schneider.uke at icloud.com (Till Schneider) Date: Mon, 28 Nov 2016 16:11:26 +0100 Subject: [FieldTrip] PhD position in Cognitive Neuroscience Message-ID: Dear Fieldtrip community, please find attached a job offer for a PhD position in Cognitive Neuroscience in Hamburg, Germany. Best regards, Till Schneider — Dr. Till Schneider Cognitive and Clinical Neurophysiology Group Dept. of Neurophysiology and Pathophysiology University Medical Center Hamburg-Eppendorf Martinistr. 52 20246 Hamburg Germany phone +49-40-7410-53188 fax +49-40-7410-57126 www.uke.de/neurophysiology t.schneider at uke.de -------------- next part -------------- A non-text attachment was scrubbed... Name: Job offer Doctoral Student SPP1665.pdf Type: application/pdf Size: 74606 bytes Desc: not available URL: From christine.blume at sbg.ac.at Mon Nov 28 16:46:08 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 28 Nov 2016 15:46:08 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Message-ID: Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 28 17:03:31 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 28 Nov 2016 16:03:31 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment In-Reply-To: References: Message-ID: Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From guiraudh at gmail.com Mon Nov 28 18:42:58 2016 From: guiraudh at gmail.com (=?UTF-8?B?SMOpbMOobmUgR3VpcmF1ZA==?=) Date: Mon, 28 Nov 2016 18:42:58 +0100 Subject: [FieldTrip] Coherence values corresponding to the statistically significant area Message-ID: Dear Fieldtrip community, I'm working on coherence measures between MEG signal and auditory signal perceived during MEG recording. I realized sources analysis and statistics analysis with cluster-based permutation test (Montecarlo method). However I would like to have the coherence values corresponding to my statistically significant area, and I can not get it. Is it possible? I thank you in advance. Best, Hélène -------------- next part -------------- An HTML attachment was scrubbed... URL: From nancygao260 at hotmail.com Tue Nov 29 04:36:10 2016 From: nancygao260 at hotmail.com (gao nuo) Date: Tue, 29 Nov 2016 03:36:10 +0000 Subject: [FieldTrip] problems in emotiv fieldtrip and matlab Message-ID: Dear Sir: I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. What I did is : 1. my matlab is matlab R2014a, 2,download the fieldtrip 20161108; 3. add all the files to the matlab path; 4. installed MinGW and set the path of the /bin in environmental variables; 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. 6. run buffer.exe. 7 . run cmd.exe; 8. go to fieldtrip-20161108/realtime/bin/win32; 9. input the command line: emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] the response is: passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port 10. run viewer.exe, push connect botton. but no response. I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. thanks for your reading and look forward to the suggestions. best wishes. Gao Nuo -------------- next part -------------- An HTML attachment was scrubbed... URL: From nancygao260 at hotmail.com Tue Nov 29 04:45:15 2016 From: nancygao260 at hotmail.com (gao nuo) Date: Tue, 29 Nov 2016 03:45:15 +0000 Subject: [FieldTrip] Fw: problems in emotiv fieldtrip and matlab In-Reply-To: References: Message-ID: ________________________________ From: fieldtrip-bounces at science.ru.nl on behalf of gao nuo Sent: Monday, November 28, 2016 6:36 PM To: fieldtrip at science.ru.nl Subject: [FieldTrip] problems in emotiv fieldtrip and matlab Dear Sir: I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. What I did is : 1. my matlab is matlab R2014a, 2,download the fieldtrip 20161108; 3. add all the files to the matlab path; 4. installed MinGW and set the path of the /bin in environmental variables; 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. 6. run buffer.exe. 7 . run cmd.exe; 8. go to fieldtrip-20161108/realtime/bin/win32; 9. input the command line: emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] the response is: passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port 10. run viewer.exe, push connect botton. but no response. I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. thanks for your reading and look forward to the suggestions. best wishes. Gao Nuo -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Tue Nov 29 08:50:00 2016 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Tue, 29 Nov 2016 08:50:00 +0100 Subject: [FieldTrip] problems in emotiv fieldtrip and matlab In-Reply-To: References: Message-ID: <2182D0F4-2698-4C33-AF99-66DB9C79D074@donders.ru.nl> Dear Gao, it seems to me that in your step 9 the emotiv2ft did not actually start properly. It prints a help message out on screen, which I think it would only do if it did could not make sense of the command line options. If you specified verbatim "emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]]” then it would indeed not work. You should specify the right options in the brackets. The square [] brackets are optional, the <> brackets are required. So you should at least specify the configuration file. The defaults for the last three options (localhost, 1972 and 8000) should be ok, since you started the buffer in step 6 on the localhost with the default port (which is 1972). best regards, Robert > On 29 Nov 2016, at 04:36, gao nuo wrote: > > Dear Sir: > I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. > What I did is : > 1. my matlab is matlab R2014a, > 2,download the fieldtrip 20161108; > 3. add all the files to the matlab path; > 4. installed MinGW and set the path of the /bin in environmental variables; > 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. > 6. run buffer.exe. > 7 . run cmd.exe; > 8. go to fieldtrip-20161108/realtime/bin/win32; > 9. input the command line: > emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] > the response is: > passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port > > 10. run viewer.exe, push connect botton. but no response. > > I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. > > thanks for your reading and look forward to the suggestions. > > best wishes. > Gao Nuo > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From gaetan.sanchez at sbg.ac.at Tue Nov 29 10:13:24 2016 From: gaetan.sanchez at sbg.ac.at (Gaetan) Date: Tue, 29 Nov 2016 10:13:24 +0100 Subject: [FieldTrip] Salzburg Mind Brain Annual (SAMBA) Meeting 2017 - Announcement Message-ID: <7e7b674d-ee40-d29d-9e4f-a3b8128971e7@sbg.ac.at> Dear all, apologies in advance if you should receive this mail multiple times due to cross-posting on different lists. On behalf also of my colleagues and our advisory board, I am happy to announce the /*Salzburg Mind Brain Annual Meeting*/ (SAMBA) which will take place on the 13.-14. July 2017. Confirmed speakers for the upcoming event are: • Ole Jensen (Birmingham) • Catherine Tallon-Baudry (Paris) • Pascal Fries (Frankfurt) • Tobias Donner (Hamburg) • Angelika Lingnau (London) • Sylvain Baillet (Montreal) • Rosalyn Moran (Bristol) • Jan Mathijs Schoffelen (Nijmegen) • Christian-G. Bénar (Marseille) The workshop will be rather small (~100 participants) to enable lots of occasion for interactions. You will have the possibility to present a poster (please indicate while registering, with -at least- a tentative title. *The participation for SAMBA2017 is free*. For more information see the workshop website: https://samba.ccns.sbg.ac.at Also, prior to the workshop (11. & 12.07) there will be a Fieldtrip workshop help by Robert Oostenveld and Jan Mathijs Schoffelen. There are ~20 places for this event. *The participation for the Fieldtrip workshop is for free*. Registration is on the same site as above. So if you play it smart you can be part of 2 neuroscience highlights in 2017! Best, Nathan --------------------------------------------- Nathan Weisz Centre for Cognitive Neuroscience Division of Physiological Psychology University of Salzburg nathan.weisz at sbg.ac.at www.oboblab.at -- Gaëtan Sanchez, PhD Centre for Cognitive Neuroscience Hellbrunnerstraße 34, 5020 Salzburg - Austria Tel: +43 662 804 451 61 -------------- next part -------------- An HTML attachment was scrubbed... URL: From skelly2 at ccny.cuny.edu Tue Nov 29 12:48:25 2016 From: skelly2 at ccny.cuny.edu (Simon Kelly) Date: Tue, 29 Nov 2016 11:48:25 +0000 Subject: [FieldTrip] Postdoc opening in perceptual/cognitive neuroscience Message-ID: Applications are invited for a postdoctoral research post in the Cognitive Neural systems lab (https://cogneusys.com/) led by Simon Kelly, to study computational and neural mechanisms of value-biased sensorimotor decision making under time pressure. This position is part of a project funded by Science Foundation Ireland, which involves a combination of psychophysics, computational modelling, non-invasive electrophysiology of human brain and muscle, and analyses of existing single-cell neurophysiological data. Though involvement is expected in all of these aspects, the most critical role of the postdoctoral researcher will be in computational modelling. Candidates must thus have strong analytic and programming skills, and specific experience in the computational modelling of cognitive processes. Candidates must also be highly motivated and have excellent communication skills. Dr. Kelly's electrophysiology/psychophysics lab is situated within the School of Electrical and Electronic Engineering in University College Dublin, Ireland, and has strong collaborative links to cognitive and clinical neuroscience research groups both locally (e.g. Trinity College Institute of Neuroscience) and internationally (e.g. City College and Columbia University in New York). The successful applicant will have ample opportunities for wider collaborations and the learning of new skills. Interested candidates should submit a brief research statement and CV including publications through the UCD job vacancies site (http://www.ucd.ie/hr/jobvacancies/ - search for keyword 'kelly', or job ref 008870). Informal enquiries can be directed to Simon (simon.kelly at ucd.edu). Candidates should explain in their statement how their own research interests fit with those of the Kelly lab. The deadline for submitting applications is Jan 8th 2017, and shortlisting and interviews will take place shortly after that. ----------------------------------------------------------- Simon Kelly, Ph.D. Associate Professor School of Electrical and Electronic Engineering University College Dublin t: +353 (1) 716 1803 e: simon.kelly at ucd.ie -----------------------------------------------------------​ -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Wed Nov 30 09:57:21 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 30 Nov 2016 08:57:21 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Message-ID: Maybe an example will stimulate some discussion ;-). I test an interaction among two factors with two levels each with regard to the ERPs elicited by each stimulus. Let's say we present two stimuli (house vs. face) in colour (black/white vs. colour). The interaction is significant and we want to know which factor levels differ using post hoc tests using the following cfg. cfg = []; cfg.latency = [0 1]; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_depsamplesT'; cfg.correctm = 'cluster'; cfg.clusteralpha = 0.05; cfg.clusterstatistic = 'maxsum'; cfg.minnbchan = 2; cfg.tail = 0; cfg.clustertail = 0; cfg.alpha = 0.025; cfg.numrandomization = 1000; cfg_neighb.method = 'distance'; cfg.neighbours = ft_prepare_neighbours(cfg_neighb, data_sample); cfg.design = design; cfg.ivar = 2; cfg.uvar = 1; What we get is the following. stat_erp.FACE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.FACE_BW); stat_erp.FACE_COLOURvsFACE_BW.posclusters.prob % 18 clusters, p > 0.4076 stat_erp.FACE_COLOURvsFACE_BW.negclusters.prob % 12 clusters, p > 0.2957 stat_erp.FACE_COLOURvsHOUSE_COLOUR = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_COLOUR); stat_erp.FACE_COLOURvsHOUSE_COLOUR.posclusters.prob % 10 clusters, p > 0.4326 stat_erp.FACE_COLOURvsHOUSE_COLOUR.negclusters.prob % 12 clusters, p = 0.0599; p > 0.8631 stat_erp.FACE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_COLOURvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0360, p > 0.7742 stat_erp.FACE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0300, p > 0.5105 stat_erp.HOUSE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.FACE_BW); stat_erp.HOUSE_COLOURvsFACE_BW.posclusters.prob % 16 clusters, p > 0.2697 stat_erp.HOUSE_COLOURvsFACE_BW.negclusters.prob % 11 clusters, p = 0.0090, p > .0.9291 stat_erp.HOUSE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.HOUSE_COLOURvsHOUSE_BW.posclusters.prob % 19 clusters, p = 0.0020, p = 0.0979, p > 0.2468 stat_erp.HOUSE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0480, p = 0.0699, p > 0.3606 stat_erp.FACE_BWvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_BWvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0410, p > 0.7732 stat_erp.FACE_BWvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0320, p > 0.4725 Now the question is: how do we correct for these 6 follow-up tests? If we say we want to use the Bonferroni-Holm correction for example, we have to sort p-values according to size. However, we have two p-values per test, one for positive and one for negative clusters and Bonferroni-Holm assumes we only have one per test. Any ideas or suggestions how to best handle this? Best, Christine Von: Blume Christine Gesendet: Montag, 28. November 2016 17:04 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: AW: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Wed Nov 30 10:25:02 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 30 Nov 2016 09:25:02 +0000 Subject: [FieldTrip] File Size depends on No. of Trials? Message-ID: Dear Community, I have an issue with data size. I am processing high-density EEG data from a sleep study. Following preprocessing I perform time-frequency transformations and a baseline correction using the following code: cfg = []; cfg.method = 'wavelet'; cfg.output = 'pow'; cfg.keeptrials = 'no'; cfg.width = 3; % cfg.foi = 1:1:16; % cfg.toi = -0.7:0.2:0.9; freqanalysis_FV = ft_freqanalysis(cfg, data_FV); cfg = []; cfg.baseline = [-0.6, 0]; cfg.baselinetype = 'relchange'; ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) As you can see, I do not keep the trials. Still, the size of ERDERS seems to depend on the size of data_FV. Why is that the case? Also, changing cfg.foi does not change the file size - why? The problem is that this way the data can hardly be handled as it takes up so much RAM. Please note that I do clear everything from the workspace I do not need, that should not be the issue. Thanks a lot for your thoughts. Best, Christine -------------- next part -------------- An HTML attachment was scrubbed... URL: From julian.keil at gmail.com Wed Nov 30 10:31:37 2016 From: julian.keil at gmail.com (Julian Keil) Date: Wed, 30 Nov 2016 10:31:37 +0100 Subject: [FieldTrip] File Size depends on No. of Trials? In-Reply-To: References: Message-ID: Hi Christine, take a look into the cfg.previous of ERDERS. Fieldtrip stores information on previous data analysis steps there. Maybe the trial information is hidden somewhere in there. The MatLab command rmfield can then be used to remove some unwanted fields from a structure. But please beware! There is a good reason for the thorough bookkeeping in Fieldtrip (which has saved me quite often). Good luck, Julian On Wed, Nov 30, 2016 at 10:25 AM, Blume Christine wrote: > Dear Community, > > > > I have an issue with data size. I am processing high-density EEG data from > a sleep study. Following preprocessing I perform time-frequency > transformations and a baseline correction using the following code: > > > > cfg = []; > > cfg.method = 'wavelet'; > > cfg.output = 'pow'; > > cfg.keeptrials = 'no'; > > cfg.width = 3; % > > cfg.foi = 1:1:16; % > > cfg.toi = -0.7:0.2:0.9; > > freqanalysis_FV = ft_freqanalysis(cfg, data_FV); > > > > cfg = []; > > cfg.baseline = [-0.6, 0]; > > cfg.baselinetype = 'relchange'; > > ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) > > > > As you can see, I do *not* keep the trials. Still, the size of ERDERS > seems to depend on the size of data_FV. Why is that the case? Also, > changing cfg.foi does not change the file size – why? The problem is that > this way the data can hardly be handled as it takes up so much RAM. Please > note that I do clear everything from the workspace I do not need, that > should not be the issue. > > > > Thanks a lot for your thoughts. > > > > Best, > > Christine > > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Claudio.Georgii at stud.sbg.ac.at Wed Nov 30 11:50:46 2016 From: Claudio.Georgii at stud.sbg.ac.at (Claudio Georgii) Date: Wed, 30 Nov 2016 11:50:46 +0100 Subject: [FieldTrip] File Size depends on No. of Trials? In-Reply-To: References: Message-ID: Hi Christine, In addition you could change the precision from double to single (which reduces the working memory needed by a half). As far as i know, double precision is not needed here and single should do fine, but please correct me if I am wrong. Claudio 2016-11-30 10:31 GMT+01:00 Julian Keil : > Hi Christine, > > take a look into the cfg.previous of ERDERS. Fieldtrip stores information > on previous data analysis steps there. Maybe the trial information is > hidden somewhere in there. > The MatLab command rmfield can then be used to remove some unwanted fields > from a structure. But please beware! There is a good reason for the > thorough bookkeeping in Fieldtrip (which has saved me quite often). > Good luck, > > Julian > > On Wed, Nov 30, 2016 at 10:25 AM, Blume Christine < > christine.blume at sbg.ac.at> wrote: > >> Dear Community, >> >> >> >> I have an issue with data size. I am processing high-density EEG data >> from a sleep study. Following preprocessing I perform time-frequency >> transformations and a baseline correction using the following code: >> >> >> >> cfg = []; >> >> cfg.method = 'wavelet'; >> >> cfg.output = 'pow'; >> >> cfg.keeptrials = 'no'; >> >> cfg.width = 3; % >> >> cfg.foi = 1:1:16; % >> >> cfg.toi = -0.7:0.2:0.9; >> >> freqanalysis_FV = ft_freqanalysis(cfg, data_FV); >> >> >> >> cfg = []; >> >> cfg.baseline = [-0.6, 0]; >> >> cfg.baselinetype = 'relchange'; >> >> ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) >> >> >> >> As you can see, I do *not* keep the trials. Still, the size of ERDERS >> seems to depend on the size of data_FV. Why is that the case? Also, >> changing cfg.foi does not change the file size – why? The problem is that >> this way the data can hardly be handled as it takes up so much RAM. Please >> note that I do clear everything from the workspace I do not need, that >> should not be the issue. >> >> >> >> Thanks a lot for your thoughts. >> >> >> >> Best, >> >> Christine >> >> >> >> _______________________________________________ >> 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 > -- Claudio Georgii, MSc. Phd student University of Salzburg - Department of Psychology Eating Behavior Laboratory Hellbrunnerstraße 34 5020 Salzburg - Austria Phone: 0043- (0)662 8044 5164 E-Mail: claudio.georgii at sbg.ac.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christophe.grova at mcgill.ca Wed Nov 30 15:33:47 2016 From: christophe.grova at mcgill.ca (Christophe Grova) Date: Wed, 30 Nov 2016 14:33:47 +0000 Subject: [FieldTrip] Postdoctoral position for a neurologist/epileptologist available in the Multimodal Functional Imaging Lab, Montreal (Montreal Neurological Inst. McGill U. and PERFORM Concordia U.) In-Reply-To: References: , Message-ID: Dear all, please see below the opportunity for a postdoctoral position in my lab. The candidate will join a multidisciplinary team composed of neurologists and methodologists within the Multimodal Functional Imaging Laboratory, directed by Pr. Christophe Grova. The laboratory is actually based on two sites: (i) Physics Dpt at Concordia University and PERFORM center, (ii) Biomedical Engineering Dpt and epilepsy group of the Montreal Neurological Institute, McGill University. Both environments offer unique platforms with access to several modalities (simultaneous high-density EEG/fMRI, MEG, simultaneous EEG/NIRS, TMS). The main expertise of the team is the development and the validation of source localization methods dedicated for EEG, MEG and NIRS as well as multimodal characterization of resting state brain activity. Project: Multimodal investigation of epileptic activity using simultaneous EEG/MEG and EEG/NIRS acquisitions. The proposed project aims at localizing and characterizing the generators of epileptic activity using simultaneous acquisitions of ElectroEncephaloGraphy (EEG) with Magneto-EncephaloGraphy (MEG), as well as simultaneous acquisitions of EEG with Near Infra-Red Spectroscopy (NIRS). EEG and MEG are respectively measuring on the scalp electric and magnetic fields generated by neuronal activity at a millisecond scale, providing a detailed description of brain bioelectrical activity. Combined with EEG measuring brain electric activity on the scalp, NIRS allows studying hemodynamic processes at the time of spontaneous epileptic activity. The specificity of NIRS data is its ability to measure local changes oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), exploiting absorption properties of infrared light within brain tissue using optic fibers placed on the surface of the head (temporal resolution: 10 ms, 16 sources x 32 detectors, penetration: 2-3 cm from the surface of the head). While methodological developpments in the lab will consist in 3D reconstruction of the generators of EEG, MEG and NIRS signals and assessing multimodal concordances between bioelectrical neuronal signals and hemodynamic processes, the purpose of this Postdoctoral project will be to assess the integrity of neurovascular coupling processes at the time of epileptic discharges, using a unique multimodal environment involving EEG/MEG (Pellegrino et al HBM 2016), EEG/NIRS (Pellegrino et al Frontiers in Neurosc. 2016) and also EEG/fMRI recordings (Heers et al HBM 2014). Close collaborations with the epilepsy group of the Montreal Neurological Institute, involving notably Dr E. Kobayashi MD-PhD, Dr F. Dubeau MD-PhD and Dr. J. Gotman PhD, will provide access to patient populations and additional clinical expertise for this project. Requirements: The candidate should be an MD (neurologist) with previous training in epileptology and neurophysiology (EEG). Expertise in analyzing MEG or NIRS signals and/or computational skills including neuroimaging softwares are appreciated additional qualification. The candidate should be fluent in English (and if possible French) due to the patient population studied. Supervisor: Christophe Grova Ph.D. Assistant Professor, Physics Dpt and PERFORM, Concordia Univ. Chair of PERFORM Applied Bio-Imaging Committee Adjunct Professor, Biomedical Engineering and Neurology & Neurosurgery dpts, McGill Univ. Member Epilepsy Group, Montreal Neurological Institute Director of the Multimodal Functional Imaging Laboratory Email: christophe.grova at concordia.ca christophe.grova at mcgill.ca Please send your CV and motivation letter before Dec 15th 2016 to christophe.grova at concordia.ca *************************** Christophe Grova, PhD Assistant Professor, Physics Dpt, Concordia University PERFORM centre, Concordia University Chair of PERFORM Applied Bio-Imaging Committee (ABC) Adjunct Prof in Biomedical Engineering, and Neurology and Neurosurgery Dpt, McGill University Multimodal Functional Imaging Lab (Multi FunkIm) Montreal Neurological Institute - epilepsy group Centre de Recherches en Mathématiques Physics Dpt Concordia University - Loyola Campus - Office SP 365.12 7141 Sherbrooke Street West, Montreal, QC H4B 1R6 Phone: (514) 848-2424 ext.4221 email : christophe.grova at concordia.ca , christophe.grova at mcgill.ca Explore Concordia: http://explore.concordia.ca/christophe-grova Physics, Concordia University: http://www.concordia.ca/artsci/physics/faculty.html?fpid=christophe-grova McGill University: http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/PeopleChristophe MultiFunkIm Lab: http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/HomePage *************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 30 15:57:21 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 30 Nov 2016 14:57:21 +0000 Subject: [FieldTrip] Elekta Neuromag SSP projectors In-Reply-To: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> References: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> Message-ID: <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> Hi Davide, At the moment, there is no support for this in Fieldtrip. However, this issue has come up in the past, and back then a bug was filed in our bug tracking system bugzilla.fieldtriptoolbox.org. The bug id is 2109; it has been silent for a while, but if there’s sufficient interest in getting this implemented it might be worthwhile to revive it and get it done. Best wishes, Jan-Mathijs On 22 Nov 2016, at 10:08, Davide Tabarelli > wrote: Dear community, my name is Davide Tabarelli and I’m currently working in the MEG lab @ Center for Mind Brain Sciences (Trento). I’m writing to get some formation about SSP projectors saved in Elekta Neuromag fif files. I know how the MNE python pipeline works (loading projectors & application on request) but I didn’t find information on how Filedtrip deals with these SSP projectors when loading data. Are they automatically applied? Are they discarded? Are they stored somewhere? Thank you in advance for your help. Have a nice day. D. — Davide Tabarelli, Ph.D. Center for Mind Brain Sciences (CIMeC) University of Trento, Via delle Regole, 101 38123 Mattarello (TN) Italy _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From wanglinsisi at gmail.com Wed Nov 30 16:30:38 2016 From: wanglinsisi at gmail.com (Lin Wang) Date: Wed, 30 Nov 2016 10:30:38 -0500 Subject: [FieldTrip] Elekta Neuromag SSP projectors In-Reply-To: <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> References: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> Message-ID: Hi Davide and JM, Thanks for bring this up. I have the same question and I'd like to see how this can be implemented in fieldtrip. Best, LIn On Wed, Nov 30, 2016 at 9:57 AM, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Davide, > > At the moment, there is no support for this in Fieldtrip. > > However, this issue has come up in the past, and back then a bug was filed > in our bug tracking system bugzilla.fieldtriptoolbox.org. > The bug id is 2109; it has been silent for a while, but if there’s > sufficient interest in getting this implemented it might be worthwhile to > revive it and get it done. > > Best wishes, > Jan-Mathijs > > > > On 22 Nov 2016, at 10:08, Davide Tabarelli > wrote: > > Dear community, > > my name is Davide Tabarelli and I’m currently working in the MEG lab @ > Center for Mind Brain Sciences (Trento). > > I’m writing to get some formation about SSP projectors saved in Elekta > Neuromag fif files. > > I know how the MNE python pipeline works (loading projectors & application > on request) but I didn’t find information on how Filedtrip deals with these > SSP projectors when loading data. Are they automatically applied? Are they > discarded? Are they stored somewhere? > > Thank you in advance for your help. > > Have a nice day. > > D. > > — > Davide Tabarelli, Ph.D. > Center for Mind Brain Sciences (CIMeC) > University of Trento, > Via delle Regole, 101 > 38123 Mattarello (TN) > Italy > > > > > > _______________________________________________ > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 30 16:56:41 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 30 Nov 2016 15:56:41 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment In-Reply-To: References: Message-ID: <6DFB7B65-FBFB-4C9D-B84F-DB00A10EF204@donders.ru.nl> Hi Christine, I don’t have the answer to your specific question, but I want to raise a few points: Although the permutation framework as implemented in FT outputs 1 p-value per cluster (for a two-sided test, both for the ‘negative’ and for the ‘positive’ clusters), Only the smallest p-value counts for the statistical inference. This is because your inferential procedure is about making a binary decision, you either reject or accept your null-hypothesis. Also, note that in the permutation framework, without explicit adjustment, the p-values that come out reflect one-sided p-values. For a valid inference, you need to Bonferroni correct these (i.e. multiply them by 2), or adjust the critical alpha level. This being said, I would say that what you want to achieve (i.e. doing post-hoc tests) does not need to be done within the cluster-based framework. The clusters are just byproducts of your inferential procedure. Some general background on how to deal with the output of the tests can be found on: http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test In your specific case, where as a first step you have evaluated the interaction as a difference of differences, I would think it’s fine to use this result to justify a selection of channel + time points, across which you average the condition specific ERP, and which you subject to your post hoc tests. Best, Jan-Mathijs On 30 Nov 2016, at 09:57, Blume Christine > wrote: Maybe an example will stimulate some discussion ;-). I test an interaction among two factors with two levels each with regard to the ERPs elicited by each stimulus. Let’s say we present two stimuli (house vs. face) in colour (black/white vs. colour). The interaction is significant and we want to know which factor levels differ using post hoc tests using the following cfg. cfg = []; cfg.latency = [0 1]; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_depsamplesT'; cfg.correctm = 'cluster'; cfg.clusteralpha = 0.05; cfg.clusterstatistic = 'maxsum'; cfg.minnbchan = 2; cfg.tail = 0; cfg.clustertail = 0; cfg.alpha = 0.025; cfg.numrandomization = 1000; cfg_neighb.method = 'distance'; cfg.neighbours = ft_prepare_neighbours(cfg_neighb, data_sample); cfg.design = design; cfg.ivar = 2; cfg.uvar = 1; What we get is the following. stat_erp.FACE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.FACE_BW); stat_erp.FACE_COLOURvsFACE_BW.posclusters.prob % 18 clusters, p > 0.4076 stat_erp.FACE_COLOURvsFACE_BW.negclusters.prob % 12 clusters, p > 0.2957 stat_erp.FACE_COLOURvsHOUSE_COLOUR = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_COLOUR); stat_erp.FACE_COLOURvsHOUSE_COLOUR.posclusters.prob % 10 clusters, p > 0.4326 stat_erp.FACE_COLOURvsHOUSE_COLOUR.negclusters.prob % 12 clusters, p = 0.0599; p > 0.8631 stat_erp.FACE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_COLOURvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0360, p > 0.7742 stat_erp.FACE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0300, p > 0.5105 stat_erp.HOUSE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.FACE_BW); stat_erp.HOUSE_COLOURvsFACE_BW.posclusters.prob % 16 clusters, p > 0.2697 stat_erp.HOUSE_COLOURvsFACE_BW.negclusters.prob % 11 clusters, p = 0.0090, p > .0.9291 stat_erp.HOUSE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.HOUSE_COLOURvsHOUSE_BW.posclusters.prob % 19 clusters, p = 0.0020, p = 0.0979, p > 0.2468 stat_erp.HOUSE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0480, p = 0.0699, p > 0.3606 stat_erp.FACE_BWvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_BWvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0410, p > 0.7732 stat_erp.FACE_BWvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0320, p > 0.4725 Now the question is: how do we correct for these 6 follow-up tests? If we say we want to use the Bonferroni-Holm correction for example, we have to sort p-values according to size. However, we have two p-values per test, one for positive and one for negative clusters and Bonferroni-Holm assumes we only have one per test. Any ideas or suggestions how to best handle this? Best, Christine Von: Blume Christine Gesendet: Montag, 28. November 2016 17:04 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: AW: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 – 8044 5146 www.sleepscience.at _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From tigoum at naver.com Wed Nov 2 08:59:45 2016 From: tigoum at naver.com (=?UTF-8?B?7JWI66+87Z2s?=) Date: Wed, 2 Nov 2016 16:59:45 +0900 (KST) Subject: [FieldTrip] =?utf-8?q?_ft=5Fsourceanalysis_with_specific_EEG_data?= In-Reply-To: References: Message-ID: <341aa5e8a982eed4ceab879e19952f@cweb26.nm.nhnsystem.com> 대용량 첨부파일 1개(166MB)대용량 첨부 파일은 30일간 보관 / 100회까지 다운로드 가능 brainvision_EEG.zip 166MB 다운로드 기간: 2016/11/02 ~ 2016/12/02 Hello? I am a graduate student in Korea university , Korea. I have a own data that are exported from brainvision analyzer.It is consisting of 3 dimension such as 5 second interval, 240 epoch, 32 channel. I hope so analyzing by ft_sourceanalysis().Then I search the getting started & User documentation on fieldtrip homepage. And I found related information as "networkanalysis", it explain the usage of ft_sourceanalysiswith example MATLAB code. BUT, it is constructed for MEG dataset only, so I do not trying for my own EEG data as mentioned before. In the attached file, It include "networkanalysis.m" and my own data file.The "networkanalysis.m" is written by me as referred from fieldtrip homepage:http://www.fieldtriptoolbox.org/tutorial/networkanalysis?s[]=networkanalysis I really need your help. I really hope so analyzing my data by ft_sourceanalysis & eLORETA option. Please help me.Thank you very much. Best regards.Min-Hee, Ahn -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: networkanalysis.zip Type: application/x-zip-compressed Size: 10283 bytes Desc: not available URL: From Ramirez_U at ukw.de Wed Nov 2 11:41:11 2016 From: Ramirez_U at ukw.de (Ramirez Pasos, ) Date: Wed, 2 Nov 2016 10:41:11 +0000 Subject: [FieldTrip] Smoothing before permutation test Message-ID: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> Dear Fieldtrippers, I have a set of subcortical LFP signals from 8 patients, which I have analyzed with fieldtrip in order to obtain event-related time-frequency plots. Unfortunately, I have only three repetitions for each of my 4 conditions, so there's a lot of noise in the subject-averages of time-frequency data. I'm interested in testing baseline vs activation for each condition as well as comparisons between my 4 conditions (A1A2, A1B1, B1B2, A2B2). Could anyone tell me their take on smoothing before permuting? Is that a valid procedure with such a small sample size? I've searched for literature discussing smoothing before permuting (mostly the Holmes papers), where much talk of smoothing refers to concepts such as "locally pooled variance" and "pseudo t-statistics," but I don't know how this fits with fieldtrip's cluster statistics functions. Any thoughts would be greatly appreciated! U. Ramirez University of Wuerzburg From david.m.groppe at gmail.com Wed Nov 2 14:43:18 2016 From: david.m.groppe at gmail.com (David Groppe) Date: Wed, 2 Nov 2016 09:43:18 -0400 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> Message-ID: Since permutation tests exploit correlations between variables to increase sensitivity, smoothing each trial will increase your sensitivity. cheers, -David On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, wrote: > Dear Fieldtrippers, > > I have a set of subcortical LFP signals from 8 patients, which I have > analyzed with fieldtrip in order to obtain event-related time-frequency > plots. Unfortunately, I have only three repetitions for each of my 4 > conditions, so there's a lot of noise in the subject-averages of > time-frequency data. I'm interested in testing baseline vs activation for > each condition as well as comparisons between my 4 conditions (A1A2, A1B1, > B1B2, A2B2). > > Could anyone tell me their take on smoothing before permuting? Is that a > valid procedure with such a small sample size? I've searched for literature > discussing smoothing before permuting (mostly the Holmes papers), where > much talk of smoothing refers to concepts such as "locally pooled variance" > and "pseudo t-statistics," but I don't know how this fits with fieldtrip's > cluster statistics functions. > > Any thoughts would be greatly appreciated! > > U. Ramirez > University of Wuerzburg > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Wed Nov 2 17:29:50 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Wed, 02 Nov 2016 16:29:50 +0000 Subject: [FieldTrip] Time course for MNE In-Reply-To: References: Message-ID: Hello, I would like to compute the time course in the source , so far the source has been constructed but source.avg.filter has 1x24024 cells which each cell is 2x241, which 241 is the number of MEG sensors. Right now I cant figure out how the time course for each source can be computed. I appreciate if anybody can help. Best Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From Ramirez_U at ukw.de Wed Nov 2 18:02:00 2016 From: Ramirez_U at ukw.de (Ramirez Pasos, ) Date: Wed, 2 Nov 2016 17:02:00 +0000 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de>, Message-ID: <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> Thank you David for your response! Just to be clear: by "smoothing" I'm not referring to the smoothing performed when using the ft_frequencyanalysis multitaper method, but some kind of spatial smoothing on the time-frequency matrix obtained via ft_frequencyanalysis - so that according to your suggestion, I would smooth each trial, then average trials for each subject/condition, and finally use these for statistical evaluation? Is there a reason why smoothing each trial might be preferable to smoothing each subject's trial average? Thank you so much in advance, Uri ________________________________ Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]" im Auftrag von "David Groppe [david.m.groppe at gmail.com] Gesendet: Mittwoch, 2. November 2016 14:43 An: FieldTrip discussion list Betreff: Re: [FieldTrip] Smoothing before permutation test Since permutation tests exploit correlations between variables to increase sensitivity, smoothing each trial will increase your sensitivity. cheers, -David On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, > wrote: Dear Fieldtrippers, I have a set of subcortical LFP signals from 8 patients, which I have analyzed with fieldtrip in order to obtain event-related time-frequency plots. Unfortunately, I have only three repetitions for each of my 4 conditions, so there's a lot of noise in the subject-averages of time-frequency data. I'm interested in testing baseline vs activation for each condition as well as comparisons between my 4 conditions (A1A2, A1B1, B1B2, A2B2). Could anyone tell me their take on smoothing before permuting? Is that a valid procedure with such a small sample size? I've searched for literature discussing smoothing before permuting (mostly the Holmes papers), where much talk of smoothing refers to concepts such as "locally pooled variance" and "pseudo t-statistics," but I don't know how this fits with fieldtrip's cluster statistics functions. Any thoughts would be greatly appreciated! U. Ramirez University of Wuerzburg _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From david.m.groppe at gmail.com Wed Nov 2 18:33:01 2016 From: david.m.groppe at gmail.com (David Groppe) Date: Wed, 2 Nov 2016 13:33:01 -0400 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> Message-ID: Smoothing a time-frequency matrix is just as valid. I would apply the smoothing to whatever it is you are plugging into the permutation test as an independent observation (in your case it sounds like trial averages). -D On Wed, Nov 2, 2016 at 1:02 PM, Ramirez Pasos, wrote: > Thank you David for your response! Just to be clear: by "smoothing" I'm > not referring to the smoothing performed when using the > ft_frequencyanalysis multitaper method, but some kind of spatial smoothing > on the time-frequency matrix obtained via ft_frequencyanalysis - so that > according to your suggestion, I would smooth each trial, then average > trials for each subject/condition, and finally use these for statistical > evaluation? Is there a reason why smoothing each trial might be preferable > to smoothing each subject's trial average? > > Thank you so much in advance, > Uri > ________________________________ > Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]" > im Auftrag von "David Groppe [david.m.groppe at gmail.com] > Gesendet: Mittwoch, 2. November 2016 14:43 > An: FieldTrip discussion list > Betreff: Re: [FieldTrip] Smoothing before permutation test > > Since permutation tests exploit correlations between variables to increase > sensitivity, smoothing each trial will increase your sensitivity. > cheers, > -David > > On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, Ramirez_U at ukw.de>> wrote: > Dear Fieldtrippers, > > I have a set of subcortical LFP signals from 8 patients, which I have > analyzed with fieldtrip in order to obtain event-related time-frequency > plots. Unfortunately, I have only three repetitions for each of my 4 > conditions, so there's a lot of noise in the subject-averages of > time-frequency data. I'm interested in testing baseline vs activation for > each condition as well as comparisons between my 4 conditions (A1A2, A1B1, > B1B2, A2B2). > > Could anyone tell me their take on smoothing before permuting? Is that a > valid procedure with such a small sample size? I've searched for literature > discussing smoothing before permuting (mostly the Holmes papers), where > much talk of smoothing refers to concepts such as "locally pooled variance" > and "pseudo t-statistics," but I don't know how this fits with fieldtrip's > cluster statistics functions. > > Any thoughts would be greatly appreciated! > > U. Ramirez > University of Wuerzburg > > > _______________________________________________ > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Thu Nov 3 17:27:40 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Thu, 3 Nov 2016 16:27:40 +0000 Subject: [FieldTrip] Permutation test with low amount of subjects participating in several sessions Message-ID: Hello Fieldtrip experts, currently I'm working on an intracranial dataset with low amount of subjects who participated repeatedly in an experiment with two different conditions. In detail: * LFP data from only three subjects. * Each subject participated several times in the same experiment (about 16 sessions per subject). * In every session subjects performed around 50 trials of condition A and around 50 trials of condition B I calculated time-frequency spectra (TFS) for the LFP's. To test if there are significant differences between the TFS(condition A) and TFS(condition B) I want to use the permutation test implemented in fieldtrip. Unfortunately I'm struggling with my little statistic knowledge, because of the low amount of subjects and the high repetition rate for every subject in multiple sessions. Here is what I have done so far, and it would be great if anyone could tell me if it is correct or totally bullshit. First I averaged over all trials, so I put one TFS for each condition and session in the statistic. The first row of the design matrix represent the repetition of the single subjects (in this case three) and the second row of the design matrix contains the two conditions A (as 1) and B (as 2). cfg = []; cfg.parameter = 'powspctrm'; cfg.numrandomization = 5000; cfg.method = 'montecarlo'; cfg.correctm = 'fdr'; cfg.alpha = 0.05; cfg.correcttail = 'prob'; cfg.ivar = 2; cfg.uvar = 1; cfg.statistic = 'ft_statfun_depsamplesT'; design = [1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]; cfg.design = design; stat = ft_freqstatistics(cfg, data_A, data_B); Thanks in advance! Iris -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Thu Nov 3 20:11:32 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 03 Nov 2016 19:11:32 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: References: Message-ID: Hello all, By using the data of HCP, and conduct eLORETA to compute the inverse problem, right now Id like to visualise the results to surface but due to not having any anatomical information saved in the matrix it does not show any mapping. Moreover, Id like to parcellate the cortex based on the AAL atlas, so I really appreciate if anybody can help me. Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 3 20:53:25 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 3 Nov 2016 19:53:25 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: References: Message-ID: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> Hi Mehdy, It is not clear to me what you want to achieve. It’s unclear what you mean with ‘the results’ to be visualized on ‘the surface’, and that you have no ‘anatomical information’ saved in ‘the matrix’, and that there is a lack of ‘any mapping’. All terms between the quotation signs (for the readers among us who understand Dutch: my daughter aptly calls these things ‘bovenkomma’tjes’) are not defined precisely enough by you to give pointers about what or whether anything is missing, or whether something goes wrong. In general, source-level data can be parcellated with ft_sourceparcellate, but only if your atlas is in the same space as your functional data. That is, there should be a one-to-one mapping between the source locations in your functional data, and the source locations in your atlas. If you want to use the AAL atlas, which is essentially defined as a volumetric image (probably at a voxel resolution of 1 or 2 mm), you need to interpolate/downsample this atlas onto your sourcemodel at the appropriate resolution .This would make most sense if your sourcemodel is also defined as a 3D grid, but it is not absolutely necessary. In order to interpolate the atlas onto your sourcemodel, you could use ft_sourceinterpolate (provided both atlas and sourcemodel are defined in the same coordinate system). Note that from your messages on this forum and on the HCP discussion list it is not clear to the reader what source model you used for the eLORETA. There is some information on the fieldtrip wiki that illustrates how to parcellate source reconstructed data http://www.fieldtriptoolbox.org/tutorial/networkanalysis In the example, it uses a surface-based parcellation, and parcellates a connectivity matrix. The function can also parcellate univariate data (e.g. time courses or power spectra), either or not defined on a 3D grid. Also, the HCP software+documentation that the MEG team released, and which accompanies the released data, might give you some pointers on how to do it. You could try and adapt the code provided to your own needs. Good luck, Jan-Mathijs J.M.Schoffelen Senior Researcher Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > On 03 Nov 2016, at 20:11, mehdy dousty wrote: > > Hello all, > By using the data of HCP, and conduct eLORETA to compute the inverse problem, right now Id like to visualise the results to surface but due to not having any anatomical information saved in the matrix it does not show any mapping. Moreover, Id like to parcellate the cortex based on the AAL atlas, so I really appreciate if anybody can help me. > Thanks > Mehdy > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From mehdy.dousty at gmail.com Thu Nov 3 21:14:51 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 03 Nov 2016 20:14:51 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> References: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> Message-ID: Hello, Thanks for the answer and sorry for my vague explanation. here is my code to compute the inverse problem by eLORETA using the provided MRI, DATA and Source model in HCP. % source localization for resting state HCP. load('100307_MEG_3-Restin_rmegpreproc.mat') ; % loading the data; load('100307_MEG_anatomy_headmodel.mat');% loading the headmodel tmp = load('100307_MEG_anatomy_sourcemodel_3d6mm.mat');% sourcemodel by 6mm individual_sourcemodel3d = tmp.sourcemodel3d; %% MRI individual_mri = ft_read_mri('T1w_acpc_dc_restore.nii.gz'); hcp_read_ascii('100307_MEG_anatomy_transform.txt'); individual_mri.transform = transform.vox07mm2bti; individual_mri.coordsys = 'bti'; %% converting to the same coordination unit individual_mri = ft_convert_units(individual_mri,'mm'); individual_sourcemodel3d = ft_convert_units(individual_sourcemodel3d,'mm'); headmodel = ft_convert_units(headmodel,'mm'); data.grad = ft_convert_units(data.grad,'mm'); %% leadfield matrix cfg = []; cfg.grid = individual_sourcemodel3d; cfg.headmodel = headmodel; cfg.grad = data.grad; cfg.channel = ft_channelselection('MEG',data.label); cfg.reducerank = 'no'; leadfield = ft_prepare_leadfield(cfg); %% timelcok cfg = []; cfg.covariance = 'yes'; cfg.covariancewindow = [0 1000]; cfg.keeptrials = 'yes'; timelockanalaysis = ft_timelockanalysis(cfg,data); %% Eloreta cfg = []; cfg.method = 'eloreta'; cfg.vol = headmodel; cfg.grid = leadfield; cfg.eloreta.lambda = 0.05; %Regularization parameters,cross-validation can be %used but as it resting state and we really dont know what the outupt looks %like then we have to use emprical numbers which cfg.mne.projectnoise = 'yes'; cfg.keepmom = 'yes'; %keep dipole moment cfg.mne.keepmom = 'yes'; cfg.senstype = 'meg'; cfg.keepfilter = 'yes'; cfg.eloreta.reducerank = 'no'; source_eloreta = ft_sourceanalysis (cfg, timelockanalaysis); so far , I compute the eLORETA, now I would like to project the data into 3d surface, like the way it was done for beamformer in http://www.fieldtriptoolbox.org/tutorial/plotting' and then make a parcellation based on some predefined atlases and then do the other analysis. I do appreciate for your helping. Thanks On Thu, Nov 3, 2016 at 1:53 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Mehdy, > > It is not clear to me what you want to achieve. It’s unclear what you mean > with ‘the results’ to be visualized on ‘the surface’, and that you have no > ‘anatomical information’ saved in ‘the matrix’, and that there is a lack of > ‘any mapping’. All terms between the quotation signs (for the readers among > us who understand Dutch: my daughter aptly calls these things > ‘bovenkomma’tjes’) are not defined precisely enough by you to give pointers > about what or whether anything is missing, or whether something goes wrong. > > In general, source-level data can be parcellated with ft_sourceparcellate, > but only if your atlas is in the same space as your functional data. That > is, there should be a one-to-one mapping between the source locations in > your functional data, and the source locations in your atlas. If you want > to use the AAL atlas, which is essentially defined as a volumetric image > (probably at a voxel resolution of 1 or 2 mm), you need to > interpolate/downsample this atlas onto your sourcemodel at the appropriate > resolution .This would make most sense if your sourcemodel is also defined > as a 3D grid, but it is not absolutely necessary. In order to interpolate > the atlas onto your sourcemodel, you could use ft_sourceinterpolate > (provided both atlas and sourcemodel are defined in the same coordinate > system). Note that from your messages on this forum and on the HCP > discussion list it is not clear to the reader what source model you used > for the eLORETA. > > There is some information on the fieldtrip wiki that illustrates how to > parcellate source reconstructed data > http://www.fieldtriptoolbox.org/tutorial/networkanalysis In the example, > it uses a surface-based parcellation, and parcellates a connectivity > matrix. The function can also parcellate univariate data (e.g. time courses > or power spectra), either or not defined on a 3D grid. > > Also, the HCP software+documentation that the MEG team released, and which > accompanies the released data, might give you some pointers on how to do > it. You could try and adapt the code provided to your own needs. > > Good luck, > > Jan-Mathijs > > > J.M.Schoffelen > Senior Researcher > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > > > > On 03 Nov 2016, at 20:11, mehdy dousty wrote: > > > > Hello all, > > By using the data of HCP, and conduct eLORETA to compute the inverse > problem, right now Id like to visualise the results to surface but due to > not having any anatomical information saved in the matrix it does not show > any mapping. Moreover, Id like to parcellate the cortex based on the AAL > atlas, so I really appreciate if anybody can help me. > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From marco.buiatti at gmail.com Fri Nov 4 11:08:10 2016 From: marco.buiatti at gmail.com (Marco Buiatti) Date: Fri, 4 Nov 2016 11:08:10 +0100 Subject: [FieldTrip] importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco On 16 August 2016 at 07:20, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Jeff, > Probably this is not yet implemented. Since it is all matlab-based it > should be pretty straightforward, once you know how the headmodel is > represented in the Brainstorm .mat file. > Once you have figured this out, it probably takes just a few lines of code > in ft_read_headshape. > > If you have an updated version of this function, you can easily contribute > it to the code-base for everyone’s use through git. > How this can be done, is shown here: http://www.fieldtriptoolbox.org/ > development/git > > Best, > Jan-Mathijs > > > > On 16 Aug 2016, at 02:05, K Jeffrey Eriksen wrote: > > Hi, > > I am trying to do some forward and inverse simulations using a 3-shell > BEM, and would prefer to import my model from Brainstorm where I have > already created it. I can find one example of importing the cortical mesh > from Brainstorm using ft_read_headshape with the ‘matlab’ format, but I > cannot find anything on how to import the Brainstorm BEM head model or > leadfield. > > Please point me to any further information on this topic, > -Jeff > > > _______________________________________________ > 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 > -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From cmuehl at gmail.com Fri Nov 4 11:37:08 2016 From: cmuehl at gmail.com (Christian Muehl) Date: Fri, 4 Nov 2016 10:37:08 +0000 Subject: [FieldTrip] Frontiers Research Topic: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures" Message-ID: <5568b746f06e44e198a78067481fe910@EXPRD02.hosting.ru.nl> *** Frontiers research topic - Call for Contributions *** We would like to invite contributions to the following research topic in Frontiers of Human Neuroscience: "Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures" Our objective is to publish a focused collection of open-access articles that represent the state of the art in detection and estimation of working memory and other cognitive functions based on neurophysiological signal classification and aimed at the application of such classified states in human-computer interaction. We specifically invite contributions that deal with the detection of cognitive states in complex scenarios as they are found in real world applications. Please refer to http://tinyurl.com/detectWM for more details and submission guidelines. * Please let us know if you are interested to contribute by replying to felix.putze at uni-bremen.de * Relevant Dates 31 January 2017 - Abstract 30 April 2017 - Manuscript * Topic Editors Felix Putze, University of Bremen, Germany Fabien Lotte, Inria Bordeaux Sud-Ouest, France Stephen Fairclough, Liverpool John Moores University, United Kingdom Christian Mühl, German Aerospace Center, Cologne, Germany * Topics of Interest Executive cognitive functions like working memory determine the success or failure of a wide variety of different cognitive tasks. Estimation of constructs like working memory load or memory capacity from neurophysiological or psychophysiological signals would enable adaptive systems to respond to cognitive states experienced by an operator and trigger responses designed to support task performance (e.g. by simplifying the exercises of a tutor system, or by shutting down distractions from the mobile phone). The determination of cognitive states like working memory load is also useful for automated testing/assessment, for usability evaluation and for tutoring applications. While there exists a huge body of research work on neural and physiological correlates of cognitive functions like working memory activity, fewer publications deal with the application of this research with respect to single-trial detection and real-time estimation of cognitive functions in complex, realistic scenarios. Single-trial classifiers based on brain activity measurements such as EEG, fNIRS or physiological signals such as EDA, ECG, BVP or Eyetracking have the potential to classify affective or cognitive states based upon short segments of data. For this purpose, signal processing and machine learning techniques need to be developed and transferred to real-world user interfaces. In this research topic, we are looking for: (1) studies in complex, realistic scenarios that specifically deal with cognitive states or cognitive processes (memory-related or other), (2) classification and estimation of cognitive states and processes like working memory activity, and (3) applications to Brain-Computer Interfaces and Human-Computer Interaction in general. Central open research questions which we would like to see approached in this research topic comprise: * How can working memory load be quantified with regression or classification models which are robust to perturbations common to realistic recording conditions and natural brain signal fluctuations? * How can detection and classification of cognitive states be used in Brain-Computer Interfaces (BCIs)? * How can multiple types of features or signal types be combined to achieve a more robust classification of working memory load? * How can working memory activity be differentiated from other types of cognitive or affective activity which co-vary with, but are not related to memory? * How well can insights from offline, average-analysis studies on memory activity be transferred to online, single-trial BCIs? * How can models of working memory load be calibrated to account for individual differences, for example in working memory capacity? * How can approaches from computational cognitive modeling be combined with physiological signals to assess memory processes? * How can working memory load be classified, for example according to modality (spatial memory, semantic memory, ...) or type of activity (encoding, retrieval, rehearsal, ...)? * How to design user-independent memory load estimators? Is that even feasible? * How can memory load estimators from a given context or modality be transferred to another modality and/or context? * How can working memory activity be classified to predict the outcome of the activity, for example by differentiating successful from failed encoding attempts? Additionally, we are also interested in other relevant submissions related to the research topic. We also sincerely invite manuscripts dealing with applications of memory-related interfaces (e.g. adaptive human-computer interfaces for memory-intensive tasks). Comprehensive review articles which critically reflect the state-of-the-art on a certain aspect of the topic are also welcome. With best regards, Felix Putze, Fabien Lotte, Stephen Fairclough, Christian Mühl. -------------- next part -------------- An HTML attachment was scrubbed... URL: From eriksenj at ohsu.edu Fri Nov 4 23:12:13 2016 From: eriksenj at ohsu.edu (K Jeffrey Eriksen) Date: Fri, 4 Nov 2016 22:12:13 +0000 Subject: [FieldTrip] importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Hi Marco, Sorry to say I was not successful, yet. I have shifted to other tasks at this point. I am using 256 channel EEG, and want to use the FreeSurfer cortical ribbon as my source model (I would prefer source/headmodels to be FEM as well). I found that a lot of FieldTrip is based on regular dipole grids, and also it is more oriented to MEG than EEG. I do not have the time right now to try to put all the pieces together in FieldTrip to do what I want, but might get back to it in a month or two. Sorry I could not be of more help to you right now. -Jeff From: Marco Buiatti [mailto:marco.buiatti at gmail.com] Sent: Friday, November 04, 2016 3:08 AM To: FieldTrip discussion list; K Jeffrey Eriksen Subject: Re: [FieldTrip] importing Brainstorm head and source models and leftfields Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco On 16 August 2016 at 07:20, Schoffelen, J.M. (Jan Mathijs) > wrote: Hi Jeff, Probably this is not yet implemented. Since it is all matlab-based it should be pretty straightforward, once you know how the headmodel is represented in the Brainstorm .mat file. Once you have figured this out, it probably takes just a few lines of code in ft_read_headshape. If you have an updated version of this function, you can easily contribute it to the code-base for everyone’s use through git. How this can be done, is shown here: http://www.fieldtriptoolbox.org/development/git Best, Jan-Mathijs On 16 Aug 2016, at 02:05, K Jeffrey Eriksen > wrote: Hi, I am trying to do some forward and inverse simulations using a 3-shell BEM, and would prefer to import my model from Brainstorm where I have already created it. I can find one example of importing the cortical mesh from Brainstorm using ft_read_headshape with the ‘matlab’ format, but I cannot find anything on how to import the Brainstorm BEM head model or leadfield. Please point me to any further information on this topic, -Jeff _______________________________________________ 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 -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.tadel at mcgill.ca Sat Nov 5 15:33:07 2016 From: francois.tadel at mcgill.ca (Francois Jean Tadel, Mr) Date: Sat, 5 Nov 2016 14:33:07 +0000 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Jeff, Marco: Good timing, I've been working on similar topics this week. I added processes in Brainstorm to use the forward models in FieldTrip, using ft_volumesegment, ft_prepare_headmodel and ft_prepare_leadfield. For now it is not possible to use the Brainstorm BEM surfaces to compute the leadfield with FieldTrip, but if you think this is useful, we could probably add this. Before the end of the month, I hope to have all the inverse models available as well. We will have the possibility to call them either with Brainstorm or FieldTrip forward solutions. I you want to help me with the debugging, or if you want to start working on the inverse/DICS part next week, it's all on github: https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_volumesegment.m https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_prepare_leadfield.m You could create a new process to call the FieldTrip function you want, there are already many other examples of wrappers available: process_ft_channelrepair.m process_ft_dipolefitting.m process_ft_scalpcurrentdensity.m process_ft_timelockstatistics.m process_ft_sourcestatistics.m process_ft_freqstatistics.m If you need help with the plugin API in Brainstorm: http://neuroimage.usc.edu/brainstorm/Tutorials/TutUserProcess Functions to convert Brainstorm files into FieldTrip structures: brainstorm3/toolbox/io/out_fieldtrip_*.m Cheers, Francois ________________________________ Hi Marco, Sorry to say I was not successful, yet. I have shifted to other tasks at this point. I am using 256 channel EEG, and want to use the FreeSurfer cortical ribbon as my source model (I would prefer source/headmodels to be FEM as well). I found that a lot of FieldTrip is based on regular dipole grids, and also it is more oriented to MEG than EEG. I do not have the time right now to try to put all the pieces together in FieldTrip to do what I want, but might get back to it in a month or two. Sorry I could not be of more help to you right now. -Jeff From: Marco Buiatti [mailto:marco.buiatti at gmail.com] Sent: Friday, November 04, 2016 3:08 AM To: FieldTrip discussion list; K Jeffrey Eriksen Subject: Re: [FieldTrip] importing Brainstorm head and source models and leftfields Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Sun Nov 6 00:51:09 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Sat, 05 Nov 2016 23:51:09 +0000 Subject: [FieldTrip] BTI to MNI coordination Message-ID: Hello all, The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Sun Nov 6 16:22:17 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Sun, 6 Nov 2016 15:22:17 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: Message-ID: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From mehdy.dousty at gmail.com Sun Nov 6 19:39:15 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Sun, 06 Nov 2016 18:39:15 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Message-ID: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < stan.vanpelt at donders.ru.nl> wrote: > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Sun Nov 6 21:02:17 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Sun, 6 Nov 2016 20:02:17 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Message-ID: <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> ft_convert_coorsys is not accurate. Don’t use it if you want an accurate conversion. Best, Jan-Mathijs On 06 Nov 2016, at 19:39, mehdy dousty > wrote: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) > wrote: Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From daniel.haehnke at tum.de Mon Nov 7 14:49:35 2016 From: daniel.haehnke at tum.de (=?utf-8?B?SMOkaG5rZSwgRGFuaWVs?=) Date: Mon, 7 Nov 2016 13:49:35 +0000 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes Message-ID: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> Dear FT community, I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. Method 1: 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. 3. For each trial shuffle (see 1.) average across subsamples (see 2.). 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. Method 2: 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. Despite the differences in the steps, there are also the following differences in the two methods. In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). Best wishes, Daniel -- Daniel Hähnke PhD student Technische Universität München Institute of Neuroscience Translational NeuroCognition Laboratory Biedersteiner Straße 29, Bau 601 80802 Munich Germany Email: daniel.haehnke at tum.de Phone: +49 89 4140 3356 From mehdy.dousty at gmail.com Mon Nov 7 17:48:25 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Mon, 07 Nov 2016 16:48:25 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> Message-ID: Thanks for the answer. I am going to volumelookup the predefined sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to the ROI_MNI_V4, as the atlas has the MNI coordination systema and the sourcemodel has the bti coordinations system and there is no transform function in the sourcmodel to convert bti to mni, what is your suggestion to do so? Thanks On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > ft_convert_coorsys is not accurate. Don’t use it if you want an accurate > conversion. > Best, > Jan-Mathijs > > On 06 Nov 2016, at 19:39, mehdy dousty wrote: > > Thanks for the answer. how accurate to use ft_convert_coordsys (source, > 'spm'), and if it is not, as there is no explanation on wiki unfortunately, > how can I make this conversion . > Thanks > Mehdy > > On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < > stan.vanpelt at donders.ru.nl> wrote: > > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Mon Nov 7 20:52:33 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 7 Nov 2016 19:52:33 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> Message-ID: <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> Mehdy, As I suggested in an earlier e-mail it’s most convenient to use ft_sourceinterpolate to interpolate the atlas onto the sourcemodel. To this end you can use the template sourcemodel that comes shipped with the HCP megconnectome software. Once you have managed to do this, you can directly look-up the anatomical labels of the dipole positions, because the dipole positions in the individual sourcemodels (in bti space) by construction coincide with the template after transformation into mni space. This is explained on page 30 of the HCP MEG manual. I suggest you to consult this in somewhat more detail. Best wishes, Jan-Mathijs On 07 Nov 2016, at 17:48, mehdy dousty > wrote: Thanks for the answer. I am going to volumelookup the predefined sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to the ROI_MNI_V4, as the atlas has the MNI coordination systema and the sourcemodel has the bti coordinations system and there is no transform function in the sourcmodel to convert bti to mni, what is your suggestion to do so? Thanks On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) > wrote: ft_convert_coorsys is not accurate. Don’t use it if you want an accurate conversion. Best, Jan-Mathijs On 06 Nov 2016, at 19:39, mehdy dousty > wrote: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) > wrote: Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Mon Nov 7 22:17:19 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Mon, 07 Nov 2016 21:17:19 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> Message-ID: Thank you very much. As you suggest after computing the inverse problem I interpolate the atlas to the sourcemodel by the below commands: atlas = ft_read_atlas('/home/mehdy/Desktop/EEG-1/fieldtrip-20160417/template/atlas/aal/ROI_MNI_V4.nii'); cfg = []; cfg.interpmethod = 'nearest'; cfg.parameter = 'tissue'; individual_sourcemodel3d1 = ft_sourceinterpolate(cfg,atlas,individual_sourcemodel3d); and then interpolate the result of source construction to the computed source model , I mean individual_sourcemodel3d1: cfg = []; cfg.parameter = 'pow'; source_eloreta_disc_int1 = ft_sourceinterpolate(cfg,source_eloreta,individual_sourcemodel3d1); now the are the data parcellated? if it is so, how can I get the parcellation labels? Thanks On Mon, Nov 7, 2016 at 12:59 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Mehdy, > > As I suggested in an earlier e-mail it’s most convenient to use > ft_sourceinterpolate to interpolate the atlas onto the sourcemodel. To this > end you can use the template sourcemodel that comes shipped with the HCP > megconnectome software. Once you have managed to do this, you can directly > look-up the anatomical labels of the dipole positions, because the dipole > positions in the individual sourcemodels (in bti space) by construction > coincide with the template after transformation into mni space. This is > explained on page 30 of the HCP MEG manual. I suggest you to consult this > in somewhat more detail. > > Best wishes, > Jan-Mathijs > > > > On 07 Nov 2016, at 17:48, mehdy dousty wrote: > > Thanks for the answer. I am going to volumelookup the predefined > sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to > the ROI_MNI_V4, as the atlas has the MNI coordination systema and the > sourcemodel has the bti coordinations system and there is no transform > function in the sourcmodel to convert bti to mni, what is your suggestion > to do so? > Thanks > > On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) < > jan.schoffelen at donders.ru.nl> wrote: > > ft_convert_coorsys is not accurate. Don’t use it if you want an accurate > conversion. > Best, > Jan-Mathijs > > On 06 Nov 2016, at 19:39, mehdy dousty wrote: > > Thanks for the answer. how accurate to use ft_convert_coordsys (source, > 'spm'), and if it is not, as there is no explanation on wiki unfortunately, > how can I make this conversion . > Thanks > Mehdy > > On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < > stan.vanpelt at donders.ru.nl> wrote: > > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From florian at brain.riken.jp Wed Nov 9 01:51:55 2016 From: florian at brain.riken.jp (Florian Gerard-Mercier) Date: Wed, 9 Nov 2016 09:51:55 +0900 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes In-Reply-To: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> Message-ID: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Dear Daniel, Did you get an answer already? I’m just wondering, what is the null hypothesis? Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions? Best, Florian Gerard-Mercier Lab. for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: 048 462 1111 and 7106 Mob: 080 3213 6851 > On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel wrote: > > Dear FT community, > > I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. > > I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. > > Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. > > Method 1: > 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. > 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. > 3. For each trial shuffle (see 1.) average across subsamples (see 2.). > 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. > > Method 2: > 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. > 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. > 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). > > Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. > > Despite the differences in the steps, there are also the following differences in the two methods. > In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. > Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. > > Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. > I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. > > It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). > > Best wishes, > > Daniel > -- > Daniel Hähnke > PhD student > > Technische Universität München > Institute of Neuroscience > Translational NeuroCognition Laboratory > Biedersteiner Straße 29, Bau 601 > 80802 Munich > Germany > > Email: daniel.haehnke at tum.de > Phone: +49 89 4140 3356 > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From daniel.haehnke at tum.de Wed Nov 9 14:30:04 2016 From: daniel.haehnke at tum.de (=?utf-8?B?SMOkaG5rZSwgRGFuaWVs?=) Date: Wed, 9 Nov 2016 13:30:04 +0000 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes In-Reply-To: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Message-ID: <4E9EA3F3-0456-4367-A17B-8ED076223E70@tum.de> Hi Florian, thanks for your reply! I haven’t had any replies yet. You’re right, it’s important to form a null hypothesis before doing statistical tests. Explicitly, my null hypothesis is that the PLV is the same for all conditions. For that I’d need to shuffle the condition labels of the spike phases across conditions. The z-scoring was meant as a normalisation of the spike-lfp-combinations, so I can pool combinations via averaging. Of course, the trial association shuffling implicitly also tests the null hypothesis of zero PLV. Best, Daniel On 9 Nov 2016, at 01:51, Florian Gerard-Mercier > wrote: Dear Daniel, Did you get an answer already? I’m just wondering, what is the null hypothesis? Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions? Best, Florian Gerard-Mercier Lab. for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: 048 462 1111 and 7106 Mob: 080 3213 6851 On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel > wrote: Dear FT community, I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. Method 1: 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. 3. For each trial shuffle (see 1.) average across subsamples (see 2.). 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. Method 2: 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. Despite the differences in the steps, there are also the following differences in the two methods. In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). Best wishes, Daniel -- Daniel Hähnke PhD student Technische Universität München Institute of Neuroscience Translational NeuroCognition Laboratory Biedersteiner Straße 29, Bau 601 80802 Munich Germany Email: daniel.haehnke at tum.de Phone: +49 89 4140 3356 _______________________________________________ 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From Darren.Price at mrc-cbu.cam.ac.uk Wed Nov 9 17:11:08 2016 From: Darren.Price at mrc-cbu.cam.ac.uk (Darren Price) Date: Wed, 9 Nov 2016 16:11:08 +0000 Subject: [FieldTrip] Open-Access Dataset: Cambridge Centre for Ageing and Neuroscience (CamCAN) Message-ID: Dear Fieldtrippers The Cambridge Centre for Ageing and Neuroscience (Cam-CAN; www.cam-can.org) is pleased to announce the release of raw data from the first wave of Phase II of the Cam-CAN cohort. These data include MRI, MEG and cognitive data from approximately 650 males and females uniformly distributed from 18 to 88 years of age. The sample is unique in its population-representativeness (e.g, relative to national census data) and the depth and breadth of neuroimaging and cognitive assessment. The MRI data (in NIFTI and BIDS format) include T1-weighted, T2-weighted and Diffusion-weighted 3T MRI images, plus 3 runs of BOLD-weighted images during 1) rest, 2) movie-watching and 3) an event-related sensorimotor task (with combined visual and auditory stimuli cueing a motor response); the MEG data (in FIF format) include 3 runs of 1) rest, 2) the same sensorimotor task as the fMRI and 3) a passive sensory task (with separate visual and auditory stimuli); the behavioural data include scores on tasks assessing a range of cognitive domains, such as fluid intelligence, memory, language, among others. For more information about the CamCAN project and data, see: Shafto et al. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology, 14(204). doi:10.1186/s12883-014-0204-1. Taylor et al. (2015). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage. 10.1016/j.neuroimage.2015.09.018. We hope to release more data (e.g, MT-weighted and preprocessed MRI/MEG data) in future. The data are provided freely after agreeing to minimal conditions, via this page: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ Darren ------------------------------------------------------- Dr. Darren Price Investigator Scientist and Cam-CAN Data Manager MRC Cognition & Brain Sciences Unit 15 Chaucer Road Cambridge, CB2 7EF England EMAIL: darren.price at mrc-cbu.cam.ac.uk URL: http://www.mrc-cbu.cam.ac.uk/people/darren.price TEL +44 (0)1223 355 294 x202 FAX +44 (0)1223 359 062 MOB +44 (0)7717822431 ------------------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From ph442 at cam.ac.uk Wed Nov 9 18:45:27 2016 From: ph442 at cam.ac.uk (parham hashemzadeh) Date: Wed, 09 Nov 2016 17:45:27 +0000 Subject: [FieldTrip] Literature or methods in Fieldtrip on estimating missing data. In-Reply-To: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Message-ID: Dear all I was wondering, if you know of any resources or techniques in dealing with missing data. Does fieldtrip have any routines . I am looking for better methods that extrapolate data points to where there are no sensors. Are there any studies or papers on extrapolation or estimating data at points where there are no sensors. Maybe some matlab routines. I would appreciate any help you are willing to provide on known literature. best regards parham From marco.buiatti at gmail.com Thu Nov 10 11:56:03 2016 From: marco.buiatti at gmail.com (Marco Buiatti) Date: Thu, 10 Nov 2016 11:56:03 +0100 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Dear Francois, this is really good news, thanks! I have tested the Process > Sources > Fieldtrip: ft_prepare_leadfield on different MEG data (always recorded from an Elekta 306 sensors system), and I get an error "cannot work on balanced gradiometer definition" (see the warnings below). I have tried to debug this but I am a bit lost since I am not very familiar with manipulating source data. I also have a basic question on the data to put in the process: my understanding is that I should feed the process with the segmented anatomy, that I identify with (for the default anatomy) the "Cortex_15002V" in the anatomy tab. However, I cannot drag it to the Process window (I understand that the sensor configuration is also needed). I therefore drag in the Process window any data associated with the subject. Is this correct? Does the program then automatically process the segmented anatomy? Can you please shed light on this, or advice me on how to debug it? Thanks a lot, Marco BST> FieldTrip install: C:\Users\marco.buiatti\Documents\software\fieldtrip-20161107 the input is volume data with dimensions [181 217 181] Converting the coordinate system from ctf to spm Rescaling NIFTI: slope = 0.00342945, intercept = 0 Smoothing by 0 & 8mm.. Coarse Affine Registration.. Fine Affine Registration.. performing the segmentation on the specified volume creating brainmask smoothing brainmask with a 5-voxel FWHM kernel thresholding brainmask at a relative threshold of 0.500 the call to "ft_volumesegment" took 52 seconds Warning: assuming that planar MEG channel units are T/m > In ft_chanunit at 173 In ft_datatype_sens at 392 In ft_datatype_sens at 158 In ft_checkconfig at 232 In utilities\private\ft_preamble_trackconfig at 37 In ft_preamble at 56 In ft_prepare_headmodel at 148 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 Warning: please specify cfg.method='projectmesh', 'iso2mesh' or 'isosurface' > In ft_prepare_mesh at 137 In ft_prepare_headmodel at 348 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 Warning: using 'projectmesh' as default > In ft_prepare_mesh at 138 In ft_prepare_headmodel at 348 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 triangulating the outer boundary of compartment 1 (brain) with 3000 vertices the call to "ft_prepare_mesh" took 1 seconds the call to "ft_prepare_headmodel" took 1 seconds *************************************************************************** ** Error: [process_ft_prepare_leadfield] Sources > FieldTrip: ft_prepare_leadfield ** Line 228: ft_plot_sens (line 228) ** cannot work with balanced gradiometer definition ** ** Call stack: ** >ft_plot_sens.m at 228 ** >process_ft_prepare_leadfield.m>Run at 255 ** >process_ft_prepare_leadfield.m at 24 ** >bst_process.m>Run at 229 ** >bst_process.m at 36 ** >panel_process1.m>RunProcess at 141 ** >panel_process1.m at 27 ** >gui_brainstorm.m>CreateWindow/ProcessRun_Callback at 707 ** >bst_call.m at 28 ** >gui_brainstorm.m>@(h,ev)bst_call(@ProcessRun_Callback) at 261 ** ** ** File: 150505/@raw19900812VTTN_01run4/data_0raw_19900812VTTN_01run4.mat ** *************************************************************************** [image: Inline images 2] On 5 November 2016 at 15:33, Francois Jean Tadel, Mr < francois.tadel at mcgill.ca> wrote: > Jeff, Marco: > > > Good timing, I've been working on similar topics this week. I added > processes in Brainstorm to use the forward models in FieldTrip, using > ft_volumesegment, ft_prepare_headmodel and ft_prepare_leadfield. For now it > is not possible to use the Brainstorm BEM surfaces to compute the leadfield > with FieldTrip, but if you think this is useful, we could probably add this. > > > Before the end of the month, I hope to have all the inverse models > available as well. We will have the possibility to call them either with > Brainstorm or FieldTrip forward solutions. > > > I you want to help me with the debugging, or if you want to start working > on the inverse/DICS part next week, it's all on github: > https://github.com/brainstorm-tools/brainstorm3/blob/master/ > toolbox/process/functions/process_ft_volumesegment.m > https://github.com/brainstorm-tools/brainstorm3/blob/master/ > toolbox/process/functions/process_ft_prepare_leadfield.m > > You could create a new process to call the FieldTrip function you want, > there are already many other examples of wrappers available: > process_ft_channelrepair.m > process_ft_dipolefitting.m > process_ft_scalpcurrentdensity.m > process_ft_timelockstatistics.m > process_ft_sourcestatistics.m > process_ft_freqstatistics.m > > If you need help with the plugin API in Brainstorm: > http://neuroimage.usc.edu/brainstorm/Tutorials/TutUserProcess > > Functions to convert Brainstorm files into FieldTrip structures: > brainstorm3/toolbox/io/out_fieldtrip_*.m > > Cheers, > Francois > > > ------------------------------ > > Hi Marco, > > Sorry to say I was not successful, yet. I have shifted to other tasks at > this point. I am using 256 channel EEG, and want to use the FreeSurfer > cortical ribbon as my source model (I would prefer source/headmodels to be > FEM as well). I found that a lot of FieldTrip is based on regular dipole > grids, and also it is more oriented to MEG than EEG. I do not have the > time right now to try to put all the pieces together in FieldTrip to do > what I want, but might get back to it in a month or two. > > Sorry I could not be of more help to you right now. > > -Jeff > > > > From: Marco Buiatti [mailto:marco.buiatti at gmail.com > ] > Sent: Friday, November 04, 2016 3:08 AM > To: FieldTrip discussion list; K Jeffrey Eriksen > Subject: Re: [FieldTrip] importing Brainstorm head and source models and > leftfields > > Dear all, > > I have Jeff's same question. I have anatomies ready in Brainstorm: > segmented data imported, co-registration with MEG sensor space, computation > of head model with overlapping spheres. Now I would like to import all this > into Fieldtrip for source reconstruction of oscillatory sources (DICS). > What's the best way to do this? > > And is there any difference between Brainstorm and Fieldtrip in these > steps that I should be aware of? > > Jeff, did you manage to solve the problem? > > Thanks a lot, > > Marco > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 53275 bytes Desc: not available URL: From mehdy.dousty at gmail.com Thu Nov 10 19:03:31 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 10 Nov 2016 18:03:31 +0000 Subject: [FieldTrip] Extracting time course of specific region in source level Message-ID: Hi all, I am working on source reconstruction of HCP MEG data by using eLORETA. The inverse problem is computed and the data are projected to the MNI by sourceinterpolate, so I would like to extract all the time courses which deal with specific ROI. I searched the mailing list and mostly they just provide information on the beamformer, so Id be grateful if anybody could possibly give me some hints about it. Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From amitjaiswal.elect at gmail.com Fri Nov 11 09:51:49 2016 From: amitjaiswal.elect at gmail.com (amit kumar Jaiswal) Date: Fri, 11 Nov 2016 10:51:49 +0200 Subject: [FieldTrip] checkinput_mex Error Message-ID: Hi everyone, I am using dipole fitting with .fif MEG data and while using ICP my Centos workstation is coming with an error: Undefined function '*checkinput_mex*' for input arguments of type 'cell' . I tried to get this function but couldn't. I checked that the same script is running fine on windows machine. What is the solution?? -- *Thanks & Regards......* *Amit Kumar Jaiswal* *Researcher @ChildBrain Project* *Elekta Oy, Helsinki (Finland)* *Call/Whatsapp: +358-405222805* *Important facts to be careful:* *** 800 million people (1 of every 9) in the world don't get clean & safe water. ** Save water daily and plant trees on every occasion.** **Save earth, Save lives.* -------------- next part -------------- An HTML attachment was scrubbed... URL: From deefje.meijer at gmail.com Mon Nov 14 12:35:05 2016 From: deefje.meijer at gmail.com (David Meijer) Date: Mon, 14 Nov 2016 11:35:05 +0000 Subject: [FieldTrip] PhD position, Neurocomputational Linguistics, Uni Bham / Google Research London Message-ID: On behalf of Prof Uta Noppeney. *PhD position in Neurocomputational Linguistics* *University of Birmingham in collaboration with Google Research London* Language comprehension is critical for effective interactions in our social world. In order to understand ‘who does what to whom’ in natural language processing, the brain needs to assign a syntactic structure to every sentence – a process coined ‘syntactic parsing’. This interdisciplinary project will combine expertise from human neuroscience (University of Birmingham) and computational linguistics (Google Research London) to determine the neural mechanisms underlying sentence comprehension in the human brain and advance parsing algorithms in machines. To study natural language processing and the underlying neural mechanisms in humans, we will measure eye movements, behavioural (psychophysics) and electrophysiological responses (EEG/fMRI) in participants reading natural sentences from syntactically annotated corpora. We will employ advanced machine learning algorithms to characterize the computational operations and neural mechanisms underlying syntactic processing in the human brain. Conversely, the insights obtained from human neuroimaging (EEG/fMRI) and eye tracking will provide critical constraints on the parameters and algorithms used in machine. The PhD position is designed to involve a 3 month internship at Google Research London. The Computational Cognitive Neuroimaging Group (Uta Noppeney) in collaboration with Google Research London (Bernd Bohnet, Ryan McDonald) is seeking an enthusiastic PhD candidate with strong analytical and quantitative abilities. Applicants should have a background in computational linguistics, neuroscience, computer science, psychology, physics or related areas. Prior experience in statistical analysis and/or machine learning would be an advantage. The Computational Cognitive Neuroimaging Lab is based at the Department of Psychology and the Computational Neuroscience and Cognitive Robotics Centre of the University of Birmingham, UK. The centre provides an excellent multidisciplinary, interactive and collaborative research environment combining expertise in cognitive neuroimaging, psychophysics and computational neuroscience. The psychology department was rated 5th in the UK research assessment exercise. http://www.birmingham.ac.uk/schools/psychology/research/labs/comp-cog-neuro/index.aspx http://www.birmingham.ac.uk/research/activity/cncr/index.aspx Applications will be considered until 8th January 2017. The starting date is Sept/Oct 2017. iCASE students must fulfil the MIBTP entry requirements and will join the MIBTP cohort for the taught modules and masterclasses during the first term. They will remain as an integral part of the MIBTP cohort and take part in the core networking activities and transferable skills training. For further information, please contact u.noppeney at bham.ac.uk. Check eligibility and apply here: https://www2.warwick.ac.uk/fac/cross_fac/mibtp/pgstudy/phd_opportunities/application/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.tadel at mcgill.ca Tue Nov 15 21:33:42 2016 From: francois.tadel at mcgill.ca (=?UTF-8?Q?Fran=c3=a7ois_Tadel?=) Date: Tue, 15 Nov 2016 15:33:42 -0500 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields Message-ID: Hi Marco, > I have tested the Process > Sources > Fieldtrip: ft_prepare_leadfield on > different MEG data (always recorded from an Elekta 306 sensors system), and > I get an error "cannot work on balanced gradiometer definition" For some reason, ft_plot_sens doesn't want to get passed both the gradiometers and the magnetometers at once. Just run the process without the option "Display sensor/MRI registration" and you won't get this error any more. > I also have a basic question on the data to put in the process: my > understanding is that I should feed the process with the segmented anatomy, > that I identify with (for the default anatomy) the "Cortex_15002V" in the > anatomy tab. However, I cannot drag it to the Process window (I understand > that the sensor configuration is also needed). I therefore drag in the > Process window any data associated with the subject. Is this correct? Does > the program then automatically process the segmented anatomy? Yes, this is what you should do: select some recordings for the subject, and it will get the files it needs automatically from the database. Right now, this process does the following: - If the segmented masks produced previously with ft_volumesegment are available (called "mask_innerskull", "mask_outerskull" and "mask_scalp") it uses them. - Otherwise it uses the selected volume (displayed in green), passes it to ft_volumesegment for segmentation, then uses the segmented volumes without storing them anywhere. https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_prepare_leadfield.m#L153 Cheers, Francois -- François Tadel, MSc MEG / McConnell Brain Imaging Center / MNI / McGill University 3801 rue University, Montreal, QC H3A2B4, Canada -------------- next part -------------- An HTML attachment was scrubbed... URL: From hbharadw at purdue.edu Tue Nov 15 22:06:51 2016 From: hbharadw at purdue.edu (Bharadwaj, Hari M) Date: Tue, 15 Nov 2016 21:06:51 +0000 Subject: [FieldTrip] PhD student openings in auditory neuroscience at Purdue University Message-ID: <1479244011788.63656@purdue.edu> [Apologies for cross posting] Two PhD student positions are available at the Systems Neuroscience of Auditory Perception Lab at Purdue University. We study the neural mechanisms of auditory perception in complex multi-source environments (e.g., crowded restaurants or busy streets) using a combination of human neuroimaging techniques (EEG, MRI, otoacoustic emissions), behavioral listening experiments, and computational modeling. In addition we are interested in the effects of overexposure to loud sounds and early aging on the auditory system, and how that affects perception. A notable capability of the lab is the ability to non-invasively measure, and to model physiological responses at different levels of the auditory pathway from the cochlea to the cortex. Please visit https://engineering.purdue.edu/SNAPLab for more information about the lab, the facilities, and the vibrant intellectual environment at Purdue with a large community of hearing researchers and neuroscientists. Applications can be submitted through the Weldon School of Biomedical Engineering, or the Department of Speech, Language, and Hearing Sciences as described here. Informal enquiries about the positions can be directed to Hari Bharadwaj (hbharadwaj at purdue.edu). Preference will be given to applications received by December 15, 2016. -- Hari M. Bharadwaj, Ph.D. Assistant Professor of Speech, Language, and Hearing Sciences Assistant Professor of Biomedical Engineering Lyles-Porter Hall Purdue University 715 Clinic Drive, Room 3162 West Lafayette, IN 47907 (765) 496-2249 hbharadwaj at purdue.edu http://engineering.purdue.edu/SNAPLab -------------- next part -------------- An HTML attachment was scrubbed... URL: From mailtome.2113 at gmail.com Wed Nov 16 05:09:12 2016 From: mailtome.2113 at gmail.com (Arti Abhishek) Date: Wed, 16 Nov 2016 15:09:12 +1100 Subject: [FieldTrip] Questions about time frequency analysis of EEG data Message-ID: Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Wed Nov 16 08:58:21 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Wed, 16 Nov 2016 07:58:21 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: References: Message-ID: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From Simon.VanEyndhoven at esat.kuleuven.be Wed Nov 16 14:07:22 2016 From: Simon.VanEyndhoven at esat.kuleuven.be (Simon Van Eyndhoven) Date: Wed, 16 Nov 2016 14:07:22 +0100 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp Message-ID: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Dear all, I recently started using FieldTrip in order to simulate pseudo-realistic EEG recordings from e.g. a set of dipoles located in the brain. Therefore, I tried to follow the tutorial on the construction of a BEM head model based on an anatomical MRI scan and an electrode set: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . The problem is that I don't succeed to reproduce the head model that is distributed as a template in the FieldTrip toolbox: '/template/headmodel/standard_bem.mat'. I start from the template MRI scan in FieldTrip ('standard_mri.mat') and use the following code (taken from the tutorial). --- % Load MRI data and electrode setup mri = importdata('standard_mri.mat'); elec = ft_read_sens('standard_1020.elc') % Segment the MRI volume cfg = []; cfg.output = {'brain','skull','scalp'}; segmentedmri = ft_volumesegment(cfg, mri); % Create a mesh cfg = []; cfg.tissue = {'brain','skull','scalp'}; cfg.numvertices = [1500 1000 500]; bnd = ft_prepare_mesh(cfg,segmentedmri); % Construct the headmodel cfg = []; cfg.conductivity = [0.3300 0.0041 0.3300]; cfg.method = 'dipoli'; vol = ft_prepare_headmodel(cfg, bnd); --- The following error is thrown: "Fatal error in dipoli: during computation of B-matrix; vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of /tmp/tp39371164882184732_2.tri" I've tried to create meshes with different numbers of vertices (e.g. the combination from the website tutorial 'numvertices = [3000 2000 1000]' produces the same error): for some combinations this problem does not occur, luckily. However, I wonder how the template model (that used the aforementioned number of vertices) got created successfully; I want to make sure that I work in the correct way and don't forget something. Can anyone shed some light on this issue, or hint what might be wrong/missing in the implementation above, compared to the template results that are provided in the FieldTrip distribution? Moreover, I've tried to create the head model using the 'bemcp' method as well. As a test, I placed a dipole close to the scalp surface, near a particular electrode. It is hence expected that the recorded amplitudes are highest for electrodes in the vicinity of this electrode. This effect is correctly reproduced when performing the computation of the forward problem using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' method). By contrast, the output of the forward problem using the bemcp-based head model shows a topographic distribution with many positive 'peaks' and negative 'troughs', spread kind of randomly over the scalp surface. Since neither the segmentation step nor meshing step changed (only the final step, namely the creation of the volume conduction model), I have no clue why this problem occurs. I've looked around in the discussion list's archives for possible causes: - it is reported that the bemcp method does not behave well if the dipole is located (very) close to the skull: I ruled this out for my problem by varying this distance - one user (Debora Desideri) reported a problem with the bemcp-method that appears to be very similar to mine, but there was no reply to her problem... - another user (John Richards) calls for caution when using the bemcp-method, stating that it behaves poorly sometimes Has anyone experienced similar issues when using the 'becmp' method and maybe found an explanation for this? Thanks in advance for any help! Best regards, -- Simon Van Eyndhoven PhD researcher Stadius Center for Dynamical Systems, Signal Processing and Data Analytics KU Leuven, Dept. Electrical Engineering (ESAT) email: simon.vaneyndhoven at esat.kuleuven.be From litvak.vladimir at gmail.com Wed Nov 16 16:14:08 2016 From: litvak.vladimir at gmail.com (Vladimir Litvak) Date: Wed, 16 Nov 2016 15:14:08 +0000 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Message-ID: Dear Simon, I tested this before and now re-tested again using the versions of FT and BEMCP which come with SPM and should be quite recent. For SPM cortical mesh where we take special measures to make sure that it's far enough from the boundary the correlation coefficients between BEMCP and 3-spheres model are all above 0.9. You say you varied the depth but have you varied it enough? What 'enough' is depends on the density of your head meshes. For SPM meshes it was more than 6mm from the boundary. The difference between different BEM methods is how close you can get to the boundary without breaking. BEMCP is the simplest method which is not very good in this respect. dipoli or OpenMEEG will also break but closer to the surface. Best, Vladimir On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven < Simon.VanEyndhoven at esat.kuleuven.be> wrote: > Dear all, > > I recently started using FieldTrip in order to simulate pseudo-realistic > EEG recordings from e.g. a set of dipoles located in the brain. > Therefore, I tried to follow the tutorial on the construction of a BEM > head model based on an anatomical MRI scan and an electrode set: > http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . > > The problem is that I don't succeed to reproduce the head model that is > distributed as a template in the FieldTrip toolbox: > '/template/headmodel/standard_bem.mat'. I start from the template MRI > scan in FieldTrip ('standard_mri.mat') and use the following code (taken > from the tutorial). > > --- > % Load MRI data and electrode setup > mri = importdata('standard_mri.mat'); > elec = ft_read_sens('standard_1020.elc') > > % Segment the MRI volume > cfg = []; > cfg.output = {'brain','skull','scalp'}; > segmentedmri = ft_volumesegment(cfg, mri); > > % Create a mesh > cfg = []; > cfg.tissue = {'brain','skull','scalp'}; > cfg.numvertices = [1500 1000 500]; > bnd = ft_prepare_mesh(cfg,segmentedmri); > > % Construct the headmodel > cfg = []; > cfg.conductivity = [0.3300 0.0041 0.3300]; > cfg.method = 'dipoli'; > vol = ft_prepare_headmodel(cfg, bnd); > --- > > The following error is thrown: > > "Fatal error in dipoli: during computation of B-matrix; > vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of > /tmp/tp39371164882184732_2.tri" > > I've tried to create meshes with different numbers of vertices (e.g. the > combination from the website tutorial 'numvertices = [3000 2000 1000]' > produces the same error): for some combinations this problem does not > occur, luckily. However, I wonder how the template model (that used the > aforementioned number of vertices) got created successfully; I want to make > sure that I work in the correct way and don't forget something. > > Can anyone shed some light on this issue, or hint what might be > wrong/missing in the implementation above, compared to the template results > that are provided in the FieldTrip distribution? > > Moreover, I've tried to create the head model using the 'bemcp' method as > well. As a test, I placed a dipole close to the scalp surface, near a > particular electrode. It is hence expected that the recorded amplitudes are > highest for electrodes in the vicinity of this electrode. This effect is > correctly reproduced when performing the computation of the forward problem > using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' > method). By contrast, the output of the forward problem using the > bemcp-based head model shows a topographic distribution with many positive > 'peaks' and negative 'troughs', spread kind of randomly over the scalp > surface. Since neither the segmentation step nor meshing step changed (only > the final step, namely the creation of the volume conduction model), I have > no clue why this problem occurs. I've looked around in the discussion > list's archives for possible causes: > - it is reported that the bemcp method does not behave well if the dipole > is located (very) close to the skull: I ruled this out for my problem by > varying this distance > - one user (Debora Desideri) reported a problem with the bemcp-method that > appears to be very similar to mine, but there was no reply to her problem... > - another user (John Richards) calls for caution when using the > bemcp-method, stating that it behaves poorly sometimes > > Has anyone experienced similar issues when using the 'becmp' method and > maybe found an explanation for this? > > Thanks in advance for any help! > > Best regards, > > -- > Simon Van Eyndhoven > > PhD researcher > Stadius Center for Dynamical Systems, Signal Processing and Data Analytics > KU Leuven, Dept. Electrical Engineering (ESAT) > > email: simon.vaneyndhoven at esat.kuleuven.be > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Simon.VanEyndhoven at esat.kuleuven.be Thu Nov 17 10:34:56 2016 From: Simon.VanEyndhoven at esat.kuleuven.be (Simon Van Eyndhoven) Date: Thu, 17 Nov 2016 10:34:56 +0100 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Message-ID: <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> Hello Vladimir, Thank you for the swift response. I made sure to place the source at varying distances from the skull, to preclude any effect because of this. More precisely, I tried the following approach: -- elec = ft_read_sens('standard_1020.elc') chan_name = 'F8'; chan_index = find(strcmp(elec.label,chan_name)); chan_pos = elec.elecpos(chan_index,:); [...] cfg.dip.pos = 0.3*chan_pos; % I tried distances ranging from 0.3*chan_pos to 0.9*chan_pos -- For any distance from the skull the model yielded a nonsensical output as described earlier. This leads me to believe that this distance is not the determining factor here... Best regards, -- Simon Van Eyndhoven PhD researcher Stadius Center for Dynamical Systems, Signal Processing and Data Analytics KU Leuven, Dept. Electrical Engineering (ESAT) email: simon.vaneyndhoven at esat.kuleuven.be On 2016-11-16 16:14, Vladimir Litvak wrote: > Dear Simon, > > I tested this before and now re-tested again using the versions of FT and BEMCP which come with SPM and should be quite recent. For SPM cortical mesh where we take special measures to make sure that it's far enough from the boundary the correlation coefficients between BEMCP and 3-spheres model are all above 0.9. You say you varied the depth but have you varied it enough? What 'enough' is depends on the density of your head meshes. For SPM meshes it was more than 6mm from the boundary. The difference between different BEM methods is how close you can get to the boundary without breaking. BEMCP is the simplest method which is not very good in this respect. dipoli or OpenMEEG will also break but closer to the surface. > > Best, > > Vladimir > > On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven wrote: > >> Dear all, >> >> I recently started using FieldTrip in order to simulate pseudo-realistic EEG recordings from e.g. a set of dipoles located in the brain. >> Therefore, I tried to follow the tutorial on the construction of a BEM head model based on an anatomical MRI scan and an electrode set: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem [1] . >> >> The problem is that I don't succeed to reproduce the head model that is distributed as a template in the FieldTrip toolbox: '/template/headmodel/standard_bem.mat'. I start from the template MRI scan in FieldTrip ('standard_mri.mat') and use the following code (taken from the tutorial). >> >> --- >> % Load MRI data and electrode setup >> mri = importdata('standard_mri.mat'); >> elec = ft_read_sens('standard_1020.elc') >> >> % Segment the MRI volume >> cfg = []; >> cfg.output = {'brain','skull','scalp'}; >> segmentedmri = ft_volumesegment(cfg, mri); >> >> % Create a mesh >> cfg = []; >> cfg.tissue = {'brain','skull','scalp'}; >> cfg.numvertices = [1500 1000 500]; >> bnd = ft_prepare_mesh(cfg,segmentedmri); >> >> % Construct the headmodel >> cfg = []; >> cfg.conductivity = [0.3300 0.0041 0.3300]; >> cfg.method = 'dipoli'; >> vol = ft_prepare_headmodel(cfg, bnd); >> --- >> >> The following error is thrown: >> >> "Fatal error in dipoli: during computation of B-matrix; >> vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of /tmp/tp39371164882184732_2.tri" >> >> I've tried to create meshes with different numbers of vertices (e.g. the combination from the website tutorial 'numvertices = [3000 2000 1000]' produces the same error): for some combinations this problem does not occur, luckily. However, I wonder how the template model (that used the aforementioned number of vertices) got created successfully; I want to make sure that I work in the correct way and don't forget something. >> >> Can anyone shed some light on this issue, or hint what might be wrong/missing in the implementation above, compared to the template results that are provided in the FieldTrip distribution? >> >> Moreover, I've tried to create the head model using the 'bemcp' method as well. As a test, I placed a dipole close to the scalp surface, near a particular electrode. It is hence expected that the recorded amplitudes are highest for electrodes in the vicinity of this electrode. This effect is correctly reproduced when performing the computation of the forward problem using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' method). By contrast, the output of the forward problem using the bemcp-based head model shows a topographic distribution with many positive 'peaks' and negative 'troughs', spread kind of randomly over the scalp surface. Since neither the segmentation step nor meshing step changed (only the final step, namely the creation of the volume conduction model), I have no clue why this problem occurs. I've looked around in the discussion list's archives for possible causes: >> - it is reported that the bemcp method does not behave well if the dipole is located (very) close to the skull: I ruled this out for my problem by varying this distance >> - one user (Debora Desideri) reported a problem with the bemcp-method that appears to be very similar to mine, but there was no reply to her problem... >> - another user (John Richards) calls for caution when using the bemcp-method, stating that it behaves poorly sometimes >> >> Has anyone experienced similar issues when using the 'becmp' method and maybe found an explanation for this? >> >> Thanks in advance for any help! >> >> Best regards, >> >> -- >> Simon Van Eyndhoven >> >> PhD researcher >> Stadius Center for Dynamical Systems, Signal Processing and Data Analytics >> KU Leuven, Dept. Electrical Engineering (ESAT) >> >> email: simon.vaneyndhoven at esat.kuleuven.be >> >> _______________________________________________ >> fieldtrip mailing list >> fieldtrip at donders.ru.nl >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip [2] > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip Links: ------ [1] http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem [2] https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From litvak.vladimir at gmail.com Thu Nov 17 11:05:15 2016 From: litvak.vladimir at gmail.com (Vladimir Litvak) Date: Thu, 17 Nov 2016 10:05:15 +0000 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> Message-ID: Hi Simon, Then it might be something else. As I said, I checked yesterday in the latest SPM and it looks OK but there might be some code differences. We don't update our bemcp version every time we update the core FT. The approach Robert used and he might still have the script for it is to make a mesh for a single sphere and compare the bemcp solution for it with the analytical one. Vladimir On Thu, Nov 17, 2016 at 9:34 AM, Simon Van Eyndhoven < Simon.VanEyndhoven at esat.kuleuven.be> wrote: > Hello Vladimir, > > Thank you for the swift response. I made sure to place the source at > varying distances from the skull, to preclude any effect because of this. > More precisely, I tried the following approach: > > -- > elec = ft_read_sens('standard_1020.elc') > chan_name = 'F8'; > chan_index = find(strcmp(elec.label,chan_name)); > chan_pos = elec.elecpos(chan_index,:); > > [...] > > > cfg.dip.pos = 0.3*chan_pos; % I tried distances ranging from > 0.3*chan_pos to 0.9*chan_pos > -- > > For any distance from the skull the model yielded a nonsensical output as > described earlier. This leads me to believe that this distance is not the > determining factor here... > > Best regards, > > -- > Simon Van Eyndhoven > > PhD researcher > Stadius Center for Dynamical Systems, Signal Processing and Data Analytics > KU Leuven, Dept. Electrical Engineering (ESAT) > > email: simon.vaneyndhoven at esat.kuleuven.be > > On 2016-11-16 16:14, Vladimir Litvak wrote: > > Dear Simon, > > I tested this before and now re-tested again using the versions of FT and > BEMCP which come with SPM and should be quite recent. For SPM cortical mesh > where we take special measures to make sure that it's far enough from the > boundary the correlation coefficients between BEMCP and 3-spheres model are > all above 0.9. You say you varied the depth but have you varied it enough? > What 'enough' is depends on the density of your head meshes. For SPM meshes > it was more than 6mm from the boundary. The difference between different > BEM methods is how close you can get to the boundary without breaking. > BEMCP is the simplest method which is not very good in this respect. dipoli > or OpenMEEG will also break but closer to the surface. > > Best, > > Vladimir > > On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven < > Simon.VanEyndhoven at esat.kuleuven.be> wrote: > >> Dear all, >> >> I recently started using FieldTrip in order to simulate pseudo-realistic >> EEG recordings from e.g. a set of dipoles located in the brain. >> Therefore, I tried to follow the tutorial on the construction of a BEM >> head model based on an anatomical MRI scan and an electrode set: >> http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . >> >> The problem is that I don't succeed to reproduce the head model that is >> distributed as a template in the FieldTrip toolbox: >> '/template/headmodel/standard_bem.mat'. I start from the template MRI >> scan in FieldTrip ('standard_mri.mat') and use the following code (taken >> from the tutorial). >> >> --- >> % Load MRI data and electrode setup >> mri = importdata('standard_mri.mat'); >> elec = ft_read_sens('standard_1020.elc') >> >> % Segment the MRI volume >> cfg = []; >> cfg.output = {'brain','skull','scalp'}; >> segmentedmri = ft_volumesegment(cfg, mri); >> >> % Create a mesh >> cfg = []; >> cfg.tissue = {'brain','skull','scalp'}; >> cfg.numvertices = [1500 1000 500]; >> bnd = ft_prepare_mesh(cfg,segmentedmri); >> >> % Construct the headmodel >> cfg = []; >> cfg.conductivity = [0.3300 0.0041 0.3300]; >> cfg.method = 'dipoli'; >> vol = ft_prepare_headmodel(cfg, bnd); >> --- >> >> The following error is thrown: >> >> "Fatal error in dipoli: during computation of B-matrix; >> vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of >> /tmp/tp39371164882184732_2.tri" >> >> I've tried to create meshes with different numbers of vertices (e.g. the >> combination from the website tutorial 'numvertices = [3000 2000 1000]' >> produces the same error): for some combinations this problem does not >> occur, luckily. However, I wonder how the template model (that used the >> aforementioned number of vertices) got created successfully; I want to make >> sure that I work in the correct way and don't forget something. >> >> Can anyone shed some light on this issue, or hint what might be >> wrong/missing in the implementation above, compared to the template results >> that are provided in the FieldTrip distribution? >> >> Moreover, I've tried to create the head model using the 'bemcp' method as >> well. As a test, I placed a dipole close to the scalp surface, near a >> particular electrode. It is hence expected that the recorded amplitudes are >> highest for electrodes in the vicinity of this electrode. This effect is >> correctly reproduced when performing the computation of the forward problem >> using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' >> method). By contrast, the output of the forward problem using the >> bemcp-based head model shows a topographic distribution with many positive >> 'peaks' and negative 'troughs', spread kind of randomly over the scalp >> surface. Since neither the segmentation step nor meshing step changed (only >> the final step, namely the creation of the volume conduction model), I have >> no clue why this problem occurs. I've looked around in the discussion >> list's archives for possible causes: >> - it is reported that the bemcp method does not behave well if the dipole >> is located (very) close to the skull: I ruled this out for my problem by >> varying this distance >> - one user (Debora Desideri) reported a problem with the bemcp-method >> that appears to be very similar to mine, but there was no reply to her >> problem... >> - another user (John Richards) calls for caution when using the >> bemcp-method, stating that it behaves poorly sometimes >> >> Has anyone experienced similar issues when using the 'becmp' method and >> maybe found an explanation for this? >> >> Thanks in advance for any help! >> >> Best regards, >> >> -- >> Simon Van Eyndhoven >> >> PhD researcher >> Stadius Center for Dynamical Systems, Signal Processing and Data Analytics >> KU Leuven, Dept. Electrical Engineering (ESAT) >> >> email: simon.vaneyndhoven at esat.kuleuven.be >> >> _______________________________________________ >> 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.augusti at unsw.edu.au Fri Nov 18 00:59:05 2016 From: f.augusti at unsw.edu.au (f.augusti at unsw.edu.au) Date: Thu, 17 Nov 2016 23:59:05 +0000 Subject: [FieldTrip] hdr format correction Message-ID: Hi, I have some problems with the correct format of the reader. My data has been recorded in poly32 format and read by tms_read function. My header contains the following fields, but for some reason it's not being read from ft_definetrials and no event type are recognised: data.hdr.filename= 2048 data.hdr.path=67 data.hdr.chantype= 66x1 cell data.hdr.chanunit=1x67 cell data.hdr.label=68x1 cell data.hdr.nTrials=1 data.hdr.nSamplesPre=0 data.hdr.nSamples =776220 data.hdr.nChans=67 data.hdr.Fs=2048 >>cfg = []; cfg.dataset = 'FAonNC16112016.mat'; cfg.trialdef.eventtype = '?'; ft_definetrial(cfg); evaluating trialfunction 'ft_trialfun_general' Error using ft_read_header (line 2248) unsupported header format "matlab" Error in ft_trialfun_general (line 78) hdr = ft_read_header(cfg.headerfile, 'headerformat', cfg.headerformat); Error in ft_definetrial (line 177) [trl, event] = feval(cfg.trialfun, cfg); -------------- next part -------------- An HTML attachment was scrubbed... URL: From cecile.issard at etu.parisdescartes.fr Fri Nov 18 15:42:26 2016 From: cecile.issard at etu.parisdescartes.fr (Cecile Issard) Date: Fri, 18 Nov 2016 14:42:26 +0000 Subject: [FieldTrip] samples present in multiple trials Message-ID: <83D31873-260A-4C54-B941-FDB4B63D3B6F@etu.parisdescartes.fr> Hello, I got this error message when I used ft_databrowser : Warning: samples present in multiple trials, using only the last occurence of each sample > In ft_fetch_data at 145 In ft_databrowser>redraw_cb at 1544 In ft_databrowser>keyboard_cb at 1311 When preprocessing, I used cfg.trialdef.prestim = 3; cfg.trialdef.poststim = 4; and cfg.padding=10; cfg.padtype=‘data’; If I understand well, 1.5 seconds of supplementary data are added before and after each trial before filtering, but removed after filtering. Do I get this error message because my trials overlap with padding ? If yes why is it still the case after the filters have been applied ? I intentionally programmed ISI of 3 to 4 s to avoid overlaps (without padding). Also how should I choose the duration of padding ? Best wishes, Cécile -- Cécile Issard PhD student Laboratoire Psychologie de la Perception - UMR8242 45 rue des Sts Pères 75270 Paris cedex 06 http://lpp.psycho.univ-paris5.fr/index.php 01.70.64.99.69 @CecileIcecile -------------- next part -------------- An HTML attachment was scrubbed... URL: From cwilling at bu.edu Fri Nov 18 17:10:04 2016 From: cwilling at bu.edu (Carly Rose Willing) Date: Fri, 18 Nov 2016 11:10:04 -0500 Subject: [FieldTrip] Units of ERPS Message-ID: <2561EEF9-7246-4342-B989-9FC6140094C5@bu.edu> Hello, When I was plotting an ERP using ft_singleplotER from some BVA EEG data, I was wondering what unit the y-axis should be in by default. I want the y-axis to be in terms of μV, and it is obvious from comparison with my BVA output that this is not just a matter of scale. Does anyone know how to code to change the scaling/units? Thank you in advance! Carly Rose Willing --- Carly Rose Willing Boston University 2018 Lab Assistant Laboratory of Visual Cognitive Neuroscience Department of Psychological and Brain Sciences -------------- next part -------------- An HTML attachment was scrubbed... URL: From cecile.issard at etu.parisdescartes.fr Fri Nov 18 22:15:28 2016 From: cecile.issard at etu.parisdescartes.fr (Cecile Issard) Date: Fri, 18 Nov 2016 21:15:28 +0000 Subject: [FieldTrip] add statistical dispersion on erp plots Message-ID: Hello, Is there a way to add a measure of statistical dispersion (standard error of the mean or standard deviation) around ERPs in ft_singleplotER or ft_multiplotER ? Best wishes, Cécile -- Cécile Issard PhD student Laboratoire Psychologie de la Perception - UMR8242 45 rue des Sts Pères 75270 Paris cedex 06 http://lpp.psycho.univ-paris5.fr/index.php 01.70.64.99.69 @CecileIcecile -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Mon Nov 21 13:17:48 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Mon, 21 Nov 2016 12:17:48 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Message-ID: Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From davide.tabarelli at unitn.it Tue Nov 22 10:08:04 2016 From: davide.tabarelli at unitn.it (Davide Tabarelli) Date: Tue, 22 Nov 2016 10:08:04 +0100 Subject: [FieldTrip] Elekta Neuromag SSP projectors Message-ID: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> Dear community, my name is Davide Tabarelli and I’m currently working in the MEG lab @ Center for Mind Brain Sciences (Trento). I’m writing to get some formation about SSP projectors saved in Elekta Neuromag fif files. I know how the MNE python pipeline works (loading projectors & application on request) but I didn’t find information on how Filedtrip deals with these SSP projectors when loading data. Are they automatically applied? Are they discarded? Are they stored somewhere? Thank you in advance for your help. Have a nice day. D. — Davide Tabarelli, Ph.D. Center for Mind Brain Sciences (CIMeC) University of Trento, Via delle Regole, 101 38123 Mattarello (TN) Italy From mona at sdsc.edu Wed Nov 23 23:52:27 2016 From: mona at sdsc.edu (Wong-Barnum, Mona) Date: Wed, 23 Nov 2016 22:52:27 +0000 Subject: [FieldTrip] shifting data time In-Reply-To: References: Message-ID: Hi Anne: Thank you so much for your thoughtful suggestion! I will give it a try. Yes, we would like to have all the time continuous because we need to make some analysis based on the entire duration. Happy Thanksgiving (if you are in the US)! cheers, Mona On Oct 28, 2016, at 1:36 AM, anne Hauswald > wrote: Hi Mona, if you do cfg=[]; all=ft_appenddata(cfg, data1, data2, data3, data4); you get among the other fields all= time: {1x4 cell} %assuming each dataset equals one trial, which is what you want to replace. you could try to create a matrix, based on your sampling rate with all the continuous time points, here just for illustration: all_timepoints=[0:0.025:9.975] %adapt to your sampling rate and length of data, be very sure the end of one file is the beginning of the next one. (This is giving you all points from 0 to 9.975 with steps of 0.025) trl_timepoints=mat2cell(all_timepoints, 1, [100 100 100 100]) %here I create 4 cell arrays, corresponding e.g. to 4 trials will result in trl_timepoints = [1x100 double] [1x100 double] [1x100 double] [1x100 double] with continuous time information (trial 1 starts at 0, trial 2 at 2.5, trials 3 at 5, and trial 4 at 7.5) of the same length. this you can then use to replace the original time information: all_data.time_old=all_data.time %if you want to keep the original time information all_data.time=trl_timepoints In general: are you really sure that you need the continuous time information? There are many processing and analyses steps that can be done on the appended time information… There might be more elegant ways to do this. Hope there is no typo and that it helps Best Anne ********************************************* Mona Wong Web & iPad Application Developer San Diego Supercomputer Center "To handle yourself, use your head; to handle others, use your heart." -- Eleanor Roosevelt ********************************************* -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Thu Nov 24 11:12:22 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Thu, 24 Nov 2016 10:12:22 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Message-ID: <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> Hi Iris, I assume that you want to compare if spectra are different between these conditions. In that case, I would advise you to take a look at the Fieldtrip TFR-statistics tutorial, http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq. it explains how you can use cluster-based permutation to look for differences between spectra, within or between subjects. In your case, instead of comparing a baseline with an activation epoch (as described in the tutorial), you compare your (baseline-corrected) conditions A and B. Best, Stan From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Steinmann, Iris Sent: maandag 21 november 2016 13:18 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Thu Nov 24 17:02:26 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Thu, 24 Nov 2016 16:02:26 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> Message-ID: Hi Stan, Thanks a lot for your answer! The permutation test you suggested is exactly what I have done. But it gives me “only” the time-frequency bins that show a significant difference between TFR(A) and TFR(B). To visualize which of the conditions has higher or lower activity in the significant time-frequency bins I want to show a subtraction plot (TFR(A) minus TFR(B)) So, I’m not sure if I understood your answer correctly but I’m still struggling with the same two questions: 1. Is there anything wrong with subtracting TFR’s after baseline correction? 2. Is it anyhow valid to perform a linear operation (subtraction) on squared data (TFR) Sorry for still bothering you with this topic, but if anyone has an easy explanation, I would really appreciate. Thanks! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Donnerstag, 24. November 2016 11:12 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Iris, I assume that you want to compare if spectra are different between these conditions. In that case, I would advise you to take a look at the Fieldtrip TFR-statistics tutorial, http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq. it explains how you can use cluster-based permutation to look for differences between spectra, within or between subjects. In your case, instead of comparing a baseline with an activation epoch (as described in the tutorial), you compare your (baseline-corrected) conditions A and B. Best, Stan From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Steinmann, Iris Sent: maandag 21 november 2016 13:18 To: FieldTrip discussion list > Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From sherrykhan78 at gmail.com Fri Nov 25 06:39:42 2016 From: sherrykhan78 at gmail.com (Sheraz Khan) Date: Fri, 25 Nov 2016 00:39:42 -0500 Subject: [FieldTrip] Two Postdoctoral positions at the Manoach Lab @ MGH / Harvard Medical School Message-ID: On Behalf of Dr. Manoach: [Apologies for cross posting] Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Clinical/Cognitive Neuroscience Research Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with cognitive neuroscience, neuroimaging methodology including MEG and/or EEG and data analysis, and is interested in developing research questions and optimizing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. Requirements: PhD (or MD) in cognitive neuroscience, psychology or a related discipline and a strong research background are required. The ideal candidate will have a background in MEG and/or EEG methodology; research experience with clinical populations; experience in task design and analysis for cognitive experiments; fluency with statistical analysis packages. A background in computational neuroscience and/or signal processing (e.g., in Matlab) are beneficial PS. Here they are, distribute however you see fit. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach >>. Stipend levels are in line with experience and NIH. A two-year commitment is required. Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Signal Processing/Computational Neuroscience/Methodological Innovation Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with MEG/EEG methodology and data analysis, comfortable with methodological innovation, and is interested in optimizing and developing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. This is an excellent opportunity for scientists with a strong interest in clinical multimodal neuroimaging research. Requirements: PhD in neuroscience, engineering or a related field and a strong research background are required. Ideal candidates would have extensive experience in data analysis and/or an engineering background, be proficient in Matlab/Python and be interested in methods development. The following are beneficial: experience with MEG/EEG data analysis/methodology; background in cognitive neuroscience, experimental psychology and sleep; interest in clinical applications. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach >>. Stipend levels are in line with experience and NIH. A two-year commitment is required. ------------------------- Sheraz Khan, M.Eng, Ph.D. Instructor in Neurology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School McGovern Institute for Brain Research Massachusetts Institute of Technology Tel: +1 617-643-5634 Fax: +1 617-948-5966 Email: sheraz at nmr.mgh.harvard.edu sheraz at mit.edu Web: http://sheraz.mit.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From roycox.roycox at gmail.com Fri Nov 25 18:52:54 2016 From: roycox.roycox at gmail.com (Roy Cox) Date: Fri, 25 Nov 2016 12:52:54 -0500 Subject: [FieldTrip] 2 postdoctoral positions (Clinical/Cognitive Neuroscience Research & Signal Processing/Computational Neuroscience/Methodological Innovation) Message-ID: hello, On behalf of Dr. Dara Manoach I'm posting these two postdoctoral opportunities on the role of sleep in memory consolidation in healthy and clinical populations. Best, Roy ------------------------------------------------------------------------------------------------------ Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Clinical/Cognitive Neuroscience Research Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with cognitive neuroscience, neuroimaging methodology including MEG and/or EEG and data analysis, and is interested in developing research questions and optimizing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. Requirements: PhD (or MD) in cognitive neuroscience, psychology or a related discipline and a strong research background are required. The ideal candidate will have a background in MEG and/or EEG methodology; research experience with clinical populations; experience in task design and analysis for cognitive experiments; fluency with statistical analysis packages. A background in computational neuroscience and/or signal processing (e.g., in Matlab) are beneficial PS. Here they are, distribute however you see fit. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach . Stipend levels are in line with experience and NIH. A two-year commitment is required. ----------------------------------------------------------------------------------------------- Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Signal Processing/Computational Neuroscience/Methodological Innovation Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with MEG/EEG methodology and data analysis, comfortable with methodological innovation, and is interested in optimizing and developing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. This is an excellent opportunity for scientists with a strong interest in clinical multimodal neuroimaging research. Requirements: PhD in neuroscience, engineering or a related field and a strong research background are required. Ideal candidates would have extensive experience in data analysis and/or an engineering background, be proficient in Matlab/Python and be interested in methods development. The following are beneficial: experience with MEG/EEG data analysis/methodology; background in cognitive neuroscience, experimental psychology and sleep; interest in clinical applications. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach . Stipend levels are in line with experience and NIH. A two-year commitment is required. -------------- next part -------------- An HTML attachment was scrubbed... URL: From weiyong.w.xu at jyu.fi Mon Nov 28 15:21:31 2016 From: weiyong.w.xu at jyu.fi (Xu, Weiyong) Date: Mon, 28 Nov 2016 14:21:31 +0000 Subject: [FieldTrip] MNE cortical sheet parcellation with AAL atlas Message-ID: Dear all, After MNE source analysis with the template MRI(Colin27), I want to do parcellation with the AAL atlas. So first I checked how well the template cortical sheet and AAL atlas fit with the following code: -------------------------------------------------------------- aal = ft_read_atlas('C:\MyTemp\Toolbox\fieldtrip\fieldtrip_git\template\atlas\aal\ROI_MNI_V4.nii'); mne_sourcemodel=ft_read_headshape('cortex_8196.surf.gii'); cfg = []; cfg.interpmethod = 'nearest'; cfg.parameter = 'tissue'; mne_sourcemodel_with_label = ft_sourceinterpolate(cfg, aal, mne_sourcemodel); disp(mne_sourcemodel_with_label.tissuelabel') for i=1:length(mne_sourcemodel_with_label.tissue) if mne_sourcemodel_with_label.tissue(i)==0; mne_sourcemodel_with_label.tissue(i)=200; end; end; ft_plot_mesh(mne_sourcemodel_with_label,'vertexcolor',mne_sourcemodel_with_label.tissue,'edgecolor','black') colorbar --------------------------------------------------------- The result looks like that the parcellation of the sulci are not very good. Also parts of the cerebellum are included after interpolation. So I want to ask if there are surface-based atlas available (preferably also based on the Colin27)? And also I noticed the freesurfer pipeline creates cortical parcellation such as the Destrieux atlas, could I somehow utilize this in creating surface-based atlas for my MNE source model? Thanks in advance. Best, Weiyong Xu Ph.D. student Department of Psychology University of Jyväskylä -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: parcellation_with_AAL.pdf Type: application/pdf Size: 454680 bytes Desc: parcellation_with_AAL.pdf URL: From t.schneider.uke at icloud.com Mon Nov 28 16:11:26 2016 From: t.schneider.uke at icloud.com (Till Schneider) Date: Mon, 28 Nov 2016 16:11:26 +0100 Subject: [FieldTrip] PhD position in Cognitive Neuroscience Message-ID: Dear Fieldtrip community, please find attached a job offer for a PhD position in Cognitive Neuroscience in Hamburg, Germany. Best regards, Till Schneider — Dr. Till Schneider Cognitive and Clinical Neurophysiology Group Dept. of Neurophysiology and Pathophysiology University Medical Center Hamburg-Eppendorf Martinistr. 52 20246 Hamburg Germany phone +49-40-7410-53188 fax +49-40-7410-57126 www.uke.de/neurophysiology t.schneider at uke.de -------------- next part -------------- A non-text attachment was scrubbed... Name: Job offer Doctoral Student SPP1665.pdf Type: application/pdf Size: 74606 bytes Desc: not available URL: From christine.blume at sbg.ac.at Mon Nov 28 16:46:08 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 28 Nov 2016 15:46:08 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Message-ID: Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 28 17:03:31 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 28 Nov 2016 16:03:31 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment In-Reply-To: References: Message-ID: Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From guiraudh at gmail.com Mon Nov 28 18:42:58 2016 From: guiraudh at gmail.com (=?UTF-8?B?SMOpbMOobmUgR3VpcmF1ZA==?=) Date: Mon, 28 Nov 2016 18:42:58 +0100 Subject: [FieldTrip] Coherence values corresponding to the statistically significant area Message-ID: Dear Fieldtrip community, I'm working on coherence measures between MEG signal and auditory signal perceived during MEG recording. I realized sources analysis and statistics analysis with cluster-based permutation test (Montecarlo method). However I would like to have the coherence values corresponding to my statistically significant area, and I can not get it. Is it possible? I thank you in advance. Best, Hélène -------------- next part -------------- An HTML attachment was scrubbed... URL: From nancygao260 at hotmail.com Tue Nov 29 04:36:10 2016 From: nancygao260 at hotmail.com (gao nuo) Date: Tue, 29 Nov 2016 03:36:10 +0000 Subject: [FieldTrip] problems in emotiv fieldtrip and matlab Message-ID: Dear Sir: I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. What I did is : 1. my matlab is matlab R2014a, 2,download the fieldtrip 20161108; 3. add all the files to the matlab path; 4. installed MinGW and set the path of the /bin in environmental variables; 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. 6. run buffer.exe. 7 . run cmd.exe; 8. go to fieldtrip-20161108/realtime/bin/win32; 9. input the command line: emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] the response is: passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port 10. run viewer.exe, push connect botton. but no response. I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. thanks for your reading and look forward to the suggestions. best wishes. Gao Nuo -------------- next part -------------- An HTML attachment was scrubbed... URL: From nancygao260 at hotmail.com Tue Nov 29 04:45:15 2016 From: nancygao260 at hotmail.com (gao nuo) Date: Tue, 29 Nov 2016 03:45:15 +0000 Subject: [FieldTrip] Fw: problems in emotiv fieldtrip and matlab In-Reply-To: References: Message-ID: ________________________________ From: fieldtrip-bounces at science.ru.nl on behalf of gao nuo Sent: Monday, November 28, 2016 6:36 PM To: fieldtrip at science.ru.nl Subject: [FieldTrip] problems in emotiv fieldtrip and matlab Dear Sir: I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. What I did is : 1. my matlab is matlab R2014a, 2,download the fieldtrip 20161108; 3. add all the files to the matlab path; 4. installed MinGW and set the path of the /bin in environmental variables; 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. 6. run buffer.exe. 7 . run cmd.exe; 8. go to fieldtrip-20161108/realtime/bin/win32; 9. input the command line: emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] the response is: passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port 10. run viewer.exe, push connect botton. but no response. I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. thanks for your reading and look forward to the suggestions. best wishes. Gao Nuo -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Tue Nov 29 08:50:00 2016 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Tue, 29 Nov 2016 08:50:00 +0100 Subject: [FieldTrip] problems in emotiv fieldtrip and matlab In-Reply-To: References: Message-ID: <2182D0F4-2698-4C33-AF99-66DB9C79D074@donders.ru.nl> Dear Gao, it seems to me that in your step 9 the emotiv2ft did not actually start properly. It prints a help message out on screen, which I think it would only do if it did could not make sense of the command line options. If you specified verbatim "emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]]” then it would indeed not work. You should specify the right options in the brackets. The square [] brackets are optional, the <> brackets are required. So you should at least specify the configuration file. The defaults for the last three options (localhost, 1972 and 8000) should be ok, since you started the buffer in step 6 on the localhost with the default port (which is 1972). best regards, Robert > On 29 Nov 2016, at 04:36, gao nuo wrote: > > Dear Sir: > I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. > What I did is : > 1. my matlab is matlab R2014a, > 2,download the fieldtrip 20161108; > 3. add all the files to the matlab path; > 4. installed MinGW and set the path of the /bin in environmental variables; > 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. > 6. run buffer.exe. > 7 . run cmd.exe; > 8. go to fieldtrip-20161108/realtime/bin/win32; > 9. input the command line: > emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] > the response is: > passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port > > 10. run viewer.exe, push connect botton. but no response. > > I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. > > thanks for your reading and look forward to the suggestions. > > best wishes. > Gao Nuo > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From gaetan.sanchez at sbg.ac.at Tue Nov 29 10:13:24 2016 From: gaetan.sanchez at sbg.ac.at (Gaetan) Date: Tue, 29 Nov 2016 10:13:24 +0100 Subject: [FieldTrip] Salzburg Mind Brain Annual (SAMBA) Meeting 2017 - Announcement Message-ID: <7e7b674d-ee40-d29d-9e4f-a3b8128971e7@sbg.ac.at> Dear all, apologies in advance if you should receive this mail multiple times due to cross-posting on different lists. On behalf also of my colleagues and our advisory board, I am happy to announce the /*Salzburg Mind Brain Annual Meeting*/ (SAMBA) which will take place on the 13.-14. July 2017. Confirmed speakers for the upcoming event are: • Ole Jensen (Birmingham) • Catherine Tallon-Baudry (Paris) • Pascal Fries (Frankfurt) • Tobias Donner (Hamburg) • Angelika Lingnau (London) • Sylvain Baillet (Montreal) • Rosalyn Moran (Bristol) • Jan Mathijs Schoffelen (Nijmegen) • Christian-G. Bénar (Marseille) The workshop will be rather small (~100 participants) to enable lots of occasion for interactions. You will have the possibility to present a poster (please indicate while registering, with -at least- a tentative title. *The participation for SAMBA2017 is free*. For more information see the workshop website: https://samba.ccns.sbg.ac.at Also, prior to the workshop (11. & 12.07) there will be a Fieldtrip workshop help by Robert Oostenveld and Jan Mathijs Schoffelen. There are ~20 places for this event. *The participation for the Fieldtrip workshop is for free*. Registration is on the same site as above. So if you play it smart you can be part of 2 neuroscience highlights in 2017! Best, Nathan --------------------------------------------- Nathan Weisz Centre for Cognitive Neuroscience Division of Physiological Psychology University of Salzburg nathan.weisz at sbg.ac.at www.oboblab.at -- Gaëtan Sanchez, PhD Centre for Cognitive Neuroscience Hellbrunnerstraße 34, 5020 Salzburg - Austria Tel: +43 662 804 451 61 -------------- next part -------------- An HTML attachment was scrubbed... URL: From skelly2 at ccny.cuny.edu Tue Nov 29 12:48:25 2016 From: skelly2 at ccny.cuny.edu (Simon Kelly) Date: Tue, 29 Nov 2016 11:48:25 +0000 Subject: [FieldTrip] Postdoc opening in perceptual/cognitive neuroscience Message-ID: Applications are invited for a postdoctoral research post in the Cognitive Neural systems lab (https://cogneusys.com/) led by Simon Kelly, to study computational and neural mechanisms of value-biased sensorimotor decision making under time pressure. This position is part of a project funded by Science Foundation Ireland, which involves a combination of psychophysics, computational modelling, non-invasive electrophysiology of human brain and muscle, and analyses of existing single-cell neurophysiological data. Though involvement is expected in all of these aspects, the most critical role of the postdoctoral researcher will be in computational modelling. Candidates must thus have strong analytic and programming skills, and specific experience in the computational modelling of cognitive processes. Candidates must also be highly motivated and have excellent communication skills. Dr. Kelly's electrophysiology/psychophysics lab is situated within the School of Electrical and Electronic Engineering in University College Dublin, Ireland, and has strong collaborative links to cognitive and clinical neuroscience research groups both locally (e.g. Trinity College Institute of Neuroscience) and internationally (e.g. City College and Columbia University in New York). The successful applicant will have ample opportunities for wider collaborations and the learning of new skills. Interested candidates should submit a brief research statement and CV including publications through the UCD job vacancies site (http://www.ucd.ie/hr/jobvacancies/ - search for keyword 'kelly', or job ref 008870). Informal enquiries can be directed to Simon (simon.kelly at ucd.edu). Candidates should explain in their statement how their own research interests fit with those of the Kelly lab. The deadline for submitting applications is Jan 8th 2017, and shortlisting and interviews will take place shortly after that. ----------------------------------------------------------- Simon Kelly, Ph.D. Associate Professor School of Electrical and Electronic Engineering University College Dublin t: +353 (1) 716 1803 e: simon.kelly at ucd.ie -----------------------------------------------------------​ -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Wed Nov 30 09:57:21 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 30 Nov 2016 08:57:21 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Message-ID: Maybe an example will stimulate some discussion ;-). I test an interaction among two factors with two levels each with regard to the ERPs elicited by each stimulus. Let's say we present two stimuli (house vs. face) in colour (black/white vs. colour). The interaction is significant and we want to know which factor levels differ using post hoc tests using the following cfg. cfg = []; cfg.latency = [0 1]; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_depsamplesT'; cfg.correctm = 'cluster'; cfg.clusteralpha = 0.05; cfg.clusterstatistic = 'maxsum'; cfg.minnbchan = 2; cfg.tail = 0; cfg.clustertail = 0; cfg.alpha = 0.025; cfg.numrandomization = 1000; cfg_neighb.method = 'distance'; cfg.neighbours = ft_prepare_neighbours(cfg_neighb, data_sample); cfg.design = design; cfg.ivar = 2; cfg.uvar = 1; What we get is the following. stat_erp.FACE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.FACE_BW); stat_erp.FACE_COLOURvsFACE_BW.posclusters.prob % 18 clusters, p > 0.4076 stat_erp.FACE_COLOURvsFACE_BW.negclusters.prob % 12 clusters, p > 0.2957 stat_erp.FACE_COLOURvsHOUSE_COLOUR = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_COLOUR); stat_erp.FACE_COLOURvsHOUSE_COLOUR.posclusters.prob % 10 clusters, p > 0.4326 stat_erp.FACE_COLOURvsHOUSE_COLOUR.negclusters.prob % 12 clusters, p = 0.0599; p > 0.8631 stat_erp.FACE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_COLOURvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0360, p > 0.7742 stat_erp.FACE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0300, p > 0.5105 stat_erp.HOUSE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.FACE_BW); stat_erp.HOUSE_COLOURvsFACE_BW.posclusters.prob % 16 clusters, p > 0.2697 stat_erp.HOUSE_COLOURvsFACE_BW.negclusters.prob % 11 clusters, p = 0.0090, p > .0.9291 stat_erp.HOUSE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.HOUSE_COLOURvsHOUSE_BW.posclusters.prob % 19 clusters, p = 0.0020, p = 0.0979, p > 0.2468 stat_erp.HOUSE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0480, p = 0.0699, p > 0.3606 stat_erp.FACE_BWvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_BWvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0410, p > 0.7732 stat_erp.FACE_BWvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0320, p > 0.4725 Now the question is: how do we correct for these 6 follow-up tests? If we say we want to use the Bonferroni-Holm correction for example, we have to sort p-values according to size. However, we have two p-values per test, one for positive and one for negative clusters and Bonferroni-Holm assumes we only have one per test. Any ideas or suggestions how to best handle this? Best, Christine Von: Blume Christine Gesendet: Montag, 28. November 2016 17:04 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: AW: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Wed Nov 30 10:25:02 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 30 Nov 2016 09:25:02 +0000 Subject: [FieldTrip] File Size depends on No. of Trials? Message-ID: Dear Community, I have an issue with data size. I am processing high-density EEG data from a sleep study. Following preprocessing I perform time-frequency transformations and a baseline correction using the following code: cfg = []; cfg.method = 'wavelet'; cfg.output = 'pow'; cfg.keeptrials = 'no'; cfg.width = 3; % cfg.foi = 1:1:16; % cfg.toi = -0.7:0.2:0.9; freqanalysis_FV = ft_freqanalysis(cfg, data_FV); cfg = []; cfg.baseline = [-0.6, 0]; cfg.baselinetype = 'relchange'; ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) As you can see, I do not keep the trials. Still, the size of ERDERS seems to depend on the size of data_FV. Why is that the case? Also, changing cfg.foi does not change the file size - why? The problem is that this way the data can hardly be handled as it takes up so much RAM. Please note that I do clear everything from the workspace I do not need, that should not be the issue. Thanks a lot for your thoughts. Best, Christine -------------- next part -------------- An HTML attachment was scrubbed... URL: From julian.keil at gmail.com Wed Nov 30 10:31:37 2016 From: julian.keil at gmail.com (Julian Keil) Date: Wed, 30 Nov 2016 10:31:37 +0100 Subject: [FieldTrip] File Size depends on No. of Trials? In-Reply-To: References: Message-ID: Hi Christine, take a look into the cfg.previous of ERDERS. Fieldtrip stores information on previous data analysis steps there. Maybe the trial information is hidden somewhere in there. The MatLab command rmfield can then be used to remove some unwanted fields from a structure. But please beware! There is a good reason for the thorough bookkeeping in Fieldtrip (which has saved me quite often). Good luck, Julian On Wed, Nov 30, 2016 at 10:25 AM, Blume Christine wrote: > Dear Community, > > > > I have an issue with data size. I am processing high-density EEG data from > a sleep study. Following preprocessing I perform time-frequency > transformations and a baseline correction using the following code: > > > > cfg = []; > > cfg.method = 'wavelet'; > > cfg.output = 'pow'; > > cfg.keeptrials = 'no'; > > cfg.width = 3; % > > cfg.foi = 1:1:16; % > > cfg.toi = -0.7:0.2:0.9; > > freqanalysis_FV = ft_freqanalysis(cfg, data_FV); > > > > cfg = []; > > cfg.baseline = [-0.6, 0]; > > cfg.baselinetype = 'relchange'; > > ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) > > > > As you can see, I do *not* keep the trials. Still, the size of ERDERS > seems to depend on the size of data_FV. Why is that the case? Also, > changing cfg.foi does not change the file size – why? The problem is that > this way the data can hardly be handled as it takes up so much RAM. Please > note that I do clear everything from the workspace I do not need, that > should not be the issue. > > > > Thanks a lot for your thoughts. > > > > Best, > > Christine > > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Claudio.Georgii at stud.sbg.ac.at Wed Nov 30 11:50:46 2016 From: Claudio.Georgii at stud.sbg.ac.at (Claudio Georgii) Date: Wed, 30 Nov 2016 11:50:46 +0100 Subject: [FieldTrip] File Size depends on No. of Trials? In-Reply-To: References: Message-ID: Hi Christine, In addition you could change the precision from double to single (which reduces the working memory needed by a half). As far as i know, double precision is not needed here and single should do fine, but please correct me if I am wrong. Claudio 2016-11-30 10:31 GMT+01:00 Julian Keil : > Hi Christine, > > take a look into the cfg.previous of ERDERS. Fieldtrip stores information > on previous data analysis steps there. Maybe the trial information is > hidden somewhere in there. > The MatLab command rmfield can then be used to remove some unwanted fields > from a structure. But please beware! There is a good reason for the > thorough bookkeeping in Fieldtrip (which has saved me quite often). > Good luck, > > Julian > > On Wed, Nov 30, 2016 at 10:25 AM, Blume Christine < > christine.blume at sbg.ac.at> wrote: > >> Dear Community, >> >> >> >> I have an issue with data size. I am processing high-density EEG data >> from a sleep study. Following preprocessing I perform time-frequency >> transformations and a baseline correction using the following code: >> >> >> >> cfg = []; >> >> cfg.method = 'wavelet'; >> >> cfg.output = 'pow'; >> >> cfg.keeptrials = 'no'; >> >> cfg.width = 3; % >> >> cfg.foi = 1:1:16; % >> >> cfg.toi = -0.7:0.2:0.9; >> >> freqanalysis_FV = ft_freqanalysis(cfg, data_FV); >> >> >> >> cfg = []; >> >> cfg.baseline = [-0.6, 0]; >> >> cfg.baselinetype = 'relchange'; >> >> ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) >> >> >> >> As you can see, I do *not* keep the trials. Still, the size of ERDERS >> seems to depend on the size of data_FV. Why is that the case? Also, >> changing cfg.foi does not change the file size – why? The problem is that >> this way the data can hardly be handled as it takes up so much RAM. Please >> note that I do clear everything from the workspace I do not need, that >> should not be the issue. >> >> >> >> Thanks a lot for your thoughts. >> >> >> >> Best, >> >> Christine >> >> >> >> _______________________________________________ >> 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 > -- Claudio Georgii, MSc. Phd student University of Salzburg - Department of Psychology Eating Behavior Laboratory Hellbrunnerstraße 34 5020 Salzburg - Austria Phone: 0043- (0)662 8044 5164 E-Mail: claudio.georgii at sbg.ac.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christophe.grova at mcgill.ca Wed Nov 30 15:33:47 2016 From: christophe.grova at mcgill.ca (Christophe Grova) Date: Wed, 30 Nov 2016 14:33:47 +0000 Subject: [FieldTrip] Postdoctoral position for a neurologist/epileptologist available in the Multimodal Functional Imaging Lab, Montreal (Montreal Neurological Inst. McGill U. and PERFORM Concordia U.) In-Reply-To: References: , Message-ID: Dear all, please see below the opportunity for a postdoctoral position in my lab. The candidate will join a multidisciplinary team composed of neurologists and methodologists within the Multimodal Functional Imaging Laboratory, directed by Pr. Christophe Grova. The laboratory is actually based on two sites: (i) Physics Dpt at Concordia University and PERFORM center, (ii) Biomedical Engineering Dpt and epilepsy group of the Montreal Neurological Institute, McGill University. Both environments offer unique platforms with access to several modalities (simultaneous high-density EEG/fMRI, MEG, simultaneous EEG/NIRS, TMS). The main expertise of the team is the development and the validation of source localization methods dedicated for EEG, MEG and NIRS as well as multimodal characterization of resting state brain activity. Project: Multimodal investigation of epileptic activity using simultaneous EEG/MEG and EEG/NIRS acquisitions. The proposed project aims at localizing and characterizing the generators of epileptic activity using simultaneous acquisitions of ElectroEncephaloGraphy (EEG) with Magneto-EncephaloGraphy (MEG), as well as simultaneous acquisitions of EEG with Near Infra-Red Spectroscopy (NIRS). EEG and MEG are respectively measuring on the scalp electric and magnetic fields generated by neuronal activity at a millisecond scale, providing a detailed description of brain bioelectrical activity. Combined with EEG measuring brain electric activity on the scalp, NIRS allows studying hemodynamic processes at the time of spontaneous epileptic activity. The specificity of NIRS data is its ability to measure local changes oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), exploiting absorption properties of infrared light within brain tissue using optic fibers placed on the surface of the head (temporal resolution: 10 ms, 16 sources x 32 detectors, penetration: 2-3 cm from the surface of the head). While methodological developpments in the lab will consist in 3D reconstruction of the generators of EEG, MEG and NIRS signals and assessing multimodal concordances between bioelectrical neuronal signals and hemodynamic processes, the purpose of this Postdoctoral project will be to assess the integrity of neurovascular coupling processes at the time of epileptic discharges, using a unique multimodal environment involving EEG/MEG (Pellegrino et al HBM 2016), EEG/NIRS (Pellegrino et al Frontiers in Neurosc. 2016) and also EEG/fMRI recordings (Heers et al HBM 2014). Close collaborations with the epilepsy group of the Montreal Neurological Institute, involving notably Dr E. Kobayashi MD-PhD, Dr F. Dubeau MD-PhD and Dr. J. Gotman PhD, will provide access to patient populations and additional clinical expertise for this project. Requirements: The candidate should be an MD (neurologist) with previous training in epileptology and neurophysiology (EEG). Expertise in analyzing MEG or NIRS signals and/or computational skills including neuroimaging softwares are appreciated additional qualification. The candidate should be fluent in English (and if possible French) due to the patient population studied. Supervisor: Christophe Grova Ph.D. Assistant Professor, Physics Dpt and PERFORM, Concordia Univ. Chair of PERFORM Applied Bio-Imaging Committee Adjunct Professor, Biomedical Engineering and Neurology & Neurosurgery dpts, McGill Univ. Member Epilepsy Group, Montreal Neurological Institute Director of the Multimodal Functional Imaging Laboratory Email: christophe.grova at concordia.ca christophe.grova at mcgill.ca Please send your CV and motivation letter before Dec 15th 2016 to christophe.grova at concordia.ca *************************** Christophe Grova, PhD Assistant Professor, Physics Dpt, Concordia University PERFORM centre, Concordia University Chair of PERFORM Applied Bio-Imaging Committee (ABC) Adjunct Prof in Biomedical Engineering, and Neurology and Neurosurgery Dpt, McGill University Multimodal Functional Imaging Lab (Multi FunkIm) Montreal Neurological Institute - epilepsy group Centre de Recherches en Mathématiques Physics Dpt Concordia University - Loyola Campus - Office SP 365.12 7141 Sherbrooke Street West, Montreal, QC H4B 1R6 Phone: (514) 848-2424 ext.4221 email : christophe.grova at concordia.ca , christophe.grova at mcgill.ca Explore Concordia: http://explore.concordia.ca/christophe-grova Physics, Concordia University: http://www.concordia.ca/artsci/physics/faculty.html?fpid=christophe-grova McGill University: http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/PeopleChristophe MultiFunkIm Lab: http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/HomePage *************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 30 15:57:21 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 30 Nov 2016 14:57:21 +0000 Subject: [FieldTrip] Elekta Neuromag SSP projectors In-Reply-To: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> References: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> Message-ID: <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> Hi Davide, At the moment, there is no support for this in Fieldtrip. However, this issue has come up in the past, and back then a bug was filed in our bug tracking system bugzilla.fieldtriptoolbox.org. The bug id is 2109; it has been silent for a while, but if there’s sufficient interest in getting this implemented it might be worthwhile to revive it and get it done. Best wishes, Jan-Mathijs On 22 Nov 2016, at 10:08, Davide Tabarelli > wrote: Dear community, my name is Davide Tabarelli and I’m currently working in the MEG lab @ Center for Mind Brain Sciences (Trento). I’m writing to get some formation about SSP projectors saved in Elekta Neuromag fif files. I know how the MNE python pipeline works (loading projectors & application on request) but I didn’t find information on how Filedtrip deals with these SSP projectors when loading data. Are they automatically applied? Are they discarded? Are they stored somewhere? Thank you in advance for your help. Have a nice day. D. — Davide Tabarelli, Ph.D. Center for Mind Brain Sciences (CIMeC) University of Trento, Via delle Regole, 101 38123 Mattarello (TN) Italy _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From wanglinsisi at gmail.com Wed Nov 30 16:30:38 2016 From: wanglinsisi at gmail.com (Lin Wang) Date: Wed, 30 Nov 2016 10:30:38 -0500 Subject: [FieldTrip] Elekta Neuromag SSP projectors In-Reply-To: <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> References: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> Message-ID: Hi Davide and JM, Thanks for bring this up. I have the same question and I'd like to see how this can be implemented in fieldtrip. Best, LIn On Wed, Nov 30, 2016 at 9:57 AM, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Davide, > > At the moment, there is no support for this in Fieldtrip. > > However, this issue has come up in the past, and back then a bug was filed > in our bug tracking system bugzilla.fieldtriptoolbox.org. > The bug id is 2109; it has been silent for a while, but if there’s > sufficient interest in getting this implemented it might be worthwhile to > revive it and get it done. > > Best wishes, > Jan-Mathijs > > > > On 22 Nov 2016, at 10:08, Davide Tabarelli > wrote: > > Dear community, > > my name is Davide Tabarelli and I’m currently working in the MEG lab @ > Center for Mind Brain Sciences (Trento). > > I’m writing to get some formation about SSP projectors saved in Elekta > Neuromag fif files. > > I know how the MNE python pipeline works (loading projectors & application > on request) but I didn’t find information on how Filedtrip deals with these > SSP projectors when loading data. Are they automatically applied? Are they > discarded? Are they stored somewhere? > > Thank you in advance for your help. > > Have a nice day. > > D. > > — > Davide Tabarelli, Ph.D. > Center for Mind Brain Sciences (CIMeC) > University of Trento, > Via delle Regole, 101 > 38123 Mattarello (TN) > Italy > > > > > > _______________________________________________ > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 30 16:56:41 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 30 Nov 2016 15:56:41 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment In-Reply-To: References: Message-ID: <6DFB7B65-FBFB-4C9D-B84F-DB00A10EF204@donders.ru.nl> Hi Christine, I don’t have the answer to your specific question, but I want to raise a few points: Although the permutation framework as implemented in FT outputs 1 p-value per cluster (for a two-sided test, both for the ‘negative’ and for the ‘positive’ clusters), Only the smallest p-value counts for the statistical inference. This is because your inferential procedure is about making a binary decision, you either reject or accept your null-hypothesis. Also, note that in the permutation framework, without explicit adjustment, the p-values that come out reflect one-sided p-values. For a valid inference, you need to Bonferroni correct these (i.e. multiply them by 2), or adjust the critical alpha level. This being said, I would say that what you want to achieve (i.e. doing post-hoc tests) does not need to be done within the cluster-based framework. The clusters are just byproducts of your inferential procedure. Some general background on how to deal with the output of the tests can be found on: http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test In your specific case, where as a first step you have evaluated the interaction as a difference of differences, I would think it’s fine to use this result to justify a selection of channel + time points, across which you average the condition specific ERP, and which you subject to your post hoc tests. Best, Jan-Mathijs On 30 Nov 2016, at 09:57, Blume Christine > wrote: Maybe an example will stimulate some discussion ;-). I test an interaction among two factors with two levels each with regard to the ERPs elicited by each stimulus. Let’s say we present two stimuli (house vs. face) in colour (black/white vs. colour). The interaction is significant and we want to know which factor levels differ using post hoc tests using the following cfg. cfg = []; cfg.latency = [0 1]; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_depsamplesT'; cfg.correctm = 'cluster'; cfg.clusteralpha = 0.05; cfg.clusterstatistic = 'maxsum'; cfg.minnbchan = 2; cfg.tail = 0; cfg.clustertail = 0; cfg.alpha = 0.025; cfg.numrandomization = 1000; cfg_neighb.method = 'distance'; cfg.neighbours = ft_prepare_neighbours(cfg_neighb, data_sample); cfg.design = design; cfg.ivar = 2; cfg.uvar = 1; What we get is the following. stat_erp.FACE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.FACE_BW); stat_erp.FACE_COLOURvsFACE_BW.posclusters.prob % 18 clusters, p > 0.4076 stat_erp.FACE_COLOURvsFACE_BW.negclusters.prob % 12 clusters, p > 0.2957 stat_erp.FACE_COLOURvsHOUSE_COLOUR = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_COLOUR); stat_erp.FACE_COLOURvsHOUSE_COLOUR.posclusters.prob % 10 clusters, p > 0.4326 stat_erp.FACE_COLOURvsHOUSE_COLOUR.negclusters.prob % 12 clusters, p = 0.0599; p > 0.8631 stat_erp.FACE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_COLOURvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0360, p > 0.7742 stat_erp.FACE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0300, p > 0.5105 stat_erp.HOUSE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.FACE_BW); stat_erp.HOUSE_COLOURvsFACE_BW.posclusters.prob % 16 clusters, p > 0.2697 stat_erp.HOUSE_COLOURvsFACE_BW.negclusters.prob % 11 clusters, p = 0.0090, p > .0.9291 stat_erp.HOUSE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.HOUSE_COLOURvsHOUSE_BW.posclusters.prob % 19 clusters, p = 0.0020, p = 0.0979, p > 0.2468 stat_erp.HOUSE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0480, p = 0.0699, p > 0.3606 stat_erp.FACE_BWvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_BWvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0410, p > 0.7732 stat_erp.FACE_BWvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0320, p > 0.4725 Now the question is: how do we correct for these 6 follow-up tests? If we say we want to use the Bonferroni-Holm correction for example, we have to sort p-values according to size. However, we have two p-values per test, one for positive and one for negative clusters and Bonferroni-Holm assumes we only have one per test. Any ideas or suggestions how to best handle this? Best, Christine Von: Blume Christine Gesendet: Montag, 28. November 2016 17:04 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: AW: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 – 8044 5146 www.sleepscience.at _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From tigoum at naver.com Wed Nov 2 08:59:45 2016 From: tigoum at naver.com (=?UTF-8?B?7JWI66+87Z2s?=) Date: Wed, 2 Nov 2016 16:59:45 +0900 (KST) Subject: [FieldTrip] =?utf-8?q?_ft=5Fsourceanalysis_with_specific_EEG_data?= In-Reply-To: References: Message-ID: <341aa5e8a982eed4ceab879e19952f@cweb26.nm.nhnsystem.com> 대용량 첨부파일 1개(166MB)대용량 첨부 파일은 30일간 보관 / 100회까지 다운로드 가능 brainvision_EEG.zip 166MB 다운로드 기간: 2016/11/02 ~ 2016/12/02 Hello? I am a graduate student in Korea university , Korea. I have a own data that are exported from brainvision analyzer.It is consisting of 3 dimension such as 5 second interval, 240 epoch, 32 channel. I hope so analyzing by ft_sourceanalysis().Then I search the getting started & User documentation on fieldtrip homepage. And I found related information as "networkanalysis", it explain the usage of ft_sourceanalysiswith example MATLAB code. BUT, it is constructed for MEG dataset only, so I do not trying for my own EEG data as mentioned before. In the attached file, It include "networkanalysis.m" and my own data file.The "networkanalysis.m" is written by me as referred from fieldtrip homepage:http://www.fieldtriptoolbox.org/tutorial/networkanalysis?s[]=networkanalysis I really need your help. I really hope so analyzing my data by ft_sourceanalysis & eLORETA option. Please help me.Thank you very much. Best regards.Min-Hee, Ahn -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: networkanalysis.zip Type: application/x-zip-compressed Size: 10283 bytes Desc: not available URL: From Ramirez_U at ukw.de Wed Nov 2 11:41:11 2016 From: Ramirez_U at ukw.de (Ramirez Pasos, ) Date: Wed, 2 Nov 2016 10:41:11 +0000 Subject: [FieldTrip] Smoothing before permutation test Message-ID: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> Dear Fieldtrippers, I have a set of subcortical LFP signals from 8 patients, which I have analyzed with fieldtrip in order to obtain event-related time-frequency plots. Unfortunately, I have only three repetitions for each of my 4 conditions, so there's a lot of noise in the subject-averages of time-frequency data. I'm interested in testing baseline vs activation for each condition as well as comparisons between my 4 conditions (A1A2, A1B1, B1B2, A2B2). Could anyone tell me their take on smoothing before permuting? Is that a valid procedure with such a small sample size? I've searched for literature discussing smoothing before permuting (mostly the Holmes papers), where much talk of smoothing refers to concepts such as "locally pooled variance" and "pseudo t-statistics," but I don't know how this fits with fieldtrip's cluster statistics functions. Any thoughts would be greatly appreciated! U. Ramirez University of Wuerzburg From david.m.groppe at gmail.com Wed Nov 2 14:43:18 2016 From: david.m.groppe at gmail.com (David Groppe) Date: Wed, 2 Nov 2016 09:43:18 -0400 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> Message-ID: Since permutation tests exploit correlations between variables to increase sensitivity, smoothing each trial will increase your sensitivity. cheers, -David On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, wrote: > Dear Fieldtrippers, > > I have a set of subcortical LFP signals from 8 patients, which I have > analyzed with fieldtrip in order to obtain event-related time-frequency > plots. Unfortunately, I have only three repetitions for each of my 4 > conditions, so there's a lot of noise in the subject-averages of > time-frequency data. I'm interested in testing baseline vs activation for > each condition as well as comparisons between my 4 conditions (A1A2, A1B1, > B1B2, A2B2). > > Could anyone tell me their take on smoothing before permuting? Is that a > valid procedure with such a small sample size? I've searched for literature > discussing smoothing before permuting (mostly the Holmes papers), where > much talk of smoothing refers to concepts such as "locally pooled variance" > and "pseudo t-statistics," but I don't know how this fits with fieldtrip's > cluster statistics functions. > > Any thoughts would be greatly appreciated! > > U. Ramirez > University of Wuerzburg > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Wed Nov 2 17:29:50 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Wed, 02 Nov 2016 16:29:50 +0000 Subject: [FieldTrip] Time course for MNE In-Reply-To: References: Message-ID: Hello, I would like to compute the time course in the source , so far the source has been constructed but source.avg.filter has 1x24024 cells which each cell is 2x241, which 241 is the number of MEG sensors. Right now I cant figure out how the time course for each source can be computed. I appreciate if anybody can help. Best Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From Ramirez_U at ukw.de Wed Nov 2 18:02:00 2016 From: Ramirez_U at ukw.de (Ramirez Pasos, ) Date: Wed, 2 Nov 2016 17:02:00 +0000 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de>, Message-ID: <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> Thank you David for your response! Just to be clear: by "smoothing" I'm not referring to the smoothing performed when using the ft_frequencyanalysis multitaper method, but some kind of spatial smoothing on the time-frequency matrix obtained via ft_frequencyanalysis - so that according to your suggestion, I would smooth each trial, then average trials for each subject/condition, and finally use these for statistical evaluation? Is there a reason why smoothing each trial might be preferable to smoothing each subject's trial average? Thank you so much in advance, Uri ________________________________ Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]" im Auftrag von "David Groppe [david.m.groppe at gmail.com] Gesendet: Mittwoch, 2. November 2016 14:43 An: FieldTrip discussion list Betreff: Re: [FieldTrip] Smoothing before permutation test Since permutation tests exploit correlations between variables to increase sensitivity, smoothing each trial will increase your sensitivity. cheers, -David On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, > wrote: Dear Fieldtrippers, I have a set of subcortical LFP signals from 8 patients, which I have analyzed with fieldtrip in order to obtain event-related time-frequency plots. Unfortunately, I have only three repetitions for each of my 4 conditions, so there's a lot of noise in the subject-averages of time-frequency data. I'm interested in testing baseline vs activation for each condition as well as comparisons between my 4 conditions (A1A2, A1B1, B1B2, A2B2). Could anyone tell me their take on smoothing before permuting? Is that a valid procedure with such a small sample size? I've searched for literature discussing smoothing before permuting (mostly the Holmes papers), where much talk of smoothing refers to concepts such as "locally pooled variance" and "pseudo t-statistics," but I don't know how this fits with fieldtrip's cluster statistics functions. Any thoughts would be greatly appreciated! U. Ramirez University of Wuerzburg _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From david.m.groppe at gmail.com Wed Nov 2 18:33:01 2016 From: david.m.groppe at gmail.com (David Groppe) Date: Wed, 2 Nov 2016 13:33:01 -0400 Subject: [FieldTrip] Smoothing before permutation test In-Reply-To: <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> References: <6CBDD219388DCD478C1CFAB98E47DE820206C9E794@WKLEX08V.klinik.uni-wuerzburg.de> <6CBDD219388DCD478C1CFAB98E47DE820206C9E800@WKLEX08V.klinik.uni-wuerzburg.de> Message-ID: Smoothing a time-frequency matrix is just as valid. I would apply the smoothing to whatever it is you are plugging into the permutation test as an independent observation (in your case it sounds like trial averages). -D On Wed, Nov 2, 2016 at 1:02 PM, Ramirez Pasos, wrote: > Thank you David for your response! Just to be clear: by "smoothing" I'm > not referring to the smoothing performed when using the > ft_frequencyanalysis multitaper method, but some kind of spatial smoothing > on the time-frequency matrix obtained via ft_frequencyanalysis - so that > according to your suggestion, I would smooth each trial, then average > trials for each subject/condition, and finally use these for statistical > evaluation? Is there a reason why smoothing each trial might be preferable > to smoothing each subject's trial average? > > Thank you so much in advance, > Uri > ________________________________ > Von: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl]" > im Auftrag von "David Groppe [david.m.groppe at gmail.com] > Gesendet: Mittwoch, 2. November 2016 14:43 > An: FieldTrip discussion list > Betreff: Re: [FieldTrip] Smoothing before permutation test > > Since permutation tests exploit correlations between variables to increase > sensitivity, smoothing each trial will increase your sensitivity. > cheers, > -David > > On Wed, Nov 2, 2016 at 6:41 AM, Ramirez Pasos, Ramirez_U at ukw.de>> wrote: > Dear Fieldtrippers, > > I have a set of subcortical LFP signals from 8 patients, which I have > analyzed with fieldtrip in order to obtain event-related time-frequency > plots. Unfortunately, I have only three repetitions for each of my 4 > conditions, so there's a lot of noise in the subject-averages of > time-frequency data. I'm interested in testing baseline vs activation for > each condition as well as comparisons between my 4 conditions (A1A2, A1B1, > B1B2, A2B2). > > Could anyone tell me their take on smoothing before permuting? Is that a > valid procedure with such a small sample size? I've searched for literature > discussing smoothing before permuting (mostly the Holmes papers), where > much talk of smoothing refers to concepts such as "locally pooled variance" > and "pseudo t-statistics," but I don't know how this fits with fieldtrip's > cluster statistics functions. > > Any thoughts would be greatly appreciated! > > U. Ramirez > University of Wuerzburg > > > _______________________________________________ > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Thu Nov 3 17:27:40 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Thu, 3 Nov 2016 16:27:40 +0000 Subject: [FieldTrip] Permutation test with low amount of subjects participating in several sessions Message-ID: Hello Fieldtrip experts, currently I'm working on an intracranial dataset with low amount of subjects who participated repeatedly in an experiment with two different conditions. In detail: * LFP data from only three subjects. * Each subject participated several times in the same experiment (about 16 sessions per subject). * In every session subjects performed around 50 trials of condition A and around 50 trials of condition B I calculated time-frequency spectra (TFS) for the LFP's. To test if there are significant differences between the TFS(condition A) and TFS(condition B) I want to use the permutation test implemented in fieldtrip. Unfortunately I'm struggling with my little statistic knowledge, because of the low amount of subjects and the high repetition rate for every subject in multiple sessions. Here is what I have done so far, and it would be great if anyone could tell me if it is correct or totally bullshit. First I averaged over all trials, so I put one TFS for each condition and session in the statistic. The first row of the design matrix represent the repetition of the single subjects (in this case three) and the second row of the design matrix contains the two conditions A (as 1) and B (as 2). cfg = []; cfg.parameter = 'powspctrm'; cfg.numrandomization = 5000; cfg.method = 'montecarlo'; cfg.correctm = 'fdr'; cfg.alpha = 0.05; cfg.correcttail = 'prob'; cfg.ivar = 2; cfg.uvar = 1; cfg.statistic = 'ft_statfun_depsamplesT'; design = [1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]; cfg.design = design; stat = ft_freqstatistics(cfg, data_A, data_B); Thanks in advance! Iris -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Thu Nov 3 20:11:32 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 03 Nov 2016 19:11:32 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: References: Message-ID: Hello all, By using the data of HCP, and conduct eLORETA to compute the inverse problem, right now Id like to visualise the results to surface but due to not having any anatomical information saved in the matrix it does not show any mapping. Moreover, Id like to parcellate the cortex based on the AAL atlas, so I really appreciate if anybody can help me. Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 3 20:53:25 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 3 Nov 2016 19:53:25 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: References: Message-ID: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> Hi Mehdy, It is not clear to me what you want to achieve. It’s unclear what you mean with ‘the results’ to be visualized on ‘the surface’, and that you have no ‘anatomical information’ saved in ‘the matrix’, and that there is a lack of ‘any mapping’. All terms between the quotation signs (for the readers among us who understand Dutch: my daughter aptly calls these things ‘bovenkomma’tjes’) are not defined precisely enough by you to give pointers about what or whether anything is missing, or whether something goes wrong. In general, source-level data can be parcellated with ft_sourceparcellate, but only if your atlas is in the same space as your functional data. That is, there should be a one-to-one mapping between the source locations in your functional data, and the source locations in your atlas. If you want to use the AAL atlas, which is essentially defined as a volumetric image (probably at a voxel resolution of 1 or 2 mm), you need to interpolate/downsample this atlas onto your sourcemodel at the appropriate resolution .This would make most sense if your sourcemodel is also defined as a 3D grid, but it is not absolutely necessary. In order to interpolate the atlas onto your sourcemodel, you could use ft_sourceinterpolate (provided both atlas and sourcemodel are defined in the same coordinate system). Note that from your messages on this forum and on the HCP discussion list it is not clear to the reader what source model you used for the eLORETA. There is some information on the fieldtrip wiki that illustrates how to parcellate source reconstructed data http://www.fieldtriptoolbox.org/tutorial/networkanalysis In the example, it uses a surface-based parcellation, and parcellates a connectivity matrix. The function can also parcellate univariate data (e.g. time courses or power spectra), either or not defined on a 3D grid. Also, the HCP software+documentation that the MEG team released, and which accompanies the released data, might give you some pointers on how to do it. You could try and adapt the code provided to your own needs. Good luck, Jan-Mathijs J.M.Schoffelen Senior Researcher Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > On 03 Nov 2016, at 20:11, mehdy dousty wrote: > > Hello all, > By using the data of HCP, and conduct eLORETA to compute the inverse problem, right now Id like to visualise the results to surface but due to not having any anatomical information saved in the matrix it does not show any mapping. Moreover, Id like to parcellate the cortex based on the AAL atlas, so I really appreciate if anybody can help me. > Thanks > Mehdy > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From mehdy.dousty at gmail.com Thu Nov 3 21:14:51 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 03 Nov 2016 20:14:51 +0000 Subject: [FieldTrip] Parcellation Human connectome project-eLORETA In-Reply-To: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> References: <0C84064B-44AF-4174-8021-4C58788EE17F@donders.ru.nl> Message-ID: Hello, Thanks for the answer and sorry for my vague explanation. here is my code to compute the inverse problem by eLORETA using the provided MRI, DATA and Source model in HCP. % source localization for resting state HCP. load('100307_MEG_3-Restin_rmegpreproc.mat') ; % loading the data; load('100307_MEG_anatomy_headmodel.mat');% loading the headmodel tmp = load('100307_MEG_anatomy_sourcemodel_3d6mm.mat');% sourcemodel by 6mm individual_sourcemodel3d = tmp.sourcemodel3d; %% MRI individual_mri = ft_read_mri('T1w_acpc_dc_restore.nii.gz'); hcp_read_ascii('100307_MEG_anatomy_transform.txt'); individual_mri.transform = transform.vox07mm2bti; individual_mri.coordsys = 'bti'; %% converting to the same coordination unit individual_mri = ft_convert_units(individual_mri,'mm'); individual_sourcemodel3d = ft_convert_units(individual_sourcemodel3d,'mm'); headmodel = ft_convert_units(headmodel,'mm'); data.grad = ft_convert_units(data.grad,'mm'); %% leadfield matrix cfg = []; cfg.grid = individual_sourcemodel3d; cfg.headmodel = headmodel; cfg.grad = data.grad; cfg.channel = ft_channelselection('MEG',data.label); cfg.reducerank = 'no'; leadfield = ft_prepare_leadfield(cfg); %% timelcok cfg = []; cfg.covariance = 'yes'; cfg.covariancewindow = [0 1000]; cfg.keeptrials = 'yes'; timelockanalaysis = ft_timelockanalysis(cfg,data); %% Eloreta cfg = []; cfg.method = 'eloreta'; cfg.vol = headmodel; cfg.grid = leadfield; cfg.eloreta.lambda = 0.05; %Regularization parameters,cross-validation can be %used but as it resting state and we really dont know what the outupt looks %like then we have to use emprical numbers which cfg.mne.projectnoise = 'yes'; cfg.keepmom = 'yes'; %keep dipole moment cfg.mne.keepmom = 'yes'; cfg.senstype = 'meg'; cfg.keepfilter = 'yes'; cfg.eloreta.reducerank = 'no'; source_eloreta = ft_sourceanalysis (cfg, timelockanalaysis); so far , I compute the eLORETA, now I would like to project the data into 3d surface, like the way it was done for beamformer in http://www.fieldtriptoolbox.org/tutorial/plotting' and then make a parcellation based on some predefined atlases and then do the other analysis. I do appreciate for your helping. Thanks On Thu, Nov 3, 2016 at 1:53 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Mehdy, > > It is not clear to me what you want to achieve. It’s unclear what you mean > with ‘the results’ to be visualized on ‘the surface’, and that you have no > ‘anatomical information’ saved in ‘the matrix’, and that there is a lack of > ‘any mapping’. All terms between the quotation signs (for the readers among > us who understand Dutch: my daughter aptly calls these things > ‘bovenkomma’tjes’) are not defined precisely enough by you to give pointers > about what or whether anything is missing, or whether something goes wrong. > > In general, source-level data can be parcellated with ft_sourceparcellate, > but only if your atlas is in the same space as your functional data. That > is, there should be a one-to-one mapping between the source locations in > your functional data, and the source locations in your atlas. If you want > to use the AAL atlas, which is essentially defined as a volumetric image > (probably at a voxel resolution of 1 or 2 mm), you need to > interpolate/downsample this atlas onto your sourcemodel at the appropriate > resolution .This would make most sense if your sourcemodel is also defined > as a 3D grid, but it is not absolutely necessary. In order to interpolate > the atlas onto your sourcemodel, you could use ft_sourceinterpolate > (provided both atlas and sourcemodel are defined in the same coordinate > system). Note that from your messages on this forum and on the HCP > discussion list it is not clear to the reader what source model you used > for the eLORETA. > > There is some information on the fieldtrip wiki that illustrates how to > parcellate source reconstructed data > http://www.fieldtriptoolbox.org/tutorial/networkanalysis In the example, > it uses a surface-based parcellation, and parcellates a connectivity > matrix. The function can also parcellate univariate data (e.g. time courses > or power spectra), either or not defined on a 3D grid. > > Also, the HCP software+documentation that the MEG team released, and which > accompanies the released data, might give you some pointers on how to do > it. You could try and adapt the code provided to your own needs. > > Good luck, > > Jan-Mathijs > > > J.M.Schoffelen > Senior Researcher > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > > > > On 03 Nov 2016, at 20:11, mehdy dousty wrote: > > > > Hello all, > > By using the data of HCP, and conduct eLORETA to compute the inverse > problem, right now Id like to visualise the results to surface but due to > not having any anatomical information saved in the matrix it does not show > any mapping. Moreover, Id like to parcellate the cortex based on the AAL > atlas, so I really appreciate if anybody can help me. > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From marco.buiatti at gmail.com Fri Nov 4 11:08:10 2016 From: marco.buiatti at gmail.com (Marco Buiatti) Date: Fri, 4 Nov 2016 11:08:10 +0100 Subject: [FieldTrip] importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco On 16 August 2016 at 07:20, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Jeff, > Probably this is not yet implemented. Since it is all matlab-based it > should be pretty straightforward, once you know how the headmodel is > represented in the Brainstorm .mat file. > Once you have figured this out, it probably takes just a few lines of code > in ft_read_headshape. > > If you have an updated version of this function, you can easily contribute > it to the code-base for everyone’s use through git. > How this can be done, is shown here: http://www.fieldtriptoolbox.org/ > development/git > > Best, > Jan-Mathijs > > > > On 16 Aug 2016, at 02:05, K Jeffrey Eriksen wrote: > > Hi, > > I am trying to do some forward and inverse simulations using a 3-shell > BEM, and would prefer to import my model from Brainstorm where I have > already created it. I can find one example of importing the cortical mesh > from Brainstorm using ft_read_headshape with the ‘matlab’ format, but I > cannot find anything on how to import the Brainstorm BEM head model or > leadfield. > > Please point me to any further information on this topic, > -Jeff > > > _______________________________________________ > 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 > -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From cmuehl at gmail.com Fri Nov 4 11:37:08 2016 From: cmuehl at gmail.com (Christian Muehl) Date: Fri, 4 Nov 2016 10:37:08 +0000 Subject: [FieldTrip] Frontiers Research Topic: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures" Message-ID: <5568b746f06e44e198a78067481fe910@EXPRD02.hosting.ru.nl> *** Frontiers research topic - Call for Contributions *** We would like to invite contributions to the following research topic in Frontiers of Human Neuroscience: "Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures" Our objective is to publish a focused collection of open-access articles that represent the state of the art in detection and estimation of working memory and other cognitive functions based on neurophysiological signal classification and aimed at the application of such classified states in human-computer interaction. We specifically invite contributions that deal with the detection of cognitive states in complex scenarios as they are found in real world applications. Please refer to http://tinyurl.com/detectWM for more details and submission guidelines. * Please let us know if you are interested to contribute by replying to felix.putze at uni-bremen.de * Relevant Dates 31 January 2017 - Abstract 30 April 2017 - Manuscript * Topic Editors Felix Putze, University of Bremen, Germany Fabien Lotte, Inria Bordeaux Sud-Ouest, France Stephen Fairclough, Liverpool John Moores University, United Kingdom Christian Mühl, German Aerospace Center, Cologne, Germany * Topics of Interest Executive cognitive functions like working memory determine the success or failure of a wide variety of different cognitive tasks. Estimation of constructs like working memory load or memory capacity from neurophysiological or psychophysiological signals would enable adaptive systems to respond to cognitive states experienced by an operator and trigger responses designed to support task performance (e.g. by simplifying the exercises of a tutor system, or by shutting down distractions from the mobile phone). The determination of cognitive states like working memory load is also useful for automated testing/assessment, for usability evaluation and for tutoring applications. While there exists a huge body of research work on neural and physiological correlates of cognitive functions like working memory activity, fewer publications deal with the application of this research with respect to single-trial detection and real-time estimation of cognitive functions in complex, realistic scenarios. Single-trial classifiers based on brain activity measurements such as EEG, fNIRS or physiological signals such as EDA, ECG, BVP or Eyetracking have the potential to classify affective or cognitive states based upon short segments of data. For this purpose, signal processing and machine learning techniques need to be developed and transferred to real-world user interfaces. In this research topic, we are looking for: (1) studies in complex, realistic scenarios that specifically deal with cognitive states or cognitive processes (memory-related or other), (2) classification and estimation of cognitive states and processes like working memory activity, and (3) applications to Brain-Computer Interfaces and Human-Computer Interaction in general. Central open research questions which we would like to see approached in this research topic comprise: * How can working memory load be quantified with regression or classification models which are robust to perturbations common to realistic recording conditions and natural brain signal fluctuations? * How can detection and classification of cognitive states be used in Brain-Computer Interfaces (BCIs)? * How can multiple types of features or signal types be combined to achieve a more robust classification of working memory load? * How can working memory activity be differentiated from other types of cognitive or affective activity which co-vary with, but are not related to memory? * How well can insights from offline, average-analysis studies on memory activity be transferred to online, single-trial BCIs? * How can models of working memory load be calibrated to account for individual differences, for example in working memory capacity? * How can approaches from computational cognitive modeling be combined with physiological signals to assess memory processes? * How can working memory load be classified, for example according to modality (spatial memory, semantic memory, ...) or type of activity (encoding, retrieval, rehearsal, ...)? * How to design user-independent memory load estimators? Is that even feasible? * How can memory load estimators from a given context or modality be transferred to another modality and/or context? * How can working memory activity be classified to predict the outcome of the activity, for example by differentiating successful from failed encoding attempts? Additionally, we are also interested in other relevant submissions related to the research topic. We also sincerely invite manuscripts dealing with applications of memory-related interfaces (e.g. adaptive human-computer interfaces for memory-intensive tasks). Comprehensive review articles which critically reflect the state-of-the-art on a certain aspect of the topic are also welcome. With best regards, Felix Putze, Fabien Lotte, Stephen Fairclough, Christian Mühl. -------------- next part -------------- An HTML attachment was scrubbed... URL: From eriksenj at ohsu.edu Fri Nov 4 23:12:13 2016 From: eriksenj at ohsu.edu (K Jeffrey Eriksen) Date: Fri, 4 Nov 2016 22:12:13 +0000 Subject: [FieldTrip] importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Hi Marco, Sorry to say I was not successful, yet. I have shifted to other tasks at this point. I am using 256 channel EEG, and want to use the FreeSurfer cortical ribbon as my source model (I would prefer source/headmodels to be FEM as well). I found that a lot of FieldTrip is based on regular dipole grids, and also it is more oriented to MEG than EEG. I do not have the time right now to try to put all the pieces together in FieldTrip to do what I want, but might get back to it in a month or two. Sorry I could not be of more help to you right now. -Jeff From: Marco Buiatti [mailto:marco.buiatti at gmail.com] Sent: Friday, November 04, 2016 3:08 AM To: FieldTrip discussion list; K Jeffrey Eriksen Subject: Re: [FieldTrip] importing Brainstorm head and source models and leftfields Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco On 16 August 2016 at 07:20, Schoffelen, J.M. (Jan Mathijs) > wrote: Hi Jeff, Probably this is not yet implemented. Since it is all matlab-based it should be pretty straightforward, once you know how the headmodel is represented in the Brainstorm .mat file. Once you have figured this out, it probably takes just a few lines of code in ft_read_headshape. If you have an updated version of this function, you can easily contribute it to the code-base for everyone’s use through git. How this can be done, is shown here: http://www.fieldtriptoolbox.org/development/git Best, Jan-Mathijs On 16 Aug 2016, at 02:05, K Jeffrey Eriksen > wrote: Hi, I am trying to do some forward and inverse simulations using a 3-shell BEM, and would prefer to import my model from Brainstorm where I have already created it. I can find one example of importing the cortical mesh from Brainstorm using ft_read_headshape with the ‘matlab’ format, but I cannot find anything on how to import the Brainstorm BEM head model or leadfield. Please point me to any further information on this topic, -Jeff _______________________________________________ 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 -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.tadel at mcgill.ca Sat Nov 5 15:33:07 2016 From: francois.tadel at mcgill.ca (Francois Jean Tadel, Mr) Date: Sat, 5 Nov 2016 14:33:07 +0000 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Jeff, Marco: Good timing, I've been working on similar topics this week. I added processes in Brainstorm to use the forward models in FieldTrip, using ft_volumesegment, ft_prepare_headmodel and ft_prepare_leadfield. For now it is not possible to use the Brainstorm BEM surfaces to compute the leadfield with FieldTrip, but if you think this is useful, we could probably add this. Before the end of the month, I hope to have all the inverse models available as well. We will have the possibility to call them either with Brainstorm or FieldTrip forward solutions. I you want to help me with the debugging, or if you want to start working on the inverse/DICS part next week, it's all on github: https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_volumesegment.m https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_prepare_leadfield.m You could create a new process to call the FieldTrip function you want, there are already many other examples of wrappers available: process_ft_channelrepair.m process_ft_dipolefitting.m process_ft_scalpcurrentdensity.m process_ft_timelockstatistics.m process_ft_sourcestatistics.m process_ft_freqstatistics.m If you need help with the plugin API in Brainstorm: http://neuroimage.usc.edu/brainstorm/Tutorials/TutUserProcess Functions to convert Brainstorm files into FieldTrip structures: brainstorm3/toolbox/io/out_fieldtrip_*.m Cheers, Francois ________________________________ Hi Marco, Sorry to say I was not successful, yet. I have shifted to other tasks at this point. I am using 256 channel EEG, and want to use the FreeSurfer cortical ribbon as my source model (I would prefer source/headmodels to be FEM as well). I found that a lot of FieldTrip is based on regular dipole grids, and also it is more oriented to MEG than EEG. I do not have the time right now to try to put all the pieces together in FieldTrip to do what I want, but might get back to it in a month or two. Sorry I could not be of more help to you right now. -Jeff From: Marco Buiatti [mailto:marco.buiatti at gmail.com] Sent: Friday, November 04, 2016 3:08 AM To: FieldTrip discussion list; K Jeffrey Eriksen Subject: Re: [FieldTrip] importing Brainstorm head and source models and leftfields Dear all, I have Jeff's same question. I have anatomies ready in Brainstorm: segmented data imported, co-registration with MEG sensor space, computation of head model with overlapping spheres. Now I would like to import all this into Fieldtrip for source reconstruction of oscillatory sources (DICS). What's the best way to do this? And is there any difference between Brainstorm and Fieldtrip in these steps that I should be aware of? Jeff, did you manage to solve the problem? Thanks a lot, Marco -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Sun Nov 6 00:51:09 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Sat, 05 Nov 2016 23:51:09 +0000 Subject: [FieldTrip] BTI to MNI coordination Message-ID: Hello all, The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Sun Nov 6 16:22:17 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Sun, 6 Nov 2016 15:22:17 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: Message-ID: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip From mehdy.dousty at gmail.com Sun Nov 6 19:39:15 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Sun, 06 Nov 2016 18:39:15 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Message-ID: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < stan.vanpelt at donders.ru.nl> wrote: > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Sun Nov 6 21:02:17 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Sun, 6 Nov 2016 20:02:17 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> Message-ID: <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> ft_convert_coorsys is not accurate. Don’t use it if you want an accurate conversion. Best, Jan-Mathijs On 06 Nov 2016, at 19:39, mehdy dousty > wrote: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) > wrote: Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From daniel.haehnke at tum.de Mon Nov 7 14:49:35 2016 From: daniel.haehnke at tum.de (=?utf-8?B?SMOkaG5rZSwgRGFuaWVs?=) Date: Mon, 7 Nov 2016 13:49:35 +0000 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes Message-ID: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> Dear FT community, I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. Method 1: 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. 3. For each trial shuffle (see 1.) average across subsamples (see 2.). 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. Method 2: 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. Despite the differences in the steps, there are also the following differences in the two methods. In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). Best wishes, Daniel -- Daniel Hähnke PhD student Technische Universität München Institute of Neuroscience Translational NeuroCognition Laboratory Biedersteiner Straße 29, Bau 601 80802 Munich Germany Email: daniel.haehnke at tum.de Phone: +49 89 4140 3356 From mehdy.dousty at gmail.com Mon Nov 7 17:48:25 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Mon, 07 Nov 2016 16:48:25 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> Message-ID: Thanks for the answer. I am going to volumelookup the predefined sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to the ROI_MNI_V4, as the atlas has the MNI coordination systema and the sourcemodel has the bti coordinations system and there is no transform function in the sourcmodel to convert bti to mni, what is your suggestion to do so? Thanks On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > ft_convert_coorsys is not accurate. Don’t use it if you want an accurate > conversion. > Best, > Jan-Mathijs > > On 06 Nov 2016, at 19:39, mehdy dousty wrote: > > Thanks for the answer. how accurate to use ft_convert_coordsys (source, > 'spm'), and if it is not, as there is no explanation on wiki unfortunately, > how can I make this conversion . > Thanks > Mehdy > > On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < > stan.vanpelt at donders.ru.nl> wrote: > > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Mon Nov 7 20:52:33 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 7 Nov 2016 19:52:33 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> Message-ID: <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> Mehdy, As I suggested in an earlier e-mail it’s most convenient to use ft_sourceinterpolate to interpolate the atlas onto the sourcemodel. To this end you can use the template sourcemodel that comes shipped with the HCP megconnectome software. Once you have managed to do this, you can directly look-up the anatomical labels of the dipole positions, because the dipole positions in the individual sourcemodels (in bti space) by construction coincide with the template after transformation into mni space. This is explained on page 30 of the HCP MEG manual. I suggest you to consult this in somewhat more detail. Best wishes, Jan-Mathijs On 07 Nov 2016, at 17:48, mehdy dousty > wrote: Thanks for the answer. I am going to volumelookup the predefined sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to the ROI_MNI_V4, as the atlas has the MNI coordination systema and the sourcemodel has the bti coordinations system and there is no transform function in the sourcmodel to convert bti to mni, what is your suggestion to do so? Thanks On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) > wrote: ft_convert_coorsys is not accurate. Don’t use it if you want an accurate conversion. Best, Jan-Mathijs On 06 Nov 2016, at 19:39, mehdy dousty > wrote: Thanks for the answer. how accurate to use ft_convert_coordsys (source, 'spm'), and if it is not, as there is no explanation on wiki unfortunately, how can I make this conversion . Thanks Mehdy On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) > wrote: Hi Mehdy, You should use bti2spm for this. You can find more specific information on this on the HCP wiki website. Hope that helps. Stan > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > Hello all, > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP (Human connectome project) has a 'BTI' coordination system. As I am going to parcellate the data by an atlas which has a coordination system of "MNI", and could not find any transformation between BTI and MNI, I am wondering if anybody knows what are my steps to do so? > Thanks > Mehdy > _______________________________________________ > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdy.dousty at gmail.com Mon Nov 7 22:17:19 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Mon, 07 Nov 2016 21:17:19 +0000 Subject: [FieldTrip] BTI to MNI coordination In-Reply-To: <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> References: <0527DC7B-3B46-4E48-889A-1914C867FAB0@donders.ru.nl> <3AF8F6FC-7FC1-46B1-9C53-70D8A67D5533@donders.ru.nl> <5423A44F-E95B-442B-9CED-239CF90A7A77@donders.ru.nl> Message-ID: Thank you very much. As you suggest after computing the inverse problem I interpolate the atlas to the sourcemodel by the below commands: atlas = ft_read_atlas('/home/mehdy/Desktop/EEG-1/fieldtrip-20160417/template/atlas/aal/ROI_MNI_V4.nii'); cfg = []; cfg.interpmethod = 'nearest'; cfg.parameter = 'tissue'; individual_sourcemodel3d1 = ft_sourceinterpolate(cfg,atlas,individual_sourcemodel3d); and then interpolate the result of source construction to the computed source model , I mean individual_sourcemodel3d1: cfg = []; cfg.parameter = 'pow'; source_eloreta_disc_int1 = ft_sourceinterpolate(cfg,source_eloreta,individual_sourcemodel3d1); now the are the data parcellated? if it is so, how can I get the parcellation labels? Thanks On Mon, Nov 7, 2016 at 12:59 PM Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Mehdy, > > As I suggested in an earlier e-mail it’s most convenient to use > ft_sourceinterpolate to interpolate the atlas onto the sourcemodel. To this > end you can use the template sourcemodel that comes shipped with the HCP > megconnectome software. Once you have managed to do this, you can directly > look-up the anatomical labels of the dipole positions, because the dipole > positions in the individual sourcemodels (in bti space) by construction > coincide with the template after transformation into mni space. This is > explained on page 30 of the HCP MEG manual. I suggest you to consult this > in somewhat more detail. > > Best wishes, > Jan-Mathijs > > > > On 07 Nov 2016, at 17:48, mehdy dousty wrote: > > Thanks for the answer. I am going to volumelookup the predefined > sourcemodel which is provided by HCP, MEG_anatomy_sourcemodel_3d6mm.mat, to > the ROI_MNI_V4, as the atlas has the MNI coordination systema and the > sourcemodel has the bti coordinations system and there is no transform > function in the sourcmodel to convert bti to mni, what is your suggestion > to do so? > Thanks > > On Sun, Nov 6, 2016 at 1:02 PM Schoffelen, J.M. (Jan Mathijs) < > jan.schoffelen at donders.ru.nl> wrote: > > ft_convert_coorsys is not accurate. Don’t use it if you want an accurate > conversion. > Best, > Jan-Mathijs > > On 06 Nov 2016, at 19:39, mehdy dousty wrote: > > Thanks for the answer. how accurate to use ft_convert_coordsys (source, > 'spm'), and if it is not, as there is no explanation on wiki unfortunately, > how can I make this conversion . > Thanks > Mehdy > > On Sun, Nov 6, 2016 at 8:22 AM Pelt, S. van (Stan) < > stan.vanpelt at donders.ru.nl> wrote: > > Hi Mehdy, > > You should use bti2spm for this. You can find more specific information on > this on the HCP wiki website. > > Hope that helps. > > Stan > > > Op 6 nov. 2016, om 00:51 heeft mehdy dousty > het volgende geschreven: > > > > Hello all, > > The provided head model (MEG_anatomy_sourcemodel_3d6mm.mat) in the HCP > (Human connectome project) has a 'BTI' coordination system. As I am going > to parcellate the data by an atlas which has a coordination system of > "MNI", and could not find any transformation between BTI and MNI, I am > wondering if anybody knows what are my steps to do so? > > Thanks > > Mehdy > > _______________________________________________ > > 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From florian at brain.riken.jp Wed Nov 9 01:51:55 2016 From: florian at brain.riken.jp (Florian Gerard-Mercier) Date: Wed, 9 Nov 2016 09:51:55 +0900 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes In-Reply-To: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> Message-ID: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Dear Daniel, Did you get an answer already? I’m just wondering, what is the null hypothesis? Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions? Best, Florian Gerard-Mercier Lab. for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: 048 462 1111 and 7106 Mob: 080 3213 6851 > On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel wrote: > > Dear FT community, > > I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. > > I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. > > Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. > > Method 1: > 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. > 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. > 3. For each trial shuffle (see 1.) average across subsamples (see 2.). > 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. > > Method 2: > 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. > 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. > 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). > > Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. > > Despite the differences in the steps, there are also the following differences in the two methods. > In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. > Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. > > Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. > I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. > > It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). > > Best wishes, > > Daniel > -- > Daniel Hähnke > PhD student > > Technische Universität München > Institute of Neuroscience > Translational NeuroCognition Laboratory > Biedersteiner Straße 29, Bau 601 > 80802 Munich > Germany > > Email: daniel.haehnke at tum.de > Phone: +49 89 4140 3356 > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From daniel.haehnke at tum.de Wed Nov 9 14:30:04 2016 From: daniel.haehnke at tum.de (=?utf-8?B?SMOkaG5rZSwgRGFuaWVs?=) Date: Wed, 9 Nov 2016 13:30:04 +0000 Subject: [FieldTrip] Spike-Field-PLV z-scoring and comparison between three conditions of unequal sample sizes In-Reply-To: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Message-ID: <4E9EA3F3-0456-4367-A17B-8ED076223E70@tum.de> Hi Florian, thanks for your reply! I haven’t had any replies yet. You’re right, it’s important to form a null hypothesis before doing statistical tests. Explicitly, my null hypothesis is that the PLV is the same for all conditions. For that I’d need to shuffle the condition labels of the spike phases across conditions. The z-scoring was meant as a normalisation of the spike-lfp-combinations, so I can pool combinations via averaging. Of course, the trial association shuffling implicitly also tests the null hypothesis of zero PLV. Best, Daniel On 9 Nov 2016, at 01:51, Florian Gerard-Mercier > wrote: Dear Daniel, Did you get an answer already? I’m just wondering, what is the null hypothesis? Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions? Best, Florian Gerard-Mercier Lab. for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: 048 462 1111 and 7106 Mob: 080 3213 6851 On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel > wrote: Dear FT community, I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials. I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score. Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this. Method 1: 1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS. 2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times. 3. For each trial shuffle (see 1.) average across subsamples (see 2.). 4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages. Method 2: 1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times. 2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV. 3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.). Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores. Despite the differences in the steps, there are also the following differences in the two methods. In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes. Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers. Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV. I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound. It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?). Best wishes, Daniel -- Daniel Hähnke PhD student Technische Universität München Institute of Neuroscience Translational NeuroCognition Laboratory Biedersteiner Straße 29, Bau 601 80802 Munich Germany Email: daniel.haehnke at tum.de Phone: +49 89 4140 3356 _______________________________________________ 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From Darren.Price at mrc-cbu.cam.ac.uk Wed Nov 9 17:11:08 2016 From: Darren.Price at mrc-cbu.cam.ac.uk (Darren Price) Date: Wed, 9 Nov 2016 16:11:08 +0000 Subject: [FieldTrip] Open-Access Dataset: Cambridge Centre for Ageing and Neuroscience (CamCAN) Message-ID: Dear Fieldtrippers The Cambridge Centre for Ageing and Neuroscience (Cam-CAN; www.cam-can.org) is pleased to announce the release of raw data from the first wave of Phase II of the Cam-CAN cohort. These data include MRI, MEG and cognitive data from approximately 650 males and females uniformly distributed from 18 to 88 years of age. The sample is unique in its population-representativeness (e.g, relative to national census data) and the depth and breadth of neuroimaging and cognitive assessment. The MRI data (in NIFTI and BIDS format) include T1-weighted, T2-weighted and Diffusion-weighted 3T MRI images, plus 3 runs of BOLD-weighted images during 1) rest, 2) movie-watching and 3) an event-related sensorimotor task (with combined visual and auditory stimuli cueing a motor response); the MEG data (in FIF format) include 3 runs of 1) rest, 2) the same sensorimotor task as the fMRI and 3) a passive sensory task (with separate visual and auditory stimuli); the behavioural data include scores on tasks assessing a range of cognitive domains, such as fluid intelligence, memory, language, among others. For more information about the CamCAN project and data, see: Shafto et al. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology, 14(204). doi:10.1186/s12883-014-0204-1. Taylor et al. (2015). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage. 10.1016/j.neuroimage.2015.09.018. We hope to release more data (e.g, MT-weighted and preprocessed MRI/MEG data) in future. The data are provided freely after agreeing to minimal conditions, via this page: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ Darren ------------------------------------------------------- Dr. Darren Price Investigator Scientist and Cam-CAN Data Manager MRC Cognition & Brain Sciences Unit 15 Chaucer Road Cambridge, CB2 7EF England EMAIL: darren.price at mrc-cbu.cam.ac.uk URL: http://www.mrc-cbu.cam.ac.uk/people/darren.price TEL +44 (0)1223 355 294 x202 FAX +44 (0)1223 359 062 MOB +44 (0)7717822431 ------------------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From ph442 at cam.ac.uk Wed Nov 9 18:45:27 2016 From: ph442 at cam.ac.uk (parham hashemzadeh) Date: Wed, 09 Nov 2016 17:45:27 +0000 Subject: [FieldTrip] Literature or methods in Fieldtrip on estimating missing data. In-Reply-To: <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> References: <5490DE3E-9B37-4B52-A7F1-407FB1AEA891@tum.de> <536FEA15-568A-4020-A83E-F7311E630E36@brain.riken.jp> Message-ID: Dear all I was wondering, if you know of any resources or techniques in dealing with missing data. Does fieldtrip have any routines . I am looking for better methods that extrapolate data points to where there are no sensors. Are there any studies or papers on extrapolation or estimating data at points where there are no sensors. Maybe some matlab routines. I would appreciate any help you are willing to provide on known literature. best regards parham From marco.buiatti at gmail.com Thu Nov 10 11:56:03 2016 From: marco.buiatti at gmail.com (Marco Buiatti) Date: Thu, 10 Nov 2016 11:56:03 +0100 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields In-Reply-To: References: Message-ID: Dear Francois, this is really good news, thanks! I have tested the Process > Sources > Fieldtrip: ft_prepare_leadfield on different MEG data (always recorded from an Elekta 306 sensors system), and I get an error "cannot work on balanced gradiometer definition" (see the warnings below). I have tried to debug this but I am a bit lost since I am not very familiar with manipulating source data. I also have a basic question on the data to put in the process: my understanding is that I should feed the process with the segmented anatomy, that I identify with (for the default anatomy) the "Cortex_15002V" in the anatomy tab. However, I cannot drag it to the Process window (I understand that the sensor configuration is also needed). I therefore drag in the Process window any data associated with the subject. Is this correct? Does the program then automatically process the segmented anatomy? Can you please shed light on this, or advice me on how to debug it? Thanks a lot, Marco BST> FieldTrip install: C:\Users\marco.buiatti\Documents\software\fieldtrip-20161107 the input is volume data with dimensions [181 217 181] Converting the coordinate system from ctf to spm Rescaling NIFTI: slope = 0.00342945, intercept = 0 Smoothing by 0 & 8mm.. Coarse Affine Registration.. Fine Affine Registration.. performing the segmentation on the specified volume creating brainmask smoothing brainmask with a 5-voxel FWHM kernel thresholding brainmask at a relative threshold of 0.500 the call to "ft_volumesegment" took 52 seconds Warning: assuming that planar MEG channel units are T/m > In ft_chanunit at 173 In ft_datatype_sens at 392 In ft_datatype_sens at 158 In ft_checkconfig at 232 In utilities\private\ft_preamble_trackconfig at 37 In ft_preamble at 56 In ft_prepare_headmodel at 148 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 Warning: please specify cfg.method='projectmesh', 'iso2mesh' or 'isosurface' > In ft_prepare_mesh at 137 In ft_prepare_headmodel at 348 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 Warning: using 'projectmesh' as default > In ft_prepare_mesh at 138 In ft_prepare_headmodel at 348 In process_ft_prepare_leadfield>Run at 249 In process_ft_prepare_leadfield at 24 In bst_process>Run at 229 In bst_process at 36 In panel_process1>RunProcess at 141 In panel_process1 at 27 In gui_brainstorm>CreateWindow/ProcessRun_Callback at 707 In bst_call at 28 In gui_brainstorm>@(h,ev)bst_call(@ProcessRun_Callback) at 261 triangulating the outer boundary of compartment 1 (brain) with 3000 vertices the call to "ft_prepare_mesh" took 1 seconds the call to "ft_prepare_headmodel" took 1 seconds *************************************************************************** ** Error: [process_ft_prepare_leadfield] Sources > FieldTrip: ft_prepare_leadfield ** Line 228: ft_plot_sens (line 228) ** cannot work with balanced gradiometer definition ** ** Call stack: ** >ft_plot_sens.m at 228 ** >process_ft_prepare_leadfield.m>Run at 255 ** >process_ft_prepare_leadfield.m at 24 ** >bst_process.m>Run at 229 ** >bst_process.m at 36 ** >panel_process1.m>RunProcess at 141 ** >panel_process1.m at 27 ** >gui_brainstorm.m>CreateWindow/ProcessRun_Callback at 707 ** >bst_call.m at 28 ** >gui_brainstorm.m>@(h,ev)bst_call(@ProcessRun_Callback) at 261 ** ** ** File: 150505/@raw19900812VTTN_01run4/data_0raw_19900812VTTN_01run4.mat ** *************************************************************************** [image: Inline images 2] On 5 November 2016 at 15:33, Francois Jean Tadel, Mr < francois.tadel at mcgill.ca> wrote: > Jeff, Marco: > > > Good timing, I've been working on similar topics this week. I added > processes in Brainstorm to use the forward models in FieldTrip, using > ft_volumesegment, ft_prepare_headmodel and ft_prepare_leadfield. For now it > is not possible to use the Brainstorm BEM surfaces to compute the leadfield > with FieldTrip, but if you think this is useful, we could probably add this. > > > Before the end of the month, I hope to have all the inverse models > available as well. We will have the possibility to call them either with > Brainstorm or FieldTrip forward solutions. > > > I you want to help me with the debugging, or if you want to start working > on the inverse/DICS part next week, it's all on github: > https://github.com/brainstorm-tools/brainstorm3/blob/master/ > toolbox/process/functions/process_ft_volumesegment.m > https://github.com/brainstorm-tools/brainstorm3/blob/master/ > toolbox/process/functions/process_ft_prepare_leadfield.m > > You could create a new process to call the FieldTrip function you want, > there are already many other examples of wrappers available: > process_ft_channelrepair.m > process_ft_dipolefitting.m > process_ft_scalpcurrentdensity.m > process_ft_timelockstatistics.m > process_ft_sourcestatistics.m > process_ft_freqstatistics.m > > If you need help with the plugin API in Brainstorm: > http://neuroimage.usc.edu/brainstorm/Tutorials/TutUserProcess > > Functions to convert Brainstorm files into FieldTrip structures: > brainstorm3/toolbox/io/out_fieldtrip_*.m > > Cheers, > Francois > > > ------------------------------ > > Hi Marco, > > Sorry to say I was not successful, yet. I have shifted to other tasks at > this point. I am using 256 channel EEG, and want to use the FreeSurfer > cortical ribbon as my source model (I would prefer source/headmodels to be > FEM as well). I found that a lot of FieldTrip is based on regular dipole > grids, and also it is more oriented to MEG than EEG. I do not have the > time right now to try to put all the pieces together in FieldTrip to do > what I want, but might get back to it in a month or two. > > Sorry I could not be of more help to you right now. > > -Jeff > > > > From: Marco Buiatti [mailto:marco.buiatti at gmail.com > ] > Sent: Friday, November 04, 2016 3:08 AM > To: FieldTrip discussion list; K Jeffrey Eriksen > Subject: Re: [FieldTrip] importing Brainstorm head and source models and > leftfields > > Dear all, > > I have Jeff's same question. I have anatomies ready in Brainstorm: > segmented data imported, co-registration with MEG sensor space, computation > of head model with overlapping spheres. Now I would like to import all this > into Fieldtrip for source reconstruction of oscillatory sources (DICS). > What's the best way to do this? > > And is there any difference between Brainstorm and Fieldtrip in these > steps that I should be aware of? > > Jeff, did you manage to solve the problem? > > Thanks a lot, > > Marco > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -- Marco Buiatti Neonatal Neurocognition Lab Center for Mind/Brain Sciences University of Trento, Piazza della Manifattura 1, 38068 Rovereto (TN), Italy E-mail: marco.buiatti at unitn.it Phone: +39 0464-808178 https://sites.google.com/a/unitn.it/marcobuiatti/ *********************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 53275 bytes Desc: not available URL: From mehdy.dousty at gmail.com Thu Nov 10 19:03:31 2016 From: mehdy.dousty at gmail.com (mehdy dousty) Date: Thu, 10 Nov 2016 18:03:31 +0000 Subject: [FieldTrip] Extracting time course of specific region in source level Message-ID: Hi all, I am working on source reconstruction of HCP MEG data by using eLORETA. The inverse problem is computed and the data are projected to the MNI by sourceinterpolate, so I would like to extract all the time courses which deal with specific ROI. I searched the mailing list and mostly they just provide information on the beamformer, so Id be grateful if anybody could possibly give me some hints about it. Thanks Mehdy -------------- next part -------------- An HTML attachment was scrubbed... URL: From amitjaiswal.elect at gmail.com Fri Nov 11 09:51:49 2016 From: amitjaiswal.elect at gmail.com (amit kumar Jaiswal) Date: Fri, 11 Nov 2016 10:51:49 +0200 Subject: [FieldTrip] checkinput_mex Error Message-ID: Hi everyone, I am using dipole fitting with .fif MEG data and while using ICP my Centos workstation is coming with an error: Undefined function '*checkinput_mex*' for input arguments of type 'cell' . I tried to get this function but couldn't. I checked that the same script is running fine on windows machine. What is the solution?? -- *Thanks & Regards......* *Amit Kumar Jaiswal* *Researcher @ChildBrain Project* *Elekta Oy, Helsinki (Finland)* *Call/Whatsapp: +358-405222805* *Important facts to be careful:* *** 800 million people (1 of every 9) in the world don't get clean & safe water. ** Save water daily and plant trees on every occasion.** **Save earth, Save lives.* -------------- next part -------------- An HTML attachment was scrubbed... URL: From deefje.meijer at gmail.com Mon Nov 14 12:35:05 2016 From: deefje.meijer at gmail.com (David Meijer) Date: Mon, 14 Nov 2016 11:35:05 +0000 Subject: [FieldTrip] PhD position, Neurocomputational Linguistics, Uni Bham / Google Research London Message-ID: On behalf of Prof Uta Noppeney. *PhD position in Neurocomputational Linguistics* *University of Birmingham in collaboration with Google Research London* Language comprehension is critical for effective interactions in our social world. In order to understand ‘who does what to whom’ in natural language processing, the brain needs to assign a syntactic structure to every sentence – a process coined ‘syntactic parsing’. This interdisciplinary project will combine expertise from human neuroscience (University of Birmingham) and computational linguistics (Google Research London) to determine the neural mechanisms underlying sentence comprehension in the human brain and advance parsing algorithms in machines. To study natural language processing and the underlying neural mechanisms in humans, we will measure eye movements, behavioural (psychophysics) and electrophysiological responses (EEG/fMRI) in participants reading natural sentences from syntactically annotated corpora. We will employ advanced machine learning algorithms to characterize the computational operations and neural mechanisms underlying syntactic processing in the human brain. Conversely, the insights obtained from human neuroimaging (EEG/fMRI) and eye tracking will provide critical constraints on the parameters and algorithms used in machine. The PhD position is designed to involve a 3 month internship at Google Research London. The Computational Cognitive Neuroimaging Group (Uta Noppeney) in collaboration with Google Research London (Bernd Bohnet, Ryan McDonald) is seeking an enthusiastic PhD candidate with strong analytical and quantitative abilities. Applicants should have a background in computational linguistics, neuroscience, computer science, psychology, physics or related areas. Prior experience in statistical analysis and/or machine learning would be an advantage. The Computational Cognitive Neuroimaging Lab is based at the Department of Psychology and the Computational Neuroscience and Cognitive Robotics Centre of the University of Birmingham, UK. The centre provides an excellent multidisciplinary, interactive and collaborative research environment combining expertise in cognitive neuroimaging, psychophysics and computational neuroscience. The psychology department was rated 5th in the UK research assessment exercise. http://www.birmingham.ac.uk/schools/psychology/research/labs/comp-cog-neuro/index.aspx http://www.birmingham.ac.uk/research/activity/cncr/index.aspx Applications will be considered until 8th January 2017. The starting date is Sept/Oct 2017. iCASE students must fulfil the MIBTP entry requirements and will join the MIBTP cohort for the taught modules and masterclasses during the first term. They will remain as an integral part of the MIBTP cohort and take part in the core networking activities and transferable skills training. For further information, please contact u.noppeney at bham.ac.uk. Check eligibility and apply here: https://www2.warwick.ac.uk/fac/cross_fac/mibtp/pgstudy/phd_opportunities/application/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From francois.tadel at mcgill.ca Tue Nov 15 21:33:42 2016 From: francois.tadel at mcgill.ca (=?UTF-8?Q?Fran=c3=a7ois_Tadel?=) Date: Tue, 15 Nov 2016 15:33:42 -0500 Subject: [FieldTrip] Importing Brainstorm head and source models and leftfields Message-ID: Hi Marco, > I have tested the Process > Sources > Fieldtrip: ft_prepare_leadfield on > different MEG data (always recorded from an Elekta 306 sensors system), and > I get an error "cannot work on balanced gradiometer definition" For some reason, ft_plot_sens doesn't want to get passed both the gradiometers and the magnetometers at once. Just run the process without the option "Display sensor/MRI registration" and you won't get this error any more. > I also have a basic question on the data to put in the process: my > understanding is that I should feed the process with the segmented anatomy, > that I identify with (for the default anatomy) the "Cortex_15002V" in the > anatomy tab. However, I cannot drag it to the Process window (I understand > that the sensor configuration is also needed). I therefore drag in the > Process window any data associated with the subject. Is this correct? Does > the program then automatically process the segmented anatomy? Yes, this is what you should do: select some recordings for the subject, and it will get the files it needs automatically from the database. Right now, this process does the following: - If the segmented masks produced previously with ft_volumesegment are available (called "mask_innerskull", "mask_outerskull" and "mask_scalp") it uses them. - Otherwise it uses the selected volume (displayed in green), passes it to ft_volumesegment for segmentation, then uses the segmented volumes without storing them anywhere. https://github.com/brainstorm-tools/brainstorm3/blob/master/toolbox/process/functions/process_ft_prepare_leadfield.m#L153 Cheers, Francois -- François Tadel, MSc MEG / McConnell Brain Imaging Center / MNI / McGill University 3801 rue University, Montreal, QC H3A2B4, Canada -------------- next part -------------- An HTML attachment was scrubbed... URL: From hbharadw at purdue.edu Tue Nov 15 22:06:51 2016 From: hbharadw at purdue.edu (Bharadwaj, Hari M) Date: Tue, 15 Nov 2016 21:06:51 +0000 Subject: [FieldTrip] PhD student openings in auditory neuroscience at Purdue University Message-ID: <1479244011788.63656@purdue.edu> [Apologies for cross posting] Two PhD student positions are available at the Systems Neuroscience of Auditory Perception Lab at Purdue University. We study the neural mechanisms of auditory perception in complex multi-source environments (e.g., crowded restaurants or busy streets) using a combination of human neuroimaging techniques (EEG, MRI, otoacoustic emissions), behavioral listening experiments, and computational modeling. In addition we are interested in the effects of overexposure to loud sounds and early aging on the auditory system, and how that affects perception. A notable capability of the lab is the ability to non-invasively measure, and to model physiological responses at different levels of the auditory pathway from the cochlea to the cortex. Please visit https://engineering.purdue.edu/SNAPLab for more information about the lab, the facilities, and the vibrant intellectual environment at Purdue with a large community of hearing researchers and neuroscientists. Applications can be submitted through the Weldon School of Biomedical Engineering, or the Department of Speech, Language, and Hearing Sciences as described here. Informal enquiries about the positions can be directed to Hari Bharadwaj (hbharadwaj at purdue.edu). Preference will be given to applications received by December 15, 2016. -- Hari M. Bharadwaj, Ph.D. Assistant Professor of Speech, Language, and Hearing Sciences Assistant Professor of Biomedical Engineering Lyles-Porter Hall Purdue University 715 Clinic Drive, Room 3162 West Lafayette, IN 47907 (765) 496-2249 hbharadwaj at purdue.edu http://engineering.purdue.edu/SNAPLab -------------- next part -------------- An HTML attachment was scrubbed... URL: From mailtome.2113 at gmail.com Wed Nov 16 05:09:12 2016 From: mailtome.2113 at gmail.com (Arti Abhishek) Date: Wed, 16 Nov 2016 15:09:12 +1100 Subject: [FieldTrip] Questions about time frequency analysis of EEG data Message-ID: Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Wed Nov 16 08:58:21 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Wed, 16 Nov 2016 07:58:21 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: References: Message-ID: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From Simon.VanEyndhoven at esat.kuleuven.be Wed Nov 16 14:07:22 2016 From: Simon.VanEyndhoven at esat.kuleuven.be (Simon Van Eyndhoven) Date: Wed, 16 Nov 2016 14:07:22 +0100 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp Message-ID: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Dear all, I recently started using FieldTrip in order to simulate pseudo-realistic EEG recordings from e.g. a set of dipoles located in the brain. Therefore, I tried to follow the tutorial on the construction of a BEM head model based on an anatomical MRI scan and an electrode set: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . The problem is that I don't succeed to reproduce the head model that is distributed as a template in the FieldTrip toolbox: '/template/headmodel/standard_bem.mat'. I start from the template MRI scan in FieldTrip ('standard_mri.mat') and use the following code (taken from the tutorial). --- % Load MRI data and electrode setup mri = importdata('standard_mri.mat'); elec = ft_read_sens('standard_1020.elc') % Segment the MRI volume cfg = []; cfg.output = {'brain','skull','scalp'}; segmentedmri = ft_volumesegment(cfg, mri); % Create a mesh cfg = []; cfg.tissue = {'brain','skull','scalp'}; cfg.numvertices = [1500 1000 500]; bnd = ft_prepare_mesh(cfg,segmentedmri); % Construct the headmodel cfg = []; cfg.conductivity = [0.3300 0.0041 0.3300]; cfg.method = 'dipoli'; vol = ft_prepare_headmodel(cfg, bnd); --- The following error is thrown: "Fatal error in dipoli: during computation of B-matrix; vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of /tmp/tp39371164882184732_2.tri" I've tried to create meshes with different numbers of vertices (e.g. the combination from the website tutorial 'numvertices = [3000 2000 1000]' produces the same error): for some combinations this problem does not occur, luckily. However, I wonder how the template model (that used the aforementioned number of vertices) got created successfully; I want to make sure that I work in the correct way and don't forget something. Can anyone shed some light on this issue, or hint what might be wrong/missing in the implementation above, compared to the template results that are provided in the FieldTrip distribution? Moreover, I've tried to create the head model using the 'bemcp' method as well. As a test, I placed a dipole close to the scalp surface, near a particular electrode. It is hence expected that the recorded amplitudes are highest for electrodes in the vicinity of this electrode. This effect is correctly reproduced when performing the computation of the forward problem using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' method). By contrast, the output of the forward problem using the bemcp-based head model shows a topographic distribution with many positive 'peaks' and negative 'troughs', spread kind of randomly over the scalp surface. Since neither the segmentation step nor meshing step changed (only the final step, namely the creation of the volume conduction model), I have no clue why this problem occurs. I've looked around in the discussion list's archives for possible causes: - it is reported that the bemcp method does not behave well if the dipole is located (very) close to the skull: I ruled this out for my problem by varying this distance - one user (Debora Desideri) reported a problem with the bemcp-method that appears to be very similar to mine, but there was no reply to her problem... - another user (John Richards) calls for caution when using the bemcp-method, stating that it behaves poorly sometimes Has anyone experienced similar issues when using the 'becmp' method and maybe found an explanation for this? Thanks in advance for any help! Best regards, -- Simon Van Eyndhoven PhD researcher Stadius Center for Dynamical Systems, Signal Processing and Data Analytics KU Leuven, Dept. Electrical Engineering (ESAT) email: simon.vaneyndhoven at esat.kuleuven.be From litvak.vladimir at gmail.com Wed Nov 16 16:14:08 2016 From: litvak.vladimir at gmail.com (Vladimir Litvak) Date: Wed, 16 Nov 2016 15:14:08 +0000 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Message-ID: Dear Simon, I tested this before and now re-tested again using the versions of FT and BEMCP which come with SPM and should be quite recent. For SPM cortical mesh where we take special measures to make sure that it's far enough from the boundary the correlation coefficients between BEMCP and 3-spheres model are all above 0.9. You say you varied the depth but have you varied it enough? What 'enough' is depends on the density of your head meshes. For SPM meshes it was more than 6mm from the boundary. The difference between different BEM methods is how close you can get to the boundary without breaking. BEMCP is the simplest method which is not very good in this respect. dipoli or OpenMEEG will also break but closer to the surface. Best, Vladimir On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven < Simon.VanEyndhoven at esat.kuleuven.be> wrote: > Dear all, > > I recently started using FieldTrip in order to simulate pseudo-realistic > EEG recordings from e.g. a set of dipoles located in the brain. > Therefore, I tried to follow the tutorial on the construction of a BEM > head model based on an anatomical MRI scan and an electrode set: > http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . > > The problem is that I don't succeed to reproduce the head model that is > distributed as a template in the FieldTrip toolbox: > '/template/headmodel/standard_bem.mat'. I start from the template MRI > scan in FieldTrip ('standard_mri.mat') and use the following code (taken > from the tutorial). > > --- > % Load MRI data and electrode setup > mri = importdata('standard_mri.mat'); > elec = ft_read_sens('standard_1020.elc') > > % Segment the MRI volume > cfg = []; > cfg.output = {'brain','skull','scalp'}; > segmentedmri = ft_volumesegment(cfg, mri); > > % Create a mesh > cfg = []; > cfg.tissue = {'brain','skull','scalp'}; > cfg.numvertices = [1500 1000 500]; > bnd = ft_prepare_mesh(cfg,segmentedmri); > > % Construct the headmodel > cfg = []; > cfg.conductivity = [0.3300 0.0041 0.3300]; > cfg.method = 'dipoli'; > vol = ft_prepare_headmodel(cfg, bnd); > --- > > The following error is thrown: > > "Fatal error in dipoli: during computation of B-matrix; > vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of > /tmp/tp39371164882184732_2.tri" > > I've tried to create meshes with different numbers of vertices (e.g. the > combination from the website tutorial 'numvertices = [3000 2000 1000]' > produces the same error): for some combinations this problem does not > occur, luckily. However, I wonder how the template model (that used the > aforementioned number of vertices) got created successfully; I want to make > sure that I work in the correct way and don't forget something. > > Can anyone shed some light on this issue, or hint what might be > wrong/missing in the implementation above, compared to the template results > that are provided in the FieldTrip distribution? > > Moreover, I've tried to create the head model using the 'bemcp' method as > well. As a test, I placed a dipole close to the scalp surface, near a > particular electrode. It is hence expected that the recorded amplitudes are > highest for electrodes in the vicinity of this electrode. This effect is > correctly reproduced when performing the computation of the forward problem > using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' > method). By contrast, the output of the forward problem using the > bemcp-based head model shows a topographic distribution with many positive > 'peaks' and negative 'troughs', spread kind of randomly over the scalp > surface. Since neither the segmentation step nor meshing step changed (only > the final step, namely the creation of the volume conduction model), I have > no clue why this problem occurs. I've looked around in the discussion > list's archives for possible causes: > - it is reported that the bemcp method does not behave well if the dipole > is located (very) close to the skull: I ruled this out for my problem by > varying this distance > - one user (Debora Desideri) reported a problem with the bemcp-method that > appears to be very similar to mine, but there was no reply to her problem... > - another user (John Richards) calls for caution when using the > bemcp-method, stating that it behaves poorly sometimes > > Has anyone experienced similar issues when using the 'becmp' method and > maybe found an explanation for this? > > Thanks in advance for any help! > > Best regards, > > -- > Simon Van Eyndhoven > > PhD researcher > Stadius Center for Dynamical Systems, Signal Processing and Data Analytics > KU Leuven, Dept. Electrical Engineering (ESAT) > > email: simon.vaneyndhoven at esat.kuleuven.be > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Simon.VanEyndhoven at esat.kuleuven.be Thu Nov 17 10:34:56 2016 From: Simon.VanEyndhoven at esat.kuleuven.be (Simon Van Eyndhoven) Date: Thu, 17 Nov 2016 10:34:56 +0100 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> Message-ID: <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> Hello Vladimir, Thank you for the swift response. I made sure to place the source at varying distances from the skull, to preclude any effect because of this. More precisely, I tried the following approach: -- elec = ft_read_sens('standard_1020.elc') chan_name = 'F8'; chan_index = find(strcmp(elec.label,chan_name)); chan_pos = elec.elecpos(chan_index,:); [...] cfg.dip.pos = 0.3*chan_pos; % I tried distances ranging from 0.3*chan_pos to 0.9*chan_pos -- For any distance from the skull the model yielded a nonsensical output as described earlier. This leads me to believe that this distance is not the determining factor here... Best regards, -- Simon Van Eyndhoven PhD researcher Stadius Center for Dynamical Systems, Signal Processing and Data Analytics KU Leuven, Dept. Electrical Engineering (ESAT) email: simon.vaneyndhoven at esat.kuleuven.be On 2016-11-16 16:14, Vladimir Litvak wrote: > Dear Simon, > > I tested this before and now re-tested again using the versions of FT and BEMCP which come with SPM and should be quite recent. For SPM cortical mesh where we take special measures to make sure that it's far enough from the boundary the correlation coefficients between BEMCP and 3-spheres model are all above 0.9. You say you varied the depth but have you varied it enough? What 'enough' is depends on the density of your head meshes. For SPM meshes it was more than 6mm from the boundary. The difference between different BEM methods is how close you can get to the boundary without breaking. BEMCP is the simplest method which is not very good in this respect. dipoli or OpenMEEG will also break but closer to the surface. > > Best, > > Vladimir > > On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven wrote: > >> Dear all, >> >> I recently started using FieldTrip in order to simulate pseudo-realistic EEG recordings from e.g. a set of dipoles located in the brain. >> Therefore, I tried to follow the tutorial on the construction of a BEM head model based on an anatomical MRI scan and an electrode set: http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem [1] . >> >> The problem is that I don't succeed to reproduce the head model that is distributed as a template in the FieldTrip toolbox: '/template/headmodel/standard_bem.mat'. I start from the template MRI scan in FieldTrip ('standard_mri.mat') and use the following code (taken from the tutorial). >> >> --- >> % Load MRI data and electrode setup >> mri = importdata('standard_mri.mat'); >> elec = ft_read_sens('standard_1020.elc') >> >> % Segment the MRI volume >> cfg = []; >> cfg.output = {'brain','skull','scalp'}; >> segmentedmri = ft_volumesegment(cfg, mri); >> >> % Create a mesh >> cfg = []; >> cfg.tissue = {'brain','skull','scalp'}; >> cfg.numvertices = [1500 1000 500]; >> bnd = ft_prepare_mesh(cfg,segmentedmri); >> >> % Construct the headmodel >> cfg = []; >> cfg.conductivity = [0.3300 0.0041 0.3300]; >> cfg.method = 'dipoli'; >> vol = ft_prepare_headmodel(cfg, bnd); >> --- >> >> The following error is thrown: >> >> "Fatal error in dipoli: during computation of B-matrix; >> vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of /tmp/tp39371164882184732_2.tri" >> >> I've tried to create meshes with different numbers of vertices (e.g. the combination from the website tutorial 'numvertices = [3000 2000 1000]' produces the same error): for some combinations this problem does not occur, luckily. However, I wonder how the template model (that used the aforementioned number of vertices) got created successfully; I want to make sure that I work in the correct way and don't forget something. >> >> Can anyone shed some light on this issue, or hint what might be wrong/missing in the implementation above, compared to the template results that are provided in the FieldTrip distribution? >> >> Moreover, I've tried to create the head model using the 'bemcp' method as well. As a test, I placed a dipole close to the scalp surface, near a particular electrode. It is hence expected that the recorded amplitudes are highest for electrodes in the vicinity of this electrode. This effect is correctly reproduced when performing the computation of the forward problem using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' method). By contrast, the output of the forward problem using the bemcp-based head model shows a topographic distribution with many positive 'peaks' and negative 'troughs', spread kind of randomly over the scalp surface. Since neither the segmentation step nor meshing step changed (only the final step, namely the creation of the volume conduction model), I have no clue why this problem occurs. I've looked around in the discussion list's archives for possible causes: >> - it is reported that the bemcp method does not behave well if the dipole is located (very) close to the skull: I ruled this out for my problem by varying this distance >> - one user (Debora Desideri) reported a problem with the bemcp-method that appears to be very similar to mine, but there was no reply to her problem... >> - another user (John Richards) calls for caution when using the bemcp-method, stating that it behaves poorly sometimes >> >> Has anyone experienced similar issues when using the 'becmp' method and maybe found an explanation for this? >> >> Thanks in advance for any help! >> >> Best regards, >> >> -- >> Simon Van Eyndhoven >> >> PhD researcher >> Stadius Center for Dynamical Systems, Signal Processing and Data Analytics >> KU Leuven, Dept. Electrical Engineering (ESAT) >> >> email: simon.vaneyndhoven at esat.kuleuven.be >> >> _______________________________________________ >> fieldtrip mailing list >> fieldtrip at donders.ru.nl >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip [2] > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip Links: ------ [1] http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem [2] https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From litvak.vladimir at gmail.com Thu Nov 17 11:05:15 2016 From: litvak.vladimir at gmail.com (Vladimir Litvak) Date: Thu, 17 Nov 2016 10:05:15 +0000 Subject: [FieldTrip] Unable to reproduce BEM headmodel - possible bug in bemcp In-Reply-To: <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> References: <17c28a6ca5a254e624498e0954a1032a@esat.kuleuven.be> <9f5e1aa2253505ad35ba56dbe08a6431@esat.kuleuven.be> Message-ID: Hi Simon, Then it might be something else. As I said, I checked yesterday in the latest SPM and it looks OK but there might be some code differences. We don't update our bemcp version every time we update the core FT. The approach Robert used and he might still have the script for it is to make a mesh for a single sphere and compare the bemcp solution for it with the analytical one. Vladimir On Thu, Nov 17, 2016 at 9:34 AM, Simon Van Eyndhoven < Simon.VanEyndhoven at esat.kuleuven.be> wrote: > Hello Vladimir, > > Thank you for the swift response. I made sure to place the source at > varying distances from the skull, to preclude any effect because of this. > More precisely, I tried the following approach: > > -- > elec = ft_read_sens('standard_1020.elc') > chan_name = 'F8'; > chan_index = find(strcmp(elec.label,chan_name)); > chan_pos = elec.elecpos(chan_index,:); > > [...] > > > cfg.dip.pos = 0.3*chan_pos; % I tried distances ranging from > 0.3*chan_pos to 0.9*chan_pos > -- > > For any distance from the skull the model yielded a nonsensical output as > described earlier. This leads me to believe that this distance is not the > determining factor here... > > Best regards, > > -- > Simon Van Eyndhoven > > PhD researcher > Stadius Center for Dynamical Systems, Signal Processing and Data Analytics > KU Leuven, Dept. Electrical Engineering (ESAT) > > email: simon.vaneyndhoven at esat.kuleuven.be > > On 2016-11-16 16:14, Vladimir Litvak wrote: > > Dear Simon, > > I tested this before and now re-tested again using the versions of FT and > BEMCP which come with SPM and should be quite recent. For SPM cortical mesh > where we take special measures to make sure that it's far enough from the > boundary the correlation coefficients between BEMCP and 3-spheres model are > all above 0.9. You say you varied the depth but have you varied it enough? > What 'enough' is depends on the density of your head meshes. For SPM meshes > it was more than 6mm from the boundary. The difference between different > BEM methods is how close you can get to the boundary without breaking. > BEMCP is the simplest method which is not very good in this respect. dipoli > or OpenMEEG will also break but closer to the surface. > > Best, > > Vladimir > > On Wed, Nov 16, 2016 at 1:07 PM, Simon Van Eyndhoven < > Simon.VanEyndhoven at esat.kuleuven.be> wrote: > >> Dear all, >> >> I recently started using FieldTrip in order to simulate pseudo-realistic >> EEG recordings from e.g. a set of dipoles located in the brain. >> Therefore, I tried to follow the tutorial on the construction of a BEM >> head model based on an anatomical MRI scan and an electrode set: >> http://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_bem . >> >> The problem is that I don't succeed to reproduce the head model that is >> distributed as a template in the FieldTrip toolbox: >> '/template/headmodel/standard_bem.mat'. I start from the template MRI >> scan in FieldTrip ('standard_mri.mat') and use the following code (taken >> from the tutorial). >> >> --- >> % Load MRI data and electrode setup >> mri = importdata('standard_mri.mat'); >> elec = ft_read_sens('standard_1020.elc') >> >> % Segment the MRI volume >> cfg = []; >> cfg.output = {'brain','skull','scalp'}; >> segmentedmri = ft_volumesegment(cfg, mri); >> >> % Create a mesh >> cfg = []; >> cfg.tissue = {'brain','skull','scalp'}; >> cfg.numvertices = [1500 1000 500]; >> bnd = ft_prepare_mesh(cfg,segmentedmri); >> >> % Construct the headmodel >> cfg = []; >> cfg.conductivity = [0.3300 0.0041 0.3300]; >> cfg.method = 'dipoli'; >> vol = ft_prepare_headmodel(cfg, bnd); >> --- >> >> The following error is thrown: >> >> "Fatal error in dipoli: during computation of B-matrix; >> vertex 916 of /tmp/tp39371164882184732_1.tri touches triangle 811 of >> /tmp/tp39371164882184732_2.tri" >> >> I've tried to create meshes with different numbers of vertices (e.g. the >> combination from the website tutorial 'numvertices = [3000 2000 1000]' >> produces the same error): for some combinations this problem does not >> occur, luckily. However, I wonder how the template model (that used the >> aforementioned number of vertices) got created successfully; I want to make >> sure that I work in the correct way and don't forget something. >> >> Can anyone shed some light on this issue, or hint what might be >> wrong/missing in the implementation above, compared to the template results >> that are provided in the FieldTrip distribution? >> >> Moreover, I've tried to create the head model using the 'bemcp' method as >> well. As a test, I placed a dipole close to the scalp surface, near a >> particular electrode. It is hence expected that the recorded amplitudes are >> highest for electrodes in the vicinity of this electrode. This effect is >> correctly reproduced when performing the computation of the forward problem >> using the 'standard_bem.mat' head model mentioned above (using the 'dipoli' >> method). By contrast, the output of the forward problem using the >> bemcp-based head model shows a topographic distribution with many positive >> 'peaks' and negative 'troughs', spread kind of randomly over the scalp >> surface. Since neither the segmentation step nor meshing step changed (only >> the final step, namely the creation of the volume conduction model), I have >> no clue why this problem occurs. I've looked around in the discussion >> list's archives for possible causes: >> - it is reported that the bemcp method does not behave well if the dipole >> is located (very) close to the skull: I ruled this out for my problem by >> varying this distance >> - one user (Debora Desideri) reported a problem with the bemcp-method >> that appears to be very similar to mine, but there was no reply to her >> problem... >> - another user (John Richards) calls for caution when using the >> bemcp-method, stating that it behaves poorly sometimes >> >> Has anyone experienced similar issues when using the 'becmp' method and >> maybe found an explanation for this? >> >> Thanks in advance for any help! >> >> Best regards, >> >> -- >> Simon Van Eyndhoven >> >> PhD researcher >> Stadius Center for Dynamical Systems, Signal Processing and Data Analytics >> KU Leuven, Dept. Electrical Engineering (ESAT) >> >> email: simon.vaneyndhoven at esat.kuleuven.be >> >> _______________________________________________ >> 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.augusti at unsw.edu.au Fri Nov 18 00:59:05 2016 From: f.augusti at unsw.edu.au (f.augusti at unsw.edu.au) Date: Thu, 17 Nov 2016 23:59:05 +0000 Subject: [FieldTrip] hdr format correction Message-ID: Hi, I have some problems with the correct format of the reader. My data has been recorded in poly32 format and read by tms_read function. My header contains the following fields, but for some reason it's not being read from ft_definetrials and no event type are recognised: data.hdr.filename= 2048 data.hdr.path=67 data.hdr.chantype= 66x1 cell data.hdr.chanunit=1x67 cell data.hdr.label=68x1 cell data.hdr.nTrials=1 data.hdr.nSamplesPre=0 data.hdr.nSamples =776220 data.hdr.nChans=67 data.hdr.Fs=2048 >>cfg = []; cfg.dataset = 'FAonNC16112016.mat'; cfg.trialdef.eventtype = '?'; ft_definetrial(cfg); evaluating trialfunction 'ft_trialfun_general' Error using ft_read_header (line 2248) unsupported header format "matlab" Error in ft_trialfun_general (line 78) hdr = ft_read_header(cfg.headerfile, 'headerformat', cfg.headerformat); Error in ft_definetrial (line 177) [trl, event] = feval(cfg.trialfun, cfg); -------------- next part -------------- An HTML attachment was scrubbed... URL: From cecile.issard at etu.parisdescartes.fr Fri Nov 18 15:42:26 2016 From: cecile.issard at etu.parisdescartes.fr (Cecile Issard) Date: Fri, 18 Nov 2016 14:42:26 +0000 Subject: [FieldTrip] samples present in multiple trials Message-ID: <83D31873-260A-4C54-B941-FDB4B63D3B6F@etu.parisdescartes.fr> Hello, I got this error message when I used ft_databrowser : Warning: samples present in multiple trials, using only the last occurence of each sample > In ft_fetch_data at 145 In ft_databrowser>redraw_cb at 1544 In ft_databrowser>keyboard_cb at 1311 When preprocessing, I used cfg.trialdef.prestim = 3; cfg.trialdef.poststim = 4; and cfg.padding=10; cfg.padtype=‘data’; If I understand well, 1.5 seconds of supplementary data are added before and after each trial before filtering, but removed after filtering. Do I get this error message because my trials overlap with padding ? If yes why is it still the case after the filters have been applied ? I intentionally programmed ISI of 3 to 4 s to avoid overlaps (without padding). Also how should I choose the duration of padding ? Best wishes, Cécile -- Cécile Issard PhD student Laboratoire Psychologie de la Perception - UMR8242 45 rue des Sts Pères 75270 Paris cedex 06 http://lpp.psycho.univ-paris5.fr/index.php 01.70.64.99.69 @CecileIcecile -------------- next part -------------- An HTML attachment was scrubbed... URL: From cwilling at bu.edu Fri Nov 18 17:10:04 2016 From: cwilling at bu.edu (Carly Rose Willing) Date: Fri, 18 Nov 2016 11:10:04 -0500 Subject: [FieldTrip] Units of ERPS Message-ID: <2561EEF9-7246-4342-B989-9FC6140094C5@bu.edu> Hello, When I was plotting an ERP using ft_singleplotER from some BVA EEG data, I was wondering what unit the y-axis should be in by default. I want the y-axis to be in terms of μV, and it is obvious from comparison with my BVA output that this is not just a matter of scale. Does anyone know how to code to change the scaling/units? Thank you in advance! Carly Rose Willing --- Carly Rose Willing Boston University 2018 Lab Assistant Laboratory of Visual Cognitive Neuroscience Department of Psychological and Brain Sciences -------------- next part -------------- An HTML attachment was scrubbed... URL: From cecile.issard at etu.parisdescartes.fr Fri Nov 18 22:15:28 2016 From: cecile.issard at etu.parisdescartes.fr (Cecile Issard) Date: Fri, 18 Nov 2016 21:15:28 +0000 Subject: [FieldTrip] add statistical dispersion on erp plots Message-ID: Hello, Is there a way to add a measure of statistical dispersion (standard error of the mean or standard deviation) around ERPs in ft_singleplotER or ft_multiplotER ? Best wishes, Cécile -- Cécile Issard PhD student Laboratoire Psychologie de la Perception - UMR8242 45 rue des Sts Pères 75270 Paris cedex 06 http://lpp.psycho.univ-paris5.fr/index.php 01.70.64.99.69 @CecileIcecile -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Mon Nov 21 13:17:48 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Mon, 21 Nov 2016 12:17:48 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Message-ID: Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From davide.tabarelli at unitn.it Tue Nov 22 10:08:04 2016 From: davide.tabarelli at unitn.it (Davide Tabarelli) Date: Tue, 22 Nov 2016 10:08:04 +0100 Subject: [FieldTrip] Elekta Neuromag SSP projectors Message-ID: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> Dear community, my name is Davide Tabarelli and I’m currently working in the MEG lab @ Center for Mind Brain Sciences (Trento). I’m writing to get some formation about SSP projectors saved in Elekta Neuromag fif files. I know how the MNE python pipeline works (loading projectors & application on request) but I didn’t find information on how Filedtrip deals with these SSP projectors when loading data. Are they automatically applied? Are they discarded? Are they stored somewhere? Thank you in advance for your help. Have a nice day. D. — Davide Tabarelli, Ph.D. Center for Mind Brain Sciences (CIMeC) University of Trento, Via delle Regole, 101 38123 Mattarello (TN) Italy From mona at sdsc.edu Wed Nov 23 23:52:27 2016 From: mona at sdsc.edu (Wong-Barnum, Mona) Date: Wed, 23 Nov 2016 22:52:27 +0000 Subject: [FieldTrip] shifting data time In-Reply-To: References: Message-ID: Hi Anne: Thank you so much for your thoughtful suggestion! I will give it a try. Yes, we would like to have all the time continuous because we need to make some analysis based on the entire duration. Happy Thanksgiving (if you are in the US)! cheers, Mona On Oct 28, 2016, at 1:36 AM, anne Hauswald > wrote: Hi Mona, if you do cfg=[]; all=ft_appenddata(cfg, data1, data2, data3, data4); you get among the other fields all= time: {1x4 cell} %assuming each dataset equals one trial, which is what you want to replace. you could try to create a matrix, based on your sampling rate with all the continuous time points, here just for illustration: all_timepoints=[0:0.025:9.975] %adapt to your sampling rate and length of data, be very sure the end of one file is the beginning of the next one. (This is giving you all points from 0 to 9.975 with steps of 0.025) trl_timepoints=mat2cell(all_timepoints, 1, [100 100 100 100]) %here I create 4 cell arrays, corresponding e.g. to 4 trials will result in trl_timepoints = [1x100 double] [1x100 double] [1x100 double] [1x100 double] with continuous time information (trial 1 starts at 0, trial 2 at 2.5, trials 3 at 5, and trial 4 at 7.5) of the same length. this you can then use to replace the original time information: all_data.time_old=all_data.time %if you want to keep the original time information all_data.time=trl_timepoints In general: are you really sure that you need the continuous time information? There are many processing and analyses steps that can be done on the appended time information… There might be more elegant ways to do this. Hope there is no typo and that it helps Best Anne ********************************************* Mona Wong Web & iPad Application Developer San Diego Supercomputer Center "To handle yourself, use your head; to handle others, use your heart." -- Eleanor Roosevelt ********************************************* -------------- next part -------------- An HTML attachment was scrubbed... URL: From stan.vanpelt at donders.ru.nl Thu Nov 24 11:12:22 2016 From: stan.vanpelt at donders.ru.nl (Pelt, S. van (Stan)) Date: Thu, 24 Nov 2016 10:12:22 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> Message-ID: <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> Hi Iris, I assume that you want to compare if spectra are different between these conditions. In that case, I would advise you to take a look at the Fieldtrip TFR-statistics tutorial, http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq. it explains how you can use cluster-based permutation to look for differences between spectra, within or between subjects. In your case, instead of comparing a baseline with an activation epoch (as described in the tutorial), you compare your (baseline-corrected) conditions A and B. Best, Stan From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Steinmann, Iris Sent: maandag 21 november 2016 13:18 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From iris.steinmann at med.uni-goettingen.de Thu Nov 24 17:02:26 2016 From: iris.steinmann at med.uni-goettingen.de (Steinmann, Iris) Date: Thu, 24 Nov 2016 16:02:26 +0000 Subject: [FieldTrip] Questions about time frequency analysis of EEG data In-Reply-To: <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> References: <7CCA2706D7A4DA45931A892DF3C2894C521813D0@exprd03.hosting.ru.nl> <7CCA2706D7A4DA45931A892DF3C2894C52186EDD@exprd03.hosting.ru.nl> Message-ID: Hi Stan, Thanks a lot for your answer! The permutation test you suggested is exactly what I have done. But it gives me “only” the time-frequency bins that show a significant difference between TFR(A) and TFR(B). To visualize which of the conditions has higher or lower activity in the significant time-frequency bins I want to show a subtraction plot (TFR(A) minus TFR(B)) So, I’m not sure if I understood your answer correctly but I’m still struggling with the same two questions: 1. Is there anything wrong with subtracting TFR’s after baseline correction? 2. Is it anyhow valid to perform a linear operation (subtraction) on squared data (TFR) Sorry for still bothering you with this topic, but if anyone has an easy explanation, I would really appreciate. Thanks! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Donnerstag, 24. November 2016 11:12 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Iris, I assume that you want to compare if spectra are different between these conditions. In that case, I would advise you to take a look at the Fieldtrip TFR-statistics tutorial, http://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq. it explains how you can use cluster-based permutation to look for differences between spectra, within or between subjects. In your case, instead of comparing a baseline with an activation epoch (as described in the tutorial), you compare your (baseline-corrected) conditions A and B. Best, Stan From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Steinmann, Iris Sent: maandag 21 november 2016 13:18 To: FieldTrip discussion list > Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Hi Stan, thanks for your answer. Since I’m working on a similar problem it also helped me a lot. Would be great if you can answer me two more questions about subtracting time-frequency spectra: 1. Before subtracting two spectra, I baseline corrected them (ft_frequbaseline, ‘relchange’) (to circumvent the problem Arti described below) cfg_bc = []; cfg_bc.baseline = [-2 -1.8]; cfg_bc.baselinetype = 'relchange'; cfg_bc.parameter = 'powspctrm'; A_bc = ft_freqbaseline(cfg_bc, A); B_bc = ft_freqbaseline(cfg_bc, B); cfg_m = []; cfg_m.operation = 'subtract'; cfg_m.parameter = 'powspctrm'; AB_diff = ft_math(cfg_m, A_bc, B_bc); Is there anything wrong doing it like this (except I’m not able to change the baseline conditions afterwards…) 2. Is it really viable to perform a linear operation (subtraction) on squared data (spectra). I mean, a lot of people are doing, so it should be somehow reliable…but I’m still wondering… Thanks in advance! Iris From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Pelt, S. van (Stan) Sent: Mittwoch, 16. November 2016 08:58 To: FieldTrip discussion list Subject: Re: [FieldTrip] Questions about time frequency analysis of EEG data Dear Arti, My guess is that is has to do with plotting relative change instead of e.g. absolute change. In the individual conditions, you plot relative change to their own baselines, which is in the order of 0.2 or so. Now when you subtract the 2 conditions, the difference in both baseline and activation epoch is probably small. So when you plot the tfr of this, normalized by the baseline wpoch values of this difference signal, it might blow up the relative change values. You might want to plot just the absolute values of the difference TFR, since it is the difference in the activation window that is most relevant to you (assuming that baseline values are not different between the conditions. Or you could contrast just the activation epochs (e.g., using the tfr of cond1 as ‘baseline’ for cond2), and plot relative change of that. I’ll illustrate it with some example values to show what I think goes ‘wrong’: Cond. 1: baseline: 4; activation 6; -> rel.change 0.5 (50% increase) Cond. 2: baseline 3.9; acitivation 6.3; -> rel change 0.6 (60% increase) Difference: baseline 0.1; activation 0.3 -> rel change 2.0 (200% increase) Best, Stan -- Stan van Pelt, PhD Donders Institute for Brain, Cognition and Behaviour Radboud University Montessorilaan 3, B.01.34 6525 HR Nijmegen, the Netherlands tel: +31 24 3616288 From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Arti Abhishek Sent: woensdag 16 november 2016 5:09 To: FieldTrip discussion list > Subject: [FieldTrip] Questions about time frequency analysis of EEG data Dear fieldtrip community, I am running time frequency analysis on my EEG data and when I subtract the response between conditions, the difference condition has much higher amplitude. Could someone suggest what is wrong here? Please find the image here: https://www.dropbox.com/s/kh5uzz8000fmh1b/tfr.pdf?dl=0 and here is my script cfg = []; cfg.trials = find(s01_ica_clean_artrel_reref.trialinfo==102); cfg.channel = 'EEG'; cfg.method = 'wavelet'; cfg.width = 7; cfg.output = 'pow'; cfg.foi = 2:2:40; cfg.toi = -0.4:0.02:0.5; s01_cond1 = ft_freqanalysis(cfg, s01_cond1_preprocess); % difference cfg=[]; cfg.parameter='powspctrm'; cfg.operation ='x1-x2'; s01_difference =ft_math(cfg, s01_cond1, s01_cond2); % plot cfg = []; cfg.baseline=[-0.4 -0.1]; cfg.zlim=[-0.5 0.5]; cfg.xlim=[-0.4 0.5]; cfg.baselinetype = 'relchange'; cfg.layout = lay; cfg.interactive = 'yes'; cfg.channel={'FZ'}; subplot (1,3,1) ft_singleplotTFR(cfg, s01_cond1); title('deviant'); subplot (1,3,2); ft_singleplotTFR(cfg, s01_cond2); title('standard'); subplot (1,3,3) ft_singleplotTFR(cfg, s01_difference); title('difference'); Thanks, Arti -------------- next part -------------- An HTML attachment was scrubbed... URL: From sherrykhan78 at gmail.com Fri Nov 25 06:39:42 2016 From: sherrykhan78 at gmail.com (Sheraz Khan) Date: Fri, 25 Nov 2016 00:39:42 -0500 Subject: [FieldTrip] Two Postdoctoral positions at the Manoach Lab @ MGH / Harvard Medical School Message-ID: On Behalf of Dr. Manoach: [Apologies for cross posting] Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Clinical/Cognitive Neuroscience Research Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with cognitive neuroscience, neuroimaging methodology including MEG and/or EEG and data analysis, and is interested in developing research questions and optimizing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. Requirements: PhD (or MD) in cognitive neuroscience, psychology or a related discipline and a strong research background are required. The ideal candidate will have a background in MEG and/or EEG methodology; research experience with clinical populations; experience in task design and analysis for cognitive experiments; fluency with statistical analysis packages. A background in computational neuroscience and/or signal processing (e.g., in Matlab) are beneficial PS. Here they are, distribute however you see fit. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach >>. Stipend levels are in line with experience and NIH. A two-year commitment is required. Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Signal Processing/Computational Neuroscience/Methodological Innovation Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with MEG/EEG methodology and data analysis, comfortable with methodological innovation, and is interested in optimizing and developing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. This is an excellent opportunity for scientists with a strong interest in clinical multimodal neuroimaging research. Requirements: PhD in neuroscience, engineering or a related field and a strong research background are required. Ideal candidates would have extensive experience in data analysis and/or an engineering background, be proficient in Matlab/Python and be interested in methods development. The following are beneficial: experience with MEG/EEG data analysis/methodology; background in cognitive neuroscience, experimental psychology and sleep; interest in clinical applications. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach >>. Stipend levels are in line with experience and NIH. A two-year commitment is required. ------------------------- Sheraz Khan, M.Eng, Ph.D. Instructor in Neurology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School McGovern Institute for Brain Research Massachusetts Institute of Technology Tel: +1 617-643-5634 Fax: +1 617-948-5966 Email: sheraz at nmr.mgh.harvard.edu sheraz at mit.edu Web: http://sheraz.mit.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From roycox.roycox at gmail.com Fri Nov 25 18:52:54 2016 From: roycox.roycox at gmail.com (Roy Cox) Date: Fri, 25 Nov 2016 12:52:54 -0500 Subject: [FieldTrip] 2 postdoctoral positions (Clinical/Cognitive Neuroscience Research & Signal Processing/Computational Neuroscience/Methodological Innovation) Message-ID: hello, On behalf of Dr. Dara Manoach I'm posting these two postdoctoral opportunities on the role of sleep in memory consolidation in healthy and clinical populations. Best, Roy ------------------------------------------------------------------------------------------------------ Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Clinical/Cognitive Neuroscience Research Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with cognitive neuroscience, neuroimaging methodology including MEG and/or EEG and data analysis, and is interested in developing research questions and optimizing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. Requirements: PhD (or MD) in cognitive neuroscience, psychology or a related discipline and a strong research background are required. The ideal candidate will have a background in MEG and/or EEG methodology; research experience with clinical populations; experience in task design and analysis for cognitive experiments; fluency with statistical analysis packages. A background in computational neuroscience and/or signal processing (e.g., in Matlab) are beneficial PS. Here they are, distribute however you see fit. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach . Stipend levels are in line with experience and NIH. A two-year commitment is required. ----------------------------------------------------------------------------------------------- Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA and Harvard Medical School Position Description: Signal Processing/Computational Neuroscience/Methodological Innovation Project: Multimodal neuroimaging studies of sleep and memory PI: Dara S. Manoach, Ph.D. The position will involve investigating the role of sleep in memory consolidation, how these processes go awry in schizophrenia and autism, and the effects of pharmacological and other interventions. Our work has linked cognitive deficits to specific heritable mechanisms (sleep spindles and other sleep oscillations) and we are seeking effective interventions. In collaboration with Dr. Robert Stickgold’s lab at Beth Israel Deaconess Medical Center, we are extending and expanding this basic and clinical research program using state-of-the art tools including high density EEG, MEG, DTI, functional connectivity MRI, fMRI, and behavioral studies. We are seeking someone to participate in these foundation and NIMH-funded investigations who is familiar with MEG/EEG methodology and data analysis, comfortable with methodological innovation, and is interested in optimizing and developing analysis streams tailored to the study aims and populations. New approaches and ideas are encouraged, as are independent projects that dovetail with current studies. The position requires working closely with the PI, as well as with Dr. Stickgold, other Martinos Center investigators, particularly Dr. Matti Hamalainen, Director of the MEG Core Lab, and lab mates to design studies, acquire data, and develop, explore, improve and apply data analytic techniques. Training in clinical research and in the acquisition, analysis, and interpretation of neuroimaging data will be provided. This is an excellent opportunity for scientists with a strong interest in clinical multimodal neuroimaging research. Requirements: PhD in neuroscience, engineering or a related field and a strong research background are required. Ideal candidates would have extensive experience in data analysis and/or an engineering background, be proficient in Matlab/Python and be interested in methods development. The following are beneficial: experience with MEG/EEG data analysis/methodology; background in cognitive neuroscience, experimental psychology and sleep; interest in clinical applications. Position available immediately. Interested applicants should email: (a) CV, (b) statement of post-doctoral and career goals, (c) writing sample (e.g., a published manuscript), and (d) letters and/or contact information for three references to Dara Manoach . Stipend levels are in line with experience and NIH. A two-year commitment is required. -------------- next part -------------- An HTML attachment was scrubbed... URL: From weiyong.w.xu at jyu.fi Mon Nov 28 15:21:31 2016 From: weiyong.w.xu at jyu.fi (Xu, Weiyong) Date: Mon, 28 Nov 2016 14:21:31 +0000 Subject: [FieldTrip] MNE cortical sheet parcellation with AAL atlas Message-ID: Dear all, After MNE source analysis with the template MRI(Colin27), I want to do parcellation with the AAL atlas. So first I checked how well the template cortical sheet and AAL atlas fit with the following code: -------------------------------------------------------------- aal = ft_read_atlas('C:\MyTemp\Toolbox\fieldtrip\fieldtrip_git\template\atlas\aal\ROI_MNI_V4.nii'); mne_sourcemodel=ft_read_headshape('cortex_8196.surf.gii'); cfg = []; cfg.interpmethod = 'nearest'; cfg.parameter = 'tissue'; mne_sourcemodel_with_label = ft_sourceinterpolate(cfg, aal, mne_sourcemodel); disp(mne_sourcemodel_with_label.tissuelabel') for i=1:length(mne_sourcemodel_with_label.tissue) if mne_sourcemodel_with_label.tissue(i)==0; mne_sourcemodel_with_label.tissue(i)=200; end; end; ft_plot_mesh(mne_sourcemodel_with_label,'vertexcolor',mne_sourcemodel_with_label.tissue,'edgecolor','black') colorbar --------------------------------------------------------- The result looks like that the parcellation of the sulci are not very good. Also parts of the cerebellum are included after interpolation. So I want to ask if there are surface-based atlas available (preferably also based on the Colin27)? And also I noticed the freesurfer pipeline creates cortical parcellation such as the Destrieux atlas, could I somehow utilize this in creating surface-based atlas for my MNE source model? Thanks in advance. Best, Weiyong Xu Ph.D. student Department of Psychology University of Jyväskylä -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: parcellation_with_AAL.pdf Type: application/pdf Size: 454680 bytes Desc: parcellation_with_AAL.pdf URL: From t.schneider.uke at icloud.com Mon Nov 28 16:11:26 2016 From: t.schneider.uke at icloud.com (Till Schneider) Date: Mon, 28 Nov 2016 16:11:26 +0100 Subject: [FieldTrip] PhD position in Cognitive Neuroscience Message-ID: Dear Fieldtrip community, please find attached a job offer for a PhD position in Cognitive Neuroscience in Hamburg, Germany. Best regards, Till Schneider — Dr. Till Schneider Cognitive and Clinical Neurophysiology Group Dept. of Neurophysiology and Pathophysiology University Medical Center Hamburg-Eppendorf Martinistr. 52 20246 Hamburg Germany phone +49-40-7410-53188 fax +49-40-7410-57126 www.uke.de/neurophysiology t.schneider at uke.de -------------- next part -------------- A non-text attachment was scrubbed... Name: Job offer Doctoral Student SPP1665.pdf Type: application/pdf Size: 74606 bytes Desc: not available URL: From christine.blume at sbg.ac.at Mon Nov 28 16:46:08 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 28 Nov 2016 15:46:08 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Message-ID: Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 28 17:03:31 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 28 Nov 2016 16:03:31 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment In-Reply-To: References: Message-ID: Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From guiraudh at gmail.com Mon Nov 28 18:42:58 2016 From: guiraudh at gmail.com (=?UTF-8?B?SMOpbMOobmUgR3VpcmF1ZA==?=) Date: Mon, 28 Nov 2016 18:42:58 +0100 Subject: [FieldTrip] Coherence values corresponding to the statistically significant area Message-ID: Dear Fieldtrip community, I'm working on coherence measures between MEG signal and auditory signal perceived during MEG recording. I realized sources analysis and statistics analysis with cluster-based permutation test (Montecarlo method). However I would like to have the coherence values corresponding to my statistically significant area, and I can not get it. Is it possible? I thank you in advance. Best, Hélène -------------- next part -------------- An HTML attachment was scrubbed... URL: From nancygao260 at hotmail.com Tue Nov 29 04:36:10 2016 From: nancygao260 at hotmail.com (gao nuo) Date: Tue, 29 Nov 2016 03:36:10 +0000 Subject: [FieldTrip] problems in emotiv fieldtrip and matlab Message-ID: Dear Sir: I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. What I did is : 1. my matlab is matlab R2014a, 2,download the fieldtrip 20161108; 3. add all the files to the matlab path; 4. installed MinGW and set the path of the /bin in environmental variables; 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. 6. run buffer.exe. 7 . run cmd.exe; 8. go to fieldtrip-20161108/realtime/bin/win32; 9. input the command line: emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] the response is: passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port 10. run viewer.exe, push connect botton. but no response. I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. thanks for your reading and look forward to the suggestions. best wishes. Gao Nuo -------------- next part -------------- An HTML attachment was scrubbed... URL: From nancygao260 at hotmail.com Tue Nov 29 04:45:15 2016 From: nancygao260 at hotmail.com (gao nuo) Date: Tue, 29 Nov 2016 03:45:15 +0000 Subject: [FieldTrip] Fw: problems in emotiv fieldtrip and matlab In-Reply-To: References: Message-ID: ________________________________ From: fieldtrip-bounces at science.ru.nl on behalf of gao nuo Sent: Monday, November 28, 2016 6:36 PM To: fieldtrip at science.ru.nl Subject: [FieldTrip] problems in emotiv fieldtrip and matlab Dear Sir: I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. What I did is : 1. my matlab is matlab R2014a, 2,download the fieldtrip 20161108; 3. add all the files to the matlab path; 4. installed MinGW and set the path of the /bin in environmental variables; 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. 6. run buffer.exe. 7 . run cmd.exe; 8. go to fieldtrip-20161108/realtime/bin/win32; 9. input the command line: emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] the response is: passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port 10. run viewer.exe, push connect botton. but no response. I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. thanks for your reading and look forward to the suggestions. best wishes. Gao Nuo -------------- next part -------------- An HTML attachment was scrubbed... URL: From r.oostenveld at donders.ru.nl Tue Nov 29 08:50:00 2016 From: r.oostenveld at donders.ru.nl (Robert Oostenveld) Date: Tue, 29 Nov 2016 08:50:00 +0100 Subject: [FieldTrip] problems in emotiv fieldtrip and matlab In-Reply-To: References: Message-ID: <2182D0F4-2698-4C33-AF99-66DB9C79D074@donders.ru.nl> Dear Gao, it seems to me that in your step 9 the emotiv2ft did not actually start properly. It prints a help message out on screen, which I think it would only do if it did could not make sense of the command line options. If you specified verbatim "emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]]” then it would indeed not work. You should specify the right options in the brackets. The square [] brackets are optional, the <> brackets are required. So you should at least specify the configuration file. The defaults for the last three options (localhost, 1972 and 8000) should be ok, since you started the buffer in step 6 on the localhost with the default port (which is 1972). best regards, Robert > On 29 Nov 2016, at 04:36, gao nuo wrote: > > Dear Sir: > I want to import Emotiv headset data to matlab. I found that Fieldtrip can do this. But I failed. > What I did is : > 1. my matlab is matlab R2014a, > 2,download the fieldtrip 20161108; > 3. add all the files to the matlab path; > 4. installed MinGW and set the path of the /bin in environmental variables; > 5, turn on the emotiv headset, in the emotiv testbench, I can see the EEG signals. > 6. run buffer.exe. > 7 . run cmd.exe; > 8. go to fieldtrip-20161108/realtime/bin/win32; > 9. input the command line: > emotiv2ft [hostname=localhost [port=1972 [ctrlPort=8000]]] > the response is: > passing hostname by a minus (-) tells the software to spawn its own buffer server on the given port > > 10. run viewer.exe, push connect botton. but no response. > > I don't know what the problems is, and how can I connect the emotiv headset with the fieldtrip buffer. > > thanks for your reading and look forward to the suggestions. > > best wishes. > Gao Nuo > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From gaetan.sanchez at sbg.ac.at Tue Nov 29 10:13:24 2016 From: gaetan.sanchez at sbg.ac.at (Gaetan) Date: Tue, 29 Nov 2016 10:13:24 +0100 Subject: [FieldTrip] Salzburg Mind Brain Annual (SAMBA) Meeting 2017 - Announcement Message-ID: <7e7b674d-ee40-d29d-9e4f-a3b8128971e7@sbg.ac.at> Dear all, apologies in advance if you should receive this mail multiple times due to cross-posting on different lists. On behalf also of my colleagues and our advisory board, I am happy to announce the /*Salzburg Mind Brain Annual Meeting*/ (SAMBA) which will take place on the 13.-14. July 2017. Confirmed speakers for the upcoming event are: • Ole Jensen (Birmingham) • Catherine Tallon-Baudry (Paris) • Pascal Fries (Frankfurt) • Tobias Donner (Hamburg) • Angelika Lingnau (London) • Sylvain Baillet (Montreal) • Rosalyn Moran (Bristol) • Jan Mathijs Schoffelen (Nijmegen) • Christian-G. Bénar (Marseille) The workshop will be rather small (~100 participants) to enable lots of occasion for interactions. You will have the possibility to present a poster (please indicate while registering, with -at least- a tentative title. *The participation for SAMBA2017 is free*. For more information see the workshop website: https://samba.ccns.sbg.ac.at Also, prior to the workshop (11. & 12.07) there will be a Fieldtrip workshop help by Robert Oostenveld and Jan Mathijs Schoffelen. There are ~20 places for this event. *The participation for the Fieldtrip workshop is for free*. Registration is on the same site as above. So if you play it smart you can be part of 2 neuroscience highlights in 2017! Best, Nathan --------------------------------------------- Nathan Weisz Centre for Cognitive Neuroscience Division of Physiological Psychology University of Salzburg nathan.weisz at sbg.ac.at www.oboblab.at -- Gaëtan Sanchez, PhD Centre for Cognitive Neuroscience Hellbrunnerstraße 34, 5020 Salzburg - Austria Tel: +43 662 804 451 61 -------------- next part -------------- An HTML attachment was scrubbed... URL: From skelly2 at ccny.cuny.edu Tue Nov 29 12:48:25 2016 From: skelly2 at ccny.cuny.edu (Simon Kelly) Date: Tue, 29 Nov 2016 11:48:25 +0000 Subject: [FieldTrip] Postdoc opening in perceptual/cognitive neuroscience Message-ID: Applications are invited for a postdoctoral research post in the Cognitive Neural systems lab (https://cogneusys.com/) led by Simon Kelly, to study computational and neural mechanisms of value-biased sensorimotor decision making under time pressure. This position is part of a project funded by Science Foundation Ireland, which involves a combination of psychophysics, computational modelling, non-invasive electrophysiology of human brain and muscle, and analyses of existing single-cell neurophysiological data. Though involvement is expected in all of these aspects, the most critical role of the postdoctoral researcher will be in computational modelling. Candidates must thus have strong analytic and programming skills, and specific experience in the computational modelling of cognitive processes. Candidates must also be highly motivated and have excellent communication skills. Dr. Kelly's electrophysiology/psychophysics lab is situated within the School of Electrical and Electronic Engineering in University College Dublin, Ireland, and has strong collaborative links to cognitive and clinical neuroscience research groups both locally (e.g. Trinity College Institute of Neuroscience) and internationally (e.g. City College and Columbia University in New York). The successful applicant will have ample opportunities for wider collaborations and the learning of new skills. Interested candidates should submit a brief research statement and CV including publications through the UCD job vacancies site (http://www.ucd.ie/hr/jobvacancies/ - search for keyword 'kelly', or job ref 008870). Informal enquiries can be directed to Simon (simon.kelly at ucd.edu). Candidates should explain in their statement how their own research interests fit with those of the Kelly lab. The deadline for submitting applications is Jan 8th 2017, and shortlisting and interviews will take place shortly after that. ----------------------------------------------------------- Simon Kelly, Ph.D. Associate Professor School of Electrical and Electronic Engineering University College Dublin t: +353 (1) 716 1803 e: simon.kelly at ucd.ie -----------------------------------------------------------​ -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Wed Nov 30 09:57:21 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 30 Nov 2016 08:57:21 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Message-ID: Maybe an example will stimulate some discussion ;-). I test an interaction among two factors with two levels each with regard to the ERPs elicited by each stimulus. Let's say we present two stimuli (house vs. face) in colour (black/white vs. colour). The interaction is significant and we want to know which factor levels differ using post hoc tests using the following cfg. cfg = []; cfg.latency = [0 1]; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_depsamplesT'; cfg.correctm = 'cluster'; cfg.clusteralpha = 0.05; cfg.clusterstatistic = 'maxsum'; cfg.minnbchan = 2; cfg.tail = 0; cfg.clustertail = 0; cfg.alpha = 0.025; cfg.numrandomization = 1000; cfg_neighb.method = 'distance'; cfg.neighbours = ft_prepare_neighbours(cfg_neighb, data_sample); cfg.design = design; cfg.ivar = 2; cfg.uvar = 1; What we get is the following. stat_erp.FACE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.FACE_BW); stat_erp.FACE_COLOURvsFACE_BW.posclusters.prob % 18 clusters, p > 0.4076 stat_erp.FACE_COLOURvsFACE_BW.negclusters.prob % 12 clusters, p > 0.2957 stat_erp.FACE_COLOURvsHOUSE_COLOUR = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_COLOUR); stat_erp.FACE_COLOURvsHOUSE_COLOUR.posclusters.prob % 10 clusters, p > 0.4326 stat_erp.FACE_COLOURvsHOUSE_COLOUR.negclusters.prob % 12 clusters, p = 0.0599; p > 0.8631 stat_erp.FACE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_COLOURvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0360, p > 0.7742 stat_erp.FACE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0300, p > 0.5105 stat_erp.HOUSE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.FACE_BW); stat_erp.HOUSE_COLOURvsFACE_BW.posclusters.prob % 16 clusters, p > 0.2697 stat_erp.HOUSE_COLOURvsFACE_BW.negclusters.prob % 11 clusters, p = 0.0090, p > .0.9291 stat_erp.HOUSE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.HOUSE_COLOURvsHOUSE_BW.posclusters.prob % 19 clusters, p = 0.0020, p = 0.0979, p > 0.2468 stat_erp.HOUSE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0480, p = 0.0699, p > 0.3606 stat_erp.FACE_BWvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_BWvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0410, p > 0.7732 stat_erp.FACE_BWvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0320, p > 0.4725 Now the question is: how do we correct for these 6 follow-up tests? If we say we want to use the Bonferroni-Holm correction for example, we have to sort p-values according to size. However, we have two p-values per test, one for positive and one for negative clusters and Bonferroni-Holm assumes we only have one per test. Any ideas or suggestions how to best handle this? Best, Christine Von: Blume Christine Gesendet: Montag, 28. November 2016 17:04 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: AW: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 - 8044 5146 www.sleepscience.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Wed Nov 30 10:25:02 2016 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 30 Nov 2016 09:25:02 +0000 Subject: [FieldTrip] File Size depends on No. of Trials? Message-ID: Dear Community, I have an issue with data size. I am processing high-density EEG data from a sleep study. Following preprocessing I perform time-frequency transformations and a baseline correction using the following code: cfg = []; cfg.method = 'wavelet'; cfg.output = 'pow'; cfg.keeptrials = 'no'; cfg.width = 3; % cfg.foi = 1:1:16; % cfg.toi = -0.7:0.2:0.9; freqanalysis_FV = ft_freqanalysis(cfg, data_FV); cfg = []; cfg.baseline = [-0.6, 0]; cfg.baselinetype = 'relchange'; ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) As you can see, I do not keep the trials. Still, the size of ERDERS seems to depend on the size of data_FV. Why is that the case? Also, changing cfg.foi does not change the file size - why? The problem is that this way the data can hardly be handled as it takes up so much RAM. Please note that I do clear everything from the workspace I do not need, that should not be the issue. Thanks a lot for your thoughts. Best, Christine -------------- next part -------------- An HTML attachment was scrubbed... URL: From julian.keil at gmail.com Wed Nov 30 10:31:37 2016 From: julian.keil at gmail.com (Julian Keil) Date: Wed, 30 Nov 2016 10:31:37 +0100 Subject: [FieldTrip] File Size depends on No. of Trials? In-Reply-To: References: Message-ID: Hi Christine, take a look into the cfg.previous of ERDERS. Fieldtrip stores information on previous data analysis steps there. Maybe the trial information is hidden somewhere in there. The MatLab command rmfield can then be used to remove some unwanted fields from a structure. But please beware! There is a good reason for the thorough bookkeeping in Fieldtrip (which has saved me quite often). Good luck, Julian On Wed, Nov 30, 2016 at 10:25 AM, Blume Christine wrote: > Dear Community, > > > > I have an issue with data size. I am processing high-density EEG data from > a sleep study. Following preprocessing I perform time-frequency > transformations and a baseline correction using the following code: > > > > cfg = []; > > cfg.method = 'wavelet'; > > cfg.output = 'pow'; > > cfg.keeptrials = 'no'; > > cfg.width = 3; % > > cfg.foi = 1:1:16; % > > cfg.toi = -0.7:0.2:0.9; > > freqanalysis_FV = ft_freqanalysis(cfg, data_FV); > > > > cfg = []; > > cfg.baseline = [-0.6, 0]; > > cfg.baselinetype = 'relchange'; > > ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) > > > > As you can see, I do *not* keep the trials. Still, the size of ERDERS > seems to depend on the size of data_FV. Why is that the case? Also, > changing cfg.foi does not change the file size – why? The problem is that > this way the data can hardly be handled as it takes up so much RAM. Please > note that I do clear everything from the workspace I do not need, that > should not be the issue. > > > > Thanks a lot for your thoughts. > > > > Best, > > Christine > > > > _______________________________________________ > fieldtrip mailing list > fieldtrip at donders.ru.nl > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Claudio.Georgii at stud.sbg.ac.at Wed Nov 30 11:50:46 2016 From: Claudio.Georgii at stud.sbg.ac.at (Claudio Georgii) Date: Wed, 30 Nov 2016 11:50:46 +0100 Subject: [FieldTrip] File Size depends on No. of Trials? In-Reply-To: References: Message-ID: Hi Christine, In addition you could change the precision from double to single (which reduces the working memory needed by a half). As far as i know, double precision is not needed here and single should do fine, but please correct me if I am wrong. Claudio 2016-11-30 10:31 GMT+01:00 Julian Keil : > Hi Christine, > > take a look into the cfg.previous of ERDERS. Fieldtrip stores information > on previous data analysis steps there. Maybe the trial information is > hidden somewhere in there. > The MatLab command rmfield can then be used to remove some unwanted fields > from a structure. But please beware! There is a good reason for the > thorough bookkeeping in Fieldtrip (which has saved me quite often). > Good luck, > > Julian > > On Wed, Nov 30, 2016 at 10:25 AM, Blume Christine < > christine.blume at sbg.ac.at> wrote: > >> Dear Community, >> >> >> >> I have an issue with data size. I am processing high-density EEG data >> from a sleep study. Following preprocessing I perform time-frequency >> transformations and a baseline correction using the following code: >> >> >> >> cfg = []; >> >> cfg.method = 'wavelet'; >> >> cfg.output = 'pow'; >> >> cfg.keeptrials = 'no'; >> >> cfg.width = 3; % >> >> cfg.foi = 1:1:16; % >> >> cfg.toi = -0.7:0.2:0.9; >> >> freqanalysis_FV = ft_freqanalysis(cfg, data_FV); >> >> >> >> cfg = []; >> >> cfg.baseline = [-0.6, 0]; >> >> cfg.baselinetype = 'relchange'; >> >> ERDERS_FV = ft_freqbaseline(cfg, freqanalysis_FV) >> >> >> >> As you can see, I do *not* keep the trials. Still, the size of ERDERS >> seems to depend on the size of data_FV. Why is that the case? Also, >> changing cfg.foi does not change the file size – why? The problem is that >> this way the data can hardly be handled as it takes up so much RAM. Please >> note that I do clear everything from the workspace I do not need, that >> should not be the issue. >> >> >> >> Thanks a lot for your thoughts. >> >> >> >> Best, >> >> Christine >> >> >> >> _______________________________________________ >> 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 > -- Claudio Georgii, MSc. Phd student University of Salzburg - Department of Psychology Eating Behavior Laboratory Hellbrunnerstraße 34 5020 Salzburg - Austria Phone: 0043- (0)662 8044 5164 E-Mail: claudio.georgii at sbg.ac.at -------------- next part -------------- An HTML attachment was scrubbed... URL: From christophe.grova at mcgill.ca Wed Nov 30 15:33:47 2016 From: christophe.grova at mcgill.ca (Christophe Grova) Date: Wed, 30 Nov 2016 14:33:47 +0000 Subject: [FieldTrip] Postdoctoral position for a neurologist/epileptologist available in the Multimodal Functional Imaging Lab, Montreal (Montreal Neurological Inst. McGill U. and PERFORM Concordia U.) In-Reply-To: References: , Message-ID: Dear all, please see below the opportunity for a postdoctoral position in my lab. The candidate will join a multidisciplinary team composed of neurologists and methodologists within the Multimodal Functional Imaging Laboratory, directed by Pr. Christophe Grova. The laboratory is actually based on two sites: (i) Physics Dpt at Concordia University and PERFORM center, (ii) Biomedical Engineering Dpt and epilepsy group of the Montreal Neurological Institute, McGill University. Both environments offer unique platforms with access to several modalities (simultaneous high-density EEG/fMRI, MEG, simultaneous EEG/NIRS, TMS). The main expertise of the team is the development and the validation of source localization methods dedicated for EEG, MEG and NIRS as well as multimodal characterization of resting state brain activity. Project: Multimodal investigation of epileptic activity using simultaneous EEG/MEG and EEG/NIRS acquisitions. The proposed project aims at localizing and characterizing the generators of epileptic activity using simultaneous acquisitions of ElectroEncephaloGraphy (EEG) with Magneto-EncephaloGraphy (MEG), as well as simultaneous acquisitions of EEG with Near Infra-Red Spectroscopy (NIRS). EEG and MEG are respectively measuring on the scalp electric and magnetic fields generated by neuronal activity at a millisecond scale, providing a detailed description of brain bioelectrical activity. Combined with EEG measuring brain electric activity on the scalp, NIRS allows studying hemodynamic processes at the time of spontaneous epileptic activity. The specificity of NIRS data is its ability to measure local changes oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), exploiting absorption properties of infrared light within brain tissue using optic fibers placed on the surface of the head (temporal resolution: 10 ms, 16 sources x 32 detectors, penetration: 2-3 cm from the surface of the head). While methodological developpments in the lab will consist in 3D reconstruction of the generators of EEG, MEG and NIRS signals and assessing multimodal concordances between bioelectrical neuronal signals and hemodynamic processes, the purpose of this Postdoctoral project will be to assess the integrity of neurovascular coupling processes at the time of epileptic discharges, using a unique multimodal environment involving EEG/MEG (Pellegrino et al HBM 2016), EEG/NIRS (Pellegrino et al Frontiers in Neurosc. 2016) and also EEG/fMRI recordings (Heers et al HBM 2014). Close collaborations with the epilepsy group of the Montreal Neurological Institute, involving notably Dr E. Kobayashi MD-PhD, Dr F. Dubeau MD-PhD and Dr. J. Gotman PhD, will provide access to patient populations and additional clinical expertise for this project. Requirements: The candidate should be an MD (neurologist) with previous training in epileptology and neurophysiology (EEG). Expertise in analyzing MEG or NIRS signals and/or computational skills including neuroimaging softwares are appreciated additional qualification. The candidate should be fluent in English (and if possible French) due to the patient population studied. Supervisor: Christophe Grova Ph.D. Assistant Professor, Physics Dpt and PERFORM, Concordia Univ. Chair of PERFORM Applied Bio-Imaging Committee Adjunct Professor, Biomedical Engineering and Neurology & Neurosurgery dpts, McGill Univ. Member Epilepsy Group, Montreal Neurological Institute Director of the Multimodal Functional Imaging Laboratory Email: christophe.grova at concordia.ca christophe.grova at mcgill.ca Please send your CV and motivation letter before Dec 15th 2016 to christophe.grova at concordia.ca *************************** Christophe Grova, PhD Assistant Professor, Physics Dpt, Concordia University PERFORM centre, Concordia University Chair of PERFORM Applied Bio-Imaging Committee (ABC) Adjunct Prof in Biomedical Engineering, and Neurology and Neurosurgery Dpt, McGill University Multimodal Functional Imaging Lab (Multi FunkIm) Montreal Neurological Institute - epilepsy group Centre de Recherches en Mathématiques Physics Dpt Concordia University - Loyola Campus - Office SP 365.12 7141 Sherbrooke Street West, Montreal, QC H4B 1R6 Phone: (514) 848-2424 ext.4221 email : christophe.grova at concordia.ca , christophe.grova at mcgill.ca Explore Concordia: http://explore.concordia.ca/christophe-grova Physics, Concordia University: http://www.concordia.ca/artsci/physics/faculty.html?fpid=christophe-grova McGill University: http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/PeopleChristophe MultiFunkIm Lab: http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/HomePage *************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 30 15:57:21 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 30 Nov 2016 14:57:21 +0000 Subject: [FieldTrip] Elekta Neuromag SSP projectors In-Reply-To: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> References: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> Message-ID: <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> Hi Davide, At the moment, there is no support for this in Fieldtrip. However, this issue has come up in the past, and back then a bug was filed in our bug tracking system bugzilla.fieldtriptoolbox.org. The bug id is 2109; it has been silent for a while, but if there’s sufficient interest in getting this implemented it might be worthwhile to revive it and get it done. Best wishes, Jan-Mathijs On 22 Nov 2016, at 10:08, Davide Tabarelli > wrote: Dear community, my name is Davide Tabarelli and I’m currently working in the MEG lab @ Center for Mind Brain Sciences (Trento). I’m writing to get some formation about SSP projectors saved in Elekta Neuromag fif files. I know how the MNE python pipeline works (loading projectors & application on request) but I didn’t find information on how Filedtrip deals with these SSP projectors when loading data. Are they automatically applied? Are they discarded? Are they stored somewhere? Thank you in advance for your help. Have a nice day. D. — Davide Tabarelli, Ph.D. Center for Mind Brain Sciences (CIMeC) University of Trento, Via delle Regole, 101 38123 Mattarello (TN) Italy _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: From wanglinsisi at gmail.com Wed Nov 30 16:30:38 2016 From: wanglinsisi at gmail.com (Lin Wang) Date: Wed, 30 Nov 2016 10:30:38 -0500 Subject: [FieldTrip] Elekta Neuromag SSP projectors In-Reply-To: <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> References: <6B6A8EEF-88E0-440F-BF26-87B836C7A4E8@unitn.it> <6F3F64E4-E8A9-47DA-BC10-7CC889C5A892@donders.ru.nl> Message-ID: Hi Davide and JM, Thanks for bring this up. I have the same question and I'd like to see how this can be implemented in fieldtrip. Best, LIn On Wed, Nov 30, 2016 at 9:57 AM, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Hi Davide, > > At the moment, there is no support for this in Fieldtrip. > > However, this issue has come up in the past, and back then a bug was filed > in our bug tracking system bugzilla.fieldtriptoolbox.org. > The bug id is 2109; it has been silent for a while, but if there’s > sufficient interest in getting this implemented it might be worthwhile to > revive it and get it done. > > Best wishes, > Jan-Mathijs > > > > On 22 Nov 2016, at 10:08, Davide Tabarelli > wrote: > > Dear community, > > my name is Davide Tabarelli and I’m currently working in the MEG lab @ > Center for Mind Brain Sciences (Trento). > > I’m writing to get some formation about SSP projectors saved in Elekta > Neuromag fif files. > > I know how the MNE python pipeline works (loading projectors & application > on request) but I didn’t find information on how Filedtrip deals with these > SSP projectors when loading data. Are they automatically applied? Are they > discarded? Are they stored somewhere? > > Thank you in advance for your help. > > Have a nice day. > > D. > > — > Davide Tabarelli, Ph.D. > Center for Mind Brain Sciences (CIMeC) > University of Trento, > Via delle Regole, 101 > 38123 Mattarello (TN) > Italy > > > > > > _______________________________________________ > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 30 16:56:41 2016 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 30 Nov 2016 15:56:41 +0000 Subject: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment In-Reply-To: References: Message-ID: <6DFB7B65-FBFB-4C9D-B84F-DB00A10EF204@donders.ru.nl> Hi Christine, I don’t have the answer to your specific question, but I want to raise a few points: Although the permutation framework as implemented in FT outputs 1 p-value per cluster (for a two-sided test, both for the ‘negative’ and for the ‘positive’ clusters), Only the smallest p-value counts for the statistical inference. This is because your inferential procedure is about making a binary decision, you either reject or accept your null-hypothesis. Also, note that in the permutation framework, without explicit adjustment, the p-values that come out reflect one-sided p-values. For a valid inference, you need to Bonferroni correct these (i.e. multiply them by 2), or adjust the critical alpha level. This being said, I would say that what you want to achieve (i.e. doing post-hoc tests) does not need to be done within the cluster-based framework. The clusters are just byproducts of your inferential procedure. Some general background on how to deal with the output of the tests can be found on: http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test In your specific case, where as a first step you have evaluated the interaction as a difference of differences, I would think it’s fine to use this result to justify a selection of channel + time points, across which you average the condition specific ERP, and which you subject to your post hoc tests. Best, Jan-Mathijs On 30 Nov 2016, at 09:57, Blume Christine > wrote: Maybe an example will stimulate some discussion ;-). I test an interaction among two factors with two levels each with regard to the ERPs elicited by each stimulus. Let’s say we present two stimuli (house vs. face) in colour (black/white vs. colour). The interaction is significant and we want to know which factor levels differ using post hoc tests using the following cfg. cfg = []; cfg.latency = [0 1]; cfg.method = 'montecarlo'; cfg.statistic = 'ft_statfun_depsamplesT'; cfg.correctm = 'cluster'; cfg.clusteralpha = 0.05; cfg.clusterstatistic = 'maxsum'; cfg.minnbchan = 2; cfg.tail = 0; cfg.clustertail = 0; cfg.alpha = 0.025; cfg.numrandomization = 1000; cfg_neighb.method = 'distance'; cfg.neighbours = ft_prepare_neighbours(cfg_neighb, data_sample); cfg.design = design; cfg.ivar = 2; cfg.uvar = 1; What we get is the following. stat_erp.FACE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.FACE_BW); stat_erp.FACE_COLOURvsFACE_BW.posclusters.prob % 18 clusters, p > 0.4076 stat_erp.FACE_COLOURvsFACE_BW.negclusters.prob % 12 clusters, p > 0.2957 stat_erp.FACE_COLOURvsHOUSE_COLOUR = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_COLOUR); stat_erp.FACE_COLOURvsHOUSE_COLOUR.posclusters.prob % 10 clusters, p > 0.4326 stat_erp.FACE_COLOURvsHOUSE_COLOUR.negclusters.prob % 12 clusters, p = 0.0599; p > 0.8631 stat_erp.FACE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_COLOURvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0360, p > 0.7742 stat_erp.FACE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0300, p > 0.5105 stat_erp.HOUSE_COLOURvsFACE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.FACE_BW); stat_erp.HOUSE_COLOURvsFACE_BW.posclusters.prob % 16 clusters, p > 0.2697 stat_erp.HOUSE_COLOURvsFACE_BW.negclusters.prob % 11 clusters, p = 0.0090, p > .0.9291 stat_erp.HOUSE_COLOURvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.HOUSE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.HOUSE_COLOURvsHOUSE_BW.posclusters.prob % 19 clusters, p = 0.0020, p = 0.0979, p > 0.2468 stat_erp.HOUSE_COLOURvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0480, p = 0.0699, p > 0.3606 stat_erp.FACE_BWvsHOUSE_BW = ft_timelockstatistics(cfg, grandavg_erp.FACE_COLOUR, grandavg_erp.HOUSE_BW); stat_erp.FACE_BWvsHOUSE_BW.posclusters.prob % 16 clusters, p = 0.0410, p > 0.7732 stat_erp.FACE_BWvsHOUSE_BW.negclusters.prob % 13 clusters, p = 0.0320, p > 0.4725 Now the question is: how do we correct for these 6 follow-up tests? If we say we want to use the Bonferroni-Holm correction for example, we have to sort p-values according to size. However, we have two p-values per test, one for positive and one for negative clusters and Bonferroni-Holm assumes we only have one per test. Any ideas or suggestions how to best handle this? Best, Christine Von: Blume Christine Gesendet: Montag, 28. November 2016 17:04 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: AW: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Sorry, I forgot to mention that I am using cfg.method = 'montecarlo'; Best, Christine Von: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] Im Auftrag von Blume Christine Gesendet: Montag, 28. November 2016 16:46 An: FieldTrip discussion list (fieldtrip at science.ru.nl) Betreff: [FieldTrip] Cluster-based permutation: Interactions & Alpha adjustment Dear community, I am using the cluster-based permutation test for statistical evaluation of my data. Besides main effects I also test the interaction between them. When following up on a significant interaction with further permutation tests I feel I should correct my alpha level for multiple comparisons. Is that correct? Best, Christine --------------------------------------------------------------------------------- Dipl.-Psych. Christine Blume University of Salzburg Department of Psychology Centre for Cognitive Neuroscience (CCNS) Laboratory for Sleep, Cognition and Consciousness Research Hellbrunner Str. 34 A-5020 Salzburg Email: christine.blume at sbg.ac.at T: +43 (0) 662 – 8044 5146 www.sleepscience.at _______________________________________________ fieldtrip mailing list fieldtrip at donders.ru.nl https://mailman.science.ru.nl/mailman/listinfo/fieldtrip -------------- next part -------------- An HTML attachment was scrubbed... URL: