[FieldTrip] ft_globalmeanfield : Undefined function of variable
barbara.schorr at uni-ulm.de
barbara.schorr at uni-ulm.de
Wed Sep 17 11:22:19 CEST 2014
Dear Jim,
I now downloaded the newest fieldtrip version but I still get the same
error messag. Is there anything else I need to know?
Thanks a lot!
Best,
Barbara
Zitat von fieldtrip-request at science.ru.nl:
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> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of fieldtrip digest..."
>
>
> Today's Topics:
>
> 1. Re: ft_rejectvisual: removing trials marked as bad (Ashley Greene)
> 2. Read only EEG data from combined MEG/EEG ctf file (Tobias Trame)
> 3. Re: ft_globalmeanfield : Undefined function of variable
> 'abort' (Herring, J.D. (Jim))
> 4. using ft_megplanar with sinle-trial TFRs (Fr?d?ric Roux)
> 5. dipole simulation on head model (gamaliel huerta urrea)
> 6. Re: dipole simulation on head model (Tzvetan Popov)
> 7. Re: ft_globalmeanfield : Undefined function of variable
> 'abort' (Jing Fan)
> 8. Re: ft_globalmeanfield : Undefined function of variable
> 'abort' (Jing Fan)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 12 Sep 2014 21:39:43 +0900
> From: Ashley Greene <agreene24 at gmail.com>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] ft_rejectvisual: removing trials marked as
> bad
> Message-ID:
> <CAG9tv=Q9gS=FsuC7GZoL8u_fhBFzzZBDSjjP7d1SRo9OsjC3Rg at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi Emilie,
>
> Thanks!! I, of course, completely overlooked that.
>
> Ashley
>
> On Tue, Sep 9, 2014 at 3:37 PM, Caspar, Emilie <e.caspar at ucl.ac.uk> wrote:
>
>> Hi Ashley,
>>
>> Maybe the mistake is you did not integrate in your script the command to
>> save the rejection. Normally, with the following command, you will see in
>> the Matlab Window that artifacts are correctly rejected after the "quit"
>> button.
>>
>> clean_data = ft_rejectvisual(cfg, interpoldata);
>> cfg.artfctdef.reject = 'complete';
>> cfg.artfctdef.feedback = 'yes';
>> cleandata = ft_rejectartifact(cfg,clean_data);
>>
>> Emilie
>>
>>
>>
>>
>>
>> Le 3 sept. 2014 ? 11:58, Ashley Greene <agreene24 at gmail.com> a ?crit :
>>
>> Hello,
>>
>> I have used the rejectvisual function in attempt to remove bad trials,
>> but after marking them and pressing the quit button, although the command
>> window states that the selected trials have been removed, there is no
>> obvious change in the data; all of the trials are still intact. Have I
>> overlooked something?
>>
>> Thanks,
>>
>> Ashley
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>
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> ------------------------------
>
> Message: 2
> Date: Fri, 12 Sep 2014 18:23:17 +0200 (CEST)
> From: Tobias Trame <tobiastrame at uni-muenster.de>
> To: <fieldtrip at science.ru.nl>
> Subject: [FieldTrip] Read only EEG data from combined MEG/EEG ctf file
> Message-ID:
> <permail-201409121623178954262000002741-t_tram02 at message-id.uni-muenster.de>
>
> Content-Type: text/plain; charset=us-ascii
>
> Hi everybody!
>
> I'm trying to read only the EEG data from a combined MEG/EEG ctf
> file in order
> to create leadfields later on, but the function "ft_read_sens" just returns
> the gradiometer information in a 'grad' field. It searches for an
> 'elec'-field
> in the header of the data which is not present. Is there a configuration to
> get the EEG-elecrodes by using "ft_read_sens"?
>
> My first attempt was to write the EEG-Information from the header by hand
> using the following code:
>
> hdr = ft_read_header('subject.ds', 'headerformat', 'ctf_ds');
> elec.elecpos = [hdr.orig.eeg.pos]';
> elec.chanpos = [hdr.orig.eeg.pos]';
> elec.label = {hdr.orig.eeg.name}';
> elec.type = 'eeg';
> elec.unit = 'cm';
>
> But now I get a similar problem if I want to create leadfields from timelock
> data. After doing the timelock analysis using
>
> timelock_data = ft_timelockanalysis(cfg, mydata);
>
> the structure timelock_data contains just a 'grad' and no 'elec'
> field so that
> ft_prepare_leadfield(cfg, timelock_data) always ignores the EEG-elecrodes and
> tries to use the gradiometes - even if I put the electrodes defined
> above into
> cfg.elec.
>
> Thanks for any suggestions!
>
> Tobias
>
>
> ------------------------------
>
> Message: 3
> Date: Mon, 15 Sep 2014 12:01:02 +0200 (CEST)
> From: "Herring, J.D. (Jim)" <j.herring at fcdonders.ru.nl>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] ft_globalmeanfield : Undefined function of
> variable 'abort'
> Message-ID:
> <2103383764.4690548.1410775262514.JavaMail.root at draco.zimbra.ru.nl>
> Content-Type: text/plain; charset=utf-8
>
> Dear Barbara,
>
> The error seems to be related to a bug that pops up now and then
> (http://bugzilla.fcdonders.nl/show_bug.cgi?id=2580) which could be
> due to a corrupt installation of fieldtrip and/or the need to update
> fieldtrip.
>
> Could you please try downloading the most recent version of
> fieldtrip and see if the problem persists?
>
> For an example of what can be expected from this function see: Esser
> SK, Huber R, Massimini M, Peterson MJ, Ferrarelli F, Tononi G. A
> direct demonstration of cortical LTP in humans: a combined TMS/EEG
> study. Brain Res Bull. 2006 Mar 15;69(1):86-94. Epub 2005 Dec 1.
> PubMed PMID: 16464689.
>
> Best,
>
> Jim
>
>
> ----- Oorspronkelijk bericht -----
>> Van: "barbara schorr" <barbara.schorr at uni-ulm.de>
>> Aan: fieldtrip at science.ru.nl
>> Verzonden: Woensdag 10 september 2014 14:07:14
>> Onderwerp: [FieldTrip] ft_globalmeanfield : Undefined function of
>> variable 'abort'
>> Dear fieldtripers.
>>
>> as mentioned in the subject line I would like to anaylse my timelocked
>> ERP Data with the function ft_globalmeanfield.
>>
>> I get the Error: Undefined function or variable 'abort'
>> Error in ft_globalmeanfield (line84)
>> if abort
>>
>> in the function it says:
>>
>> %the abort variable is set to true or false in ft_preamble_inti
>> if abort
>> % do not continue function execution in case the outputfile is
>> present and the user indicated to keep it
>> return
>> end
>>
>>
>> I don't understand what this means. I would really appreciate your
>> help to get this running.
>>
>> Also, what is the result when I run this function on my data? (This
>> method was proposed from a reviewer. I never used it before, so I
>> don't exactly know what it does and what the result will look like).
>>
>> Thank you very much in advance!
>>
>> Best,
>>
>> Barbara Schorr
>>
>>
>>
>>
>> Zitat von fieldtrip-request at science.ru.nl:
>>
>> > Send fieldtrip mailing list submissions to
>> > fieldtrip at science.ru.nl
>> >
>> > To subscribe or unsubscribe via the World Wide Web, visit
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> > or, via email, send a message with subject or body 'help' to
>> > fieldtrip-request at science.ru.nl
>> >
>> > You can reach the person managing the list at
>> > fieldtrip-owner at science.ru.nl
>> >
>> > When replying, please edit your Subject line so it is more specific
>> > than "Re: Contents of fieldtrip digest..."
>> >
>> >
>> > Today's Topics:
>> >
>> > 1. Re: Question regarding nonparametric testing for coherence
>> > differences (Eric Maris)
>> > 2. Re: Problem with Geodesic 129 layout!! (KatrinH Heimann)
>> > 3. Re: Problem with Geodesic 129 layout!! (Joseph Dien)
>> > 4. Re: ft_rejectvisual: removing trials marked as bad
>> > (Caspar, Emilie)
>> > 5. Re: TRENTOOL pipeline help (Patricia Wollstadt)
>> > 6. Latest FT version on ftp-server is from three days ago
>> > (Holger Krause)
>> > 7. Re: TRENTOOL pipeline help (Max Cantor)
>> >
>> >
>> > ----------------------------------------------------------------------
>> >
>> > Message: 1
>> > Date: Mon, 8 Sep 2014 15:46:11 +0200 (CEST)
>> > From: "Eric Maris" <e.maris at psych.ru.nl>
>> > To: <hweeling.lee at gmail.com>, "'FieldTrip discussion list'"
>> > <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Question regarding nonparametric testing
>> > for
>> > coherence differences
>> > Message-ID: <015901cfcb6b$3fdf1f00$bf9d5d00$@maris at psych.ru.nl>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > Dear Hwee Ling,
>> >
>> >
>> >
>> > In the paper you refer to (Maris et al, JNM, 2007), I did not
>> > perform a
>> > cluster-based permutation test at the level of the channel pairs.
>> > Instead,
>> > all coherences mentioned in the paper are coherence between a single
>> > EMG
>> > channel (2 electrodes mounted on the forearm) and all MEG
>> > electrodes. Thus,
>> > there are as many EMG-MEG coherences as there are MEG channels (275
>> > in our
>> > lab), and the clustering of these coherences involves the same
>> > neighbourhood
>> > structure as the clustering of channel-specific statistics on power
>> > or
>> > evoked response. The analysis you propose involves clustering in a
>> > much more
>> > complex space, namely the formed by all (MEG,MEG) channel pairs
>> > (275*275=75625 pairs). In the early days of cluster-based
>> > permutation
>> > statistics, I have worked on this problem but did not pursue it
>> > because the
>> > resulting clusters are very hard to interpret.
>> >
>> >
>> >
>> > Best,
>> >
>> >
>> >
>> > Eric Maris
>> >
>> >
>> >
>> >
>> >
>> > Dear all and Prof Maris,
>> >
>> > I'm re-posting this question again, as I didn't get any replies
>> > previously.
>> >
>> > I?m interested to investigate if there are any differences in phase
>> > synchronization in resting state data at timepoint 2 versus
>> > timepoint 1. The
>> > EEG data was collected using 64 EEG channels, resting state, eyes
>> > opened and
>> > eyes closed. I?ve arbitrarily segmented the resting state data into
>> > epochs
>> > of 2s each, and the epochs with artifacts are excluded completely
>> > from
>> > further analyses. I then performed mtmfft to get the fourier
>> > representation
>> > of the data, extracted the coherence, and compared the coherence
>> > difference
>> > of timepoint 2 versus timepoint 1 of all channels paired with all
>> > other
>> > channels.
>> >
>> > I figured that if I extract the connectivity analyses without
>> > specifying the
>> > channelcmb, I get a 'chan_chan_freq' dimord variable that would
>> > allow me to
>> > perform cluster-based statistical analyses. I get an output with
>> > ?chan_chan?
>> > dimord variable. However, I was not 100% sure that this is correct,
>> > hence, I?ve
>> > inserted my code (see below). It?ll be great if you could tell me if
>> > what I?m
>> > doing makes any sense at all. Also, I don?t know how I can plot the
>> > results
>> > to see if it make any sense at all.
>> >
>> > Also, I checked the values obtained from when I specified
>> > "cfg.channelcmb"
>> > and when I did not specify "cfg.channelcmb", I noticed that the
>> > values were
>> > somehow different. I would assume that the values to be similar,
>> > although
>> > I'm not sure why they would have differences in the values obtained
>> > from
>> > specifying "cfg.channelcmb".
>> >
>> > This is my code that I've used thus far:
>> >
>> > for sub = 1:5
>> >
>> > cfg = [];
>> >
>> > cfg.output = 'fourier';
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.method = 'mtmfft';
>> >
>> > cfg.keeptrials = 'yes';
>> >
>> > cfg.tapsmofrq = 5;
>> >
>> > cfg.foilim = [0 100];
>> >
>> > cfg.taper = 'dpss';
>> >
>> > % find the index for the c200 condition
>> >
>> > pre_c200_idx = find(data5.trialinfo == 201);
>> >
>> > cfg.trials = pre_c200_idx;
>> >
>> > HLF_pre_c200 = ft_freqanalysis(cfg, data5);
>> >
>> > post_c200_idx = find(data5.trialinfo == 200);
>> >
>> > cfg.trials = post_c200_idx;
>> >
>> > HLF_post_c200 = ft_freqanalysis(cfg, data5);
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.keeptrials = 'no';
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.removemean = 'yes';
>> >
>> > cfg.method = 'coh';
>> >
>> > HLF_pre_c200coh{sub} = ft_connectivityanalysis(cfg,
>> > HLF_pre_c200);
>> >
>> > HLF_post_c200coh{sub} = ft_connectivityanalysis(cfg,
>> > HLF_post_c200);
>> >
>> > end
>> >
>> >
>> >
>> >
>> load('D:\Hweeling_Programs\fieldtrip-20140330\template\layout\easycapM11.mat');
>> >
>> > cfg_neighb.method = 'template';
>> >
>> > cfg_neighb.layout = lay;
>> >
>> > cfg_neighb.channel = 'all';
>> >
>> > neighbours = ft_prepare_neighbours(cfg_neighb,
>> > sub_HLF_pre_c200coh{1});
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay;
>> >
>> > cfg.neighbours = neighbours;
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.channelcmb = {cfg.channel, cfg.channel};
>> >
>> > cfg.latency = 'all';
>> >
>> > cfg.avgovertime = 'no';
>> >
>> > cfg.avgoverchan = 'no';
>> >
>> > cfg.parameter = 'cohspctrm';
>> >
>> > cfg.method = 'montecarlo';
>> >
>> > cfg.statistic = 'depsamplesT';
>> >
>> > cfg.correctm = 'cluster';
>> >
>> > cfg.tail = 0;
>> >
>> > % cfg.clustertail = 0;
>> >
>> > cfg.alpha = 0.05/8; % to correct for multiple comparisons across 8
>> > frequency
>> > bands.
>> >
>> > cfg.numrandomization = 10000;
>> >
>> > cfg.ivar = 2;
>> >
>> > cfg.uvar = 1;
>> >
>> >
>> >
>> > % design matrices
>> >
>> > clear design;
>> >
>> > design(1,:) = [1:5, 1:5];
>> >
>> > design(2,:) = [ones(1,5), ones(1,5) * 2];
>> >
>> > cfg.design = design;
>> >
>> > % for theta band
>> >
>> > cfg.avgoverfreq = 'yes';
>> >
>> > cfg.frequency = [4 8]; % I also performed the statistics for delta
>> > (2-4),
>> > alpha (8-10.5), alpha (10.5-13), beta (13-20), beta (20-30), gamma
>> > (30-40),
>> > and gamma (40-100).
>> >
>> > [diffc200_theta_stat] = ft_freqstatistics(cfg,
>> > sub_HLF_post_c200coh{:},
>> > sub_HLF_pre_c200coh{:});
>> >
>> >
>> >
>> > When I tried to plot the results, I used this code:
>> >
>> > cfg = [];
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.layout = 'lay';
>> >
>> > cfg.zlim = [-1 1];
>> >
>> > cfg.alpha = 0.05;
>> >
>> > cfg.refchannel = 'all';
>> >
>> > ft_clusterplot(cfg, diffc200_theta_stat);
>> >
>> > However, I was not sure how I could plot the results. I get an error
>> > message
>> > from Fieldtrip when using ft_clusterplot:
>> >
>> > Error using topoplot_common (line 366)
>> >
>> > no reference channel is specified
>> >
>> > Error in ft_topoplotTFR (line 192)
>> >
>> > [cfg] = topoplot_common(cfg, varargin{:});
>> >
>> > Error in ft_clusterplot (line 372)
>> >
>> > ft_topoplotTFR(cfgtopo, stat);
>> >
>> >
>> >
>> > According to your paper in 2007, the topoplot of the results were
>> > masked by
>> > the spatio-spectral pattern of the significant clusters. I don't
>> > know how to
>> > do this, and I would really appreciate if you can show me how to
>> > make such a
>> > plot.
>> >
>> > Thank you very much.
>> >
>> > Kind regards,
>> >
>> > Hweeling
>> >
>> >
>> >
>> > -------------- next part --------------
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>> >
>> > ------------------------------
>> >
>> > Message: 2
>> > Date: Mon, 8 Sep 2014 21:38:01 +0200
>> > From: KatrinH Heimann <katrinheimann at gmail.com>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Problem with Geodesic 129 layout!!
>> > Message-ID:
>> > <CACnHcBeqEoQMirfZ-hCKK8rSmbfcNYzxFQH1mmSaeGCuKMN-uA at mail.gmail.com>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > Dear Joseph, thanks so much, I really appreciate your help. I was
>> > also
>> > wandering, if maybe there is another bug in my code.
>> > Our nets are 128 channels, Hydrocel, but as a colleague of me
>> > adviced me to
>> > do so, I also tried the 129 layout.
>> > My code you find below (without selection of bad channels etc.)
>> > If you need also the data of two subjects to have a look, let me
>> > know!!!
>> > Best and thanks really!
>> > Katrin
>> >
>> >
>> > subject=input('Which subject do you want to analyse? ','s');
>> >
>> > name = strcat(subject,'.raw');
>> >
>> > subjectnumber=input('Which is the subjectnumber?', 's');
>> >
>> > sb=subjectnumber
>> >
>> > %subjectnumber = strcat(subjectnumber, '.txt')
>> >
>> > %%
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.dataset = name;
>> >
>> > cfg.trialfun = 'ft_trialfun_general'; % this is the default
>> >
>> > cfg.trialdef.eventtype = 'trigger';
>> >
>> > cfg.trialdef.eventvalue = 'stim'; % the value of the stimulus
>> > trigger
>> >
>> > cfg.trialdef.prestim = 0.234; % in seconds
>> >
>> > cfg.trialdef.poststim = 7.266; % in seconds (whole time of video
>> > observation, but maybe we need less)
>> >
>> > cfg = ft_definetrial(cfg);
>> >
>> >
>> > cfg.trl([1:7],:) = [];
>> >
>> >
>> > %change timeline according to constant offset of 16 ms = 8 samples
>> > (because
>> > recorded with 500 hz)in
>> >
>> > %structure trial
>> >
>> > cfg.trl(:,3)=cfg.trl(:,3)-8
>> >
>> >
>> > %change timeline to make the cut the zeropoint
>> >
>> >
>> > cfg.trl(:,3)=cfg.trl(:,3)- 1000;
>> >
>> >
>> >
>> > %% preprocess data
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.detrend = 'yes';
>> >
>> > cfg.preproc.demean = 'yes';
>> >
>> > cfg.preproc.baselinewindow = [-2.1 -2.0]
>> >
>> > %cfg.preproc.bpfilter = 'yes'; - the data here has to be already
>> > filtered
>> >
>> > %(0.1-45) and bad channels have to be replaced!!!!
>> >
>> > %cfg.preproc.bpfreq = [6 32];
>> >
>> > %
>> >
>> > %
>> >
>> > obs_data = ft_preprocessing(cfg);
>> >
>> > %
>> >
>> > save (strcat(sb,'obs_data') , 'obs_data')
>> >
>> >
>> > %% determining channel neighbours (necessary for Artifact detection)
>> >
>> > cfg = [];
>> >
>> > cfg.channel = obs_data.label;
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.feedback = 'yes';
>> >
>> > lay = ft_prepare_layout(cfg);
>> >
>> >
>> >
>> > cfg_neighb = [];
>> >
>> > cfg_neighb.feedback = 'yes';
>> >
>> >
>> > cfg_neighb.method = 'triangulation';
>> >
>> > cfg_neighb.layout = 'GSN128.sfp';
>> >
>> > neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >
>> >
>> > obs_data.elec = ft_read_sens('GSN128.sfp');
>> >
>> >
>> > %% Artifacts - to detect bad channels - is not saved!!!
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs_data_try = ft_rejectvisual(cfg, obs_data);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_try = ft_rejectartifact (cfg,obs_data);
>> >
>> >
>> >
>> > %% Preparing neighbours for channel repair - but bad channel info
>> > in!!!
>> >
>> >
>> > % cfg_neighb = [];
>> >
>> > % cfg_neighb.feedback = 'yes';
>> >
>> > % cfg_neighb.method = 'triangulation';
>> >
>> > % cfg_neighb.layout = 'GSN-HydroCel-128.sfp';
>> >
>> > % neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >
>> >
>> > % Interpolate and put into new data structure
>> >
>> > cfg = [];
>> >
>> > cfg.badchannel = {};
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.method = 'nearest';
>> >
>> > cfg.neighbours = neighbours;
>> >
>> > obs_data_channelrepaired = ft_channelrepair(cfg,obs_data)
>> >
>> >
>> > % Check reconstruction
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >
>> >
>> > % dat1=obs_data;
>> >
>> > % dat2=obs_data_channelrepaired;
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(62,:); % 68 is channel index of E68
>> >
>> > % y=dat2.trial{1}(62,:);
>> >
>> > % plot(x);hold on;plot(y,'r');
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(72,:);
>> >
>> > % y=dat2.trial{1}(75,:);
>> >
>> > % figure;
>> >
>> > % plot(x);hold on;plot(y,'r')
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(75,:);
>> >
>> > % y=dat2.trial{1}(75,:);
>> >
>> > % figure;
>> >
>> > % plot(x);hold on;plot(y,'r')
>> >
>> > %% artifact rejection/trial inspection - throw out electrode jumps
>> > etc.
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >
>> >
>> > %% ICA - Anzahl der Komponenten anpassen!- adapt numcomponent
>> > according to
>> > channels interpolated %% 128-number of interp. channels)
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.numcomponent = 128
>> >
>> > comp = ft_componentanalysis(cfg,obs_data_clean1);
>> >
>> > save (strcat(sb,'comp_all') , 'comp')
>> >
>> >
>> > %%
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'component';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='some';
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > ft_databrowser(cfg,comp);
>> >
>> >
>> > %% poweranalysis components
>> >
>> > cfg = [];
>> >
>> > cfg.output = 'pow';
>> >
>> > cfg.channel = 'all';%compute the power spectrum in all ICs
>> >
>> > cfg.method = 'mtmfft';
>> >
>> > cfg.taper = 'hanning';
>> >
>> > cfg.foi = 0:0.2:50;
>> >
>> > obs_freq = ft_freqanalysis(cfg, comp);
>> >
>> >
>> > %And you can plot the spectra:
>> >
>> >
>> > %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>> >
>> > nsubplots = 16;
>> >
>> > nbyn = sqrt(nsubplots);% sqrt(nsubplots) should not contain
>> > decimals, type
>> > doc subplot
>> >
>> >
>> > Nfigs = ceil(size(comp.topo,1)/nsubplots);
>> >
>> > tot = Nfigs*nsubplots;
>> >
>> >
>> > rptvect = 1:size(comp.topo,1);
>> >
>> > rptvect = padarray(rptvect, [0 tot-size(comp.topo,1)], 0,'post');
>> >
>> > rptvect = reshape(rptvect,nsubplots,Nfigs)';
>> >
>> >
>> > for r=1:size(rptvect,1);
>> >
>> > figure;set(gcf,'units','normalized','outerposition',[0 0 1
>> > 1]);%full
>> > screen
>> >
>> > k=0;
>> >
>> > for j=1:size(rptvect,2);
>> >
>> > if~(rptvect(r,j)==0);
>> >
>> > k=k+1;
>> >
>> > cfg=[];
>> >
>> > cfg.channel = rptvect(r,j);
>> >
>> > cfg.ylim =[0 500]
>> >
>> > cfg.xlim =[0 50]
>> >
>> > subplot(nbyn,nbyn,k);ft_singleplotER(cfg,obs_freq);
>> >
>> > end
>> >
>> > end
>> >
>> > end
>> >
>> >
>> > %For the IC topos you'll follow the same logic as above but with:
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [1:20]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [21:40]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [41:60]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [61:80]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> >
>> >
>> >
>> >
>> > %% Seperate observation conditions
>> >
>> >
>> > name1= strcat(num2str(sb),'_90.xls');
>> >
>> > list1=xlsread (name1)
>> >
>> >
>> >
>> >
>> >
>> > name2= strcat(num2str(sb),'_180.xls');
>> >
>> > list2=xlsread (name2)
>> >
>> >
>> >
>> > %
>> >
>> >
>> > cfg = []
>> >
>> > cfg.trials = [list1]
>> >
>> > obs90_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.trials = [list2]
>> >
>> > obs180_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >
>> >
>> > %%PIPELINE FOR obs90
>> >
>> >
>> > %% Decompose original data according to the components found before
>> >
>> > load (strcat(sb,'comp_all'))
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.numcomponent = 128;
>> >
>> > cfg.unmixing = comp.unmixing;
>> >
>> > cfg.topolabel = comp.topolabel;
>> >
>> >
>> > comp_1 = ft_componentanalysis(cfg, obs90_data);
>> >
>> >
>> >
>> > %% Reject component
>> >
>> > cfg = [];
>> >
>> > cfg.component = [];
>> >
>> > obs90_data_ica_cleaned = ft_rejectcomponent(cfg, comp_1,
>> > obs90_data);
>> >
>> >
>> >
>> >
>> > save (strcat(sb,'obs90_ica_cleaned') , 'obs90_data_ica_cleaned')
>> >
>> >
>> >
>> >
>> > %% Artifacts - final detection
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs90_data_clean3 = ft_rejectvisual(cfg, obs90_data_ica_cleaned);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs90_data_clean3);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs90_data_clean2 = ft_rejectartifact (cfg,obs90_data_clean3);
>> >
>> >
>> >
>> > % Save clean data
>> >
>> >
>> > save (strcat(subject,'obs90_clean') , 'obs90_data_clean2')
>> >
>> >
>> >
>> > %% Rereferencing data
>> >
>> > cfg = [];
>> >
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.reref = 'yes';
>> >
>> > cfg.preproc.refchannel = 'all';
>> >
>> > obs90_data_ref= ft_preprocessing(cfg,obs90_data_clean2)
>> >
>> >
>> > save (strcat(subject,'obs90_ref') , 'obs90_data_ref')
>> >
>> > %% Snap out smaller pieces (the third second)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.toilim = [0 1]
>> >
>> >
>> > obs90_data_small = ft_redefinetrial(cfg,obs90_data_ref)
>> >
>> >
>> > %% TIMELOCK ERP
>> >
>> > obs90_data_ERP = ft_timelockanalysis(cfg, obs90_data_small);
>> >
>> >
>> > save (strcat(subject,'obs90_ERP') , 'obs90_data_ERP')
>> >
>> >
>> > %% plot it
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, obs90_data_ERP)
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > %% PIPELINE FOR obs180
>> >
>> >
>> >
>> > %% Decompose original data according to the components found before
>> >
>> > load (strcat(sb,'comp_all'))
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.numcomponent = 128
>> >
>> > cfg.unmixing = comp.unmixing;
>> >
>> > cfg.topolabel = comp.topolabel;
>> >
>> > comp_2 = ft_componentanalysis(cfg, obs180_data);
>> >
>> >
>> >
>> > %% Reject component
>> >
>> > cfg = [];
>> >
>> > cfg.component = [];
>> >
>> > obs180_data_ica_cleaned = ft_rejectcomponent(cfg, comp_2,
>> > obs180_data);
>> >
>> >
>> >
>> >
>> > save (strcat(sb,'obs180_ica_cleaned') , 'obs180_data_ica_cleaned')
>> >
>> >
>> >
>> >
>> > %% Artifacts final 180
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs180_data_clean3 = ft_rejectvisual(cfg, obs180_data_ica_cleaned);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs180_data_clean3);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs180_data_clean2 = ft_rejectartifact (cfg,obs180_data_clean3);
>> >
>> >
>> >
>> > % Save clean data
>> >
>> >
>> > save (strcat(subject,'obs180_clean') , 'obs180_data_clean2')
>> >
>> >
>> >
>> > %% Rereferencing data
>> >
>> > cfg = [];
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.reref = 'yes';
>> >
>> > cfg.preproc.refchannel = 'all';
>> >
>> > obs180_data_ref= ft_preprocessing(cfg,obs180_data_clean2)
>> >
>> >
>> > save (strcat(subject,'obs180_ref') , 'obs180_data_ref')
>> >
>> > %% Snap out smaller pieces (the third second)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.toilim = [0 1]
>> >
>> >
>> > obs180_data_small = ft_redefinetrial(cfg,obs180_data_ref)
>> >
>> >
>> > %% TIMELOCK ERP
>> >
>> > obs180_data_ERP = ft_timelockanalysis(cfg, obs180_data_small);
>> >
>> >
>> > save (strcat(subject,'obs180_ERP') , 'obs180_data_ERP')
>> >
>> >
>> > %% plot 180 ERP
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, obs180_data_ERP)
>> >
>> >
>> > %% plot both ERPs
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > cfg.showlabels = 'yes';
>> >
>> > ft_multiplotER(cfg, obs90_data_ERP, obs180_data_ERP)
>> >
>> >
>> > %% plot difference wave
>> >
>> >
>> > difference = obs180_data_ERP; % copy one of the
>> > structures
>> >
>> > difference.avg = obs180_data_ERP.avg - obs90_data_ERP.avg; % compute
>> > the
>> > difference ERP
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, difference)
>> >
>> > 2014-09-07 7:53 GMT+02:00 Joseph Dien <jdien07 at mac.com>:
>> >
>> >> Hi,
>> >> I?m one of the main Fieldtrip contributors working on supporting
>> >> EGI
>> >> data. A couple things. First of all, exactly which net do you use?
>> >> If
>> >> they are 129-channel, there is still the question of whether they
>> >> are the
>> >> Hydrocel or the older geodesic nets. Regardless, that shouldn't
>> >> cause an
>> >> error like this. Could you send me the file you are trying to
>> >> process and
>> >> the script you are using and I?ll try troubleshooting it.
>> >>
>> >> Joe
>> >>
>> >>
>> >> On Aug 26, 2014, at 12:53 AM, Ana Laura Diez Martini <
>> >> diezmartini at gmail.com> wrote:
>> >>
>> >> Which nets do you use? I use EGI. I tried both the ones you mention
>> >> and
>> >> they didn't work. It was hard to find that exact one online but it
>> >> was the
>> >> only file that actually worked.
>> >>
>> >>
>> >> On Mon, Aug 25, 2014 at 7:52 AM, KatrinH Heimann
>> >> <katrinheimann at gmail.com>
>> >> wrote:
>> >>
>> >>> Dear Ana, dear all,
>> >>> the layout you used does not correspond to our nets. I tried the
>> >>> GSN-HydroCel-128.sfp and the GSN-HydroCel-129.sfp. Both don't
>> >>> work.
>> >>> Please can anybody help?
>> >>> Cheers
>> >>> Katrin
>> >>>
>> >>>
>> >>> 2014-08-24 5:40 GMT+02:00 Ana Laura Diez Martini
>> >>> <diezmartini at gmail.com>:
>> >>>
>> >>> Try this one
>> >>> <https://www.dropbox.com/s/58zv658rtdant2c/GSN129.sfp?dl=0>
>> >>>> I use
>> >>>>
>> >>>>
>> >>>> On Sun, Aug 24, 2014 at 4:08 AM, KatrinH Heimann <
>> >>>> katrinheimann at gmail.com> wrote:
>> >>>>
>> >>>>> Dear all, my problems seem neverending. This time however i
>> >>>>> really need
>> >>>>> help.
>> >>>>> I implemented a pipeline for ERPs. All works now,
>> >>>>> trialdefinition,
>> >>>>> preprocessing, channelreplacement, ica, componentrejection,
>> >>>>> final
>> >>>>> artifactdetection, timelock of the single subject data. However,
>> >>>>> when I
>> >>>>> wanna compute the grandaverage of the single subjects I get the
>> >>>>> following
>> >>>>> error message:
>> >>>>>
>> >>>>> computing average of avg over 19 subjects
>> >>>>> Warning: discarding electrode information because it cannot be
>> >>>>> averaged
>> >>>>> > In ft_timelockgrandaverage at 249
>> >>>>> the call to "ft_timelockgrandaverage" took 0 seconds and
>> >>>>> required the
>> >>>>> additional allocation of an estimated 3 MB
>> >>>>> computing average of avg over 2 subjects
>> >>>>> Warning: discarding electrode information because it cannot be
>> >>>>> averaged
>> >>>>> > In ft_timelockgrandaverage at 249
>> >>>>> the call to "ft_timelockgrandaverage" took 0 seconds and
>> >>>>> required the
>> >>>>> additional allocation of an estimated 0 MB
>> >>>>> the call to "ft_prepare_layout" took 0 seconds and required the
>> >>>>> additional allocation of an estimated 0 MB
>> >>>>> the call to "ft_topoplotER" took 0 seconds and required the
>> >>>>> additional
>> >>>>> allocation of an estimated 0 MB
>> >>>>>
>> >>>>> Furthermore in the plot of the significant clusters, the
>> >>>>> channelnames
>> >>>>> are mixed up (do not correspond to my net)
>> >>>>>
>> >>>>>
>> >>>>> I guess that there is a problem with the layout I use.
>> >>>>> Here the code that I use
>> >>>>>
>> >>>>> %For generating the layout (also in the single subjects):
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.channel = obs_data.label;
>> >>>>>
>> >>>>> cfg.layout = 'GSN-HydroCel-129.sfp';
>> >>>>>
>> >>>>> cfg.feedback = 'yes';
>> >>>>>
>> >>>>> lay = ft_prepare_layout(cfg);
>> >>>>>
>> >>>>>
>> >>>>> cfg_neighb = [];
>> >>>>>
>> >>>>> cfg_neighb.feedback = 'yes';
>> >>>>>
>> >>>>> cfg_neighb.method = 'triangulation';
>> >>>>>
>> >>>>> cfg_neighb.layout = lay;
>> >>>>>
>> >>>>> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>>>>
>> >>>>>
>> >>>>> obs_data.elec = ft_read_sens('GSN-HydroCel-129.sfp');
>> >>>>>
>> >>>>>
>> >>>>> %For computing the grand average
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.channel = 'all';
>> >>>>>
>> >>>>> cfg.latency = 'all';
>> >>>>>
>> >>>>> cfg.parameter = 'avg';
>> >>>>>
>> >>>>> cfg.keepindividual = 'no'
>> >>>>>
>> >>>>> GA_90 = ft_timelockgrandaverage(cfg,all90{:});
>> >>>>>
>> >>>>> GA_180 = ft_timelockgrandaverage(cfg,all180{:});
>> >>>>>
>> >>>>> % "{:}" means to use data from all elements of the variable
>> >>>>>
>> >>>>>
>> >>>>> For plotting the significant clusters
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.style = 'blank';
>> >>>>>
>> >>>>> cfg.layout = lay;
>> >>>>>
>> >>>>> cfg.channellabels = 'yes';
>> >>>>>
>> >>>>> cfg.highlight = 'labels';
>> >>>>>
>> >>>>> cfg.highlightchannel = find(stat.mask);
>> >>>>>
>> >>>>> cfg.comment = 'no';
>> >>>>>
>> >>>>> figure; ft_topoplotER(cfg, GA_90)
>> >>>>>
>> >>>>> title('Nonparametric: significant with cluster multiple
>> >>>>> comparison
>> >>>>> correction')
>> >>>>>
>> >>>>>
>> >>>>> Do you have ANY idea to this? I am really completely
>> >>>>> helpless....
>> >>>>>
>> >>>>> thanks and best
>> >>>>>
>> >>>>> Katrin
>> >>>>>
>> >>>>> _______________________________________________
>> >>>>> fieldtrip mailing list
>> >>>>> fieldtrip at donders.ru.nl
>> >>>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>>>
>> >>>>
>> >>>>
>> >>>> _______________________________________________
>> >>>> fieldtrip mailing list
>> >>>> fieldtrip at donders.ru.nl
>> >>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>>
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
>> >>
>> >>
>> --------------------------------------------------------------------------------
>> >>
>> >> Joseph Dien, PhD
>> >> Research Associate
>> >> Cognitive Neurology
>> >> The Johns Hopkins University School of Medicine
>> >>
>> >> Lab E-mail: jdien1 at jhmi.edu
>> >> Private E-mail: jdien07 at mac.com
>> >> Office Phone: 410-614-3115
>> >> Cell Phone: 202-297-8117
>> >> Fax: 410-955-0188
>> >> http://joedien.com
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
>> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140908/e8846b61/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 3
>> > Date: Mon, 08 Sep 2014 21:55:44 -0400
>> > From: Joseph Dien <jdien07 at mac.com>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Problem with Geodesic 129 layout!!
>> > Message-ID: <148A801A-8CEA-4F88-A7D0-130637BDAC03 at mac.com>
>> > Content-Type: text/plain; charset="windows-1252"
>> >
>> > yes, please do send me the data.
>> >
>> > Thanks!
>> >
>> > Joe
>> >
>> > On Sep 8, 2014, at 3:38 PM, KatrinH Heimann
>> > <katrinheimann at gmail.com> wrote:
>> >
>> >> Dear Joseph, thanks so much, I really appreciate your help. I was
>> >> also wandering, if maybe there is another bug in my code.
>> >> Our nets are 128 channels, Hydrocel, but as a colleague of me
>> >> adviced me to do so, I also tried the 129 layout.
>> >> My code you find below (without selection of bad channels etc.)
>> >> If you need also the data of two subjects to have a look, let me
>> >> know!!!
>> >> Best and thanks really!
>> >> Katrin
>> >>
>> >>
>> >>
>> >> subject=input('Which subject do you want to analyse? ','s');
>> >>
>> >> name = strcat(subject,'.raw');
>> >>
>> >> subjectnumber=input('Which is the subjectnumber?', 's');
>> >>
>> >> sb=subjectnumber
>> >>
>> >> %subjectnumber = strcat(subjectnumber, '.txt')
>> >>
>> >> %%
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.dataset = name;
>> >>
>> >> cfg.trialfun = 'ft_trialfun_general'; % this is the default
>> >>
>> >> cfg.trialdef.eventtype = 'trigger';
>> >>
>> >> cfg.trialdef.eventvalue = 'stim'; % the value of the stimulus
>> >> trigger
>> >>
>> >> cfg.trialdef.prestim = 0.234; % in seconds
>> >>
>> >> cfg.trialdef.poststim = 7.266; % in seconds (whole time of video
>> >> observation, but maybe we need less)
>> >>
>> >> cfg = ft_definetrial(cfg);
>> >>
>> >>
>> >>
>> >> cfg.trl([1:7],:) = [];
>> >>
>> >>
>> >>
>> >> %change timeline according to constant offset of 16 ms = 8 samples
>> >> (because recorded with 500 hz)in
>> >>
>> >> %structure trial
>> >>
>> >> cfg.trl(:,3)=cfg.trl(:,3)-8
>> >>
>> >>
>> >>
>> >> %change timeline to make the cut the zeropoint
>> >>
>> >>
>> >>
>> >> cfg.trl(:,3)=cfg.trl(:,3)- 1000;
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% preprocess data
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.detrend = 'yes';
>> >>
>> >> cfg.preproc.demean = 'yes';
>> >>
>> >> cfg.preproc.baselinewindow = [-2.1 -2.0]
>> >>
>> >> %cfg.preproc.bpfilter = 'yes'; - the data here has to be already
>> >> filtered
>> >>
>> >> %(0.1-45) and bad channels have to be replaced!!!!
>> >>
>> >> %cfg.preproc.bpfreq = [6 32];
>> >>
>> >> %
>> >>
>> >> %
>> >>
>> >> obs_data = ft_preprocessing(cfg);
>> >>
>> >> %
>> >>
>> >> save (strcat(sb,'obs_data') , 'obs_data')
>> >>
>> >>
>> >>
>> >> %% determining channel neighbours (necessary for Artifact
>> >> detection)
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = obs_data.label;
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.feedback = 'yes';
>> >>
>> >> lay = ft_prepare_layout(cfg);
>> >>
>> >>
>> >> cfg_neighb = [];
>> >>
>> >> cfg_neighb.feedback = 'yes';
>> >>
>> >>
>> >>
>> >> cfg_neighb.method = 'triangulation';
>> >>
>> >> cfg_neighb.layout = 'GSN128.sfp';
>> >>
>> >> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>
>> >>
>> >>
>> >> obs_data.elec = ft_read_sens('GSN128.sfp');
>> >>
>> >>
>> >>
>> >> %% Artifacts - to detect bad channels - is not saved!!!
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs_data_try = ft_rejectvisual(cfg, obs_data);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_try = ft_rejectartifact (cfg,obs_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Preparing neighbours for channel repair - but bad channel info
>> >> in!!!
>> >>
>> >>
>> >>
>> >> % cfg_neighb = [];
>> >>
>> >> % cfg_neighb.feedback = 'yes';
>> >>
>> >> % cfg_neighb.method = 'triangulation';
>> >>
>> >> % cfg_neighb.layout = 'GSN-HydroCel-128.sfp';
>> >>
>> >> % neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>
>> >>
>> >>
>> >> % Interpolate and put into new data structure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.badchannel = {};
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.method = 'nearest';
>> >>
>> >> cfg.neighbours = neighbours;
>> >>
>> >> obs_data_channelrepaired = ft_channelrepair(cfg,obs_data)
>> >>
>> >>
>> >>
>> >> % Check reconstruction
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >>
>> >>
>> >>
>> >> % dat1=obs_data;
>> >>
>> >> % dat2=obs_data_channelrepaired;
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(62,:); % 68 is channel index of E68
>> >>
>> >> % y=dat2.trial{1}(62,:);
>> >>
>> >> % plot(x);hold on;plot(y,'r');
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(72,:);
>> >>
>> >> % y=dat2.trial{1}(75,:);
>> >>
>> >> % figure;
>> >>
>> >> % plot(x);hold on;plot(y,'r')
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(75,:);
>> >>
>> >> % y=dat2.trial{1}(75,:);
>> >>
>> >> % figure;
>> >>
>> >> % plot(x);hold on;plot(y,'r')
>> >>
>> >> %% artifact rejection/trial inspection - throw out electrode jumps
>> >> etc.
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >>
>> >>
>> >>
>> >> %% ICA - Anzahl der Komponenten anpassen!- adapt numcomponent
>> >> according to channels interpolated %% 128-number of interp.
>> >> channels)
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = {'all'};
>> >>
>> >> cfg.numcomponent = 128
>> >>
>> >> comp = ft_componentanalysis(cfg,obs_data_clean1);
>> >>
>> >> save (strcat(sb,'comp_all') , 'comp')
>> >>
>> >>
>> >>
>> >> %%
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'component';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='some';
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> ft_databrowser(cfg,comp);
>> >>
>> >>
>> >>
>> >> %% poweranalysis components
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.output = 'pow';
>> >>
>> >> cfg.channel = 'all';%compute the power spectrum in all ICs
>> >>
>> >> cfg.method = 'mtmfft';
>> >>
>> >> cfg.taper = 'hanning';
>> >>
>> >> cfg.foi = 0:0.2:50;
>> >>
>> >> obs_freq = ft_freqanalysis(cfg, comp);
>> >>
>> >>
>> >>
>> >> %And you can plot the spectra:
>> >>
>> >>
>> >>
>> >> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>> >>
>> >> nsubplots = 16;
>> >>
>> >> nbyn = sqrt(nsubplots);% sqrt(nsubplots) should not contain
>> >> decimals, type doc subplot
>> >>
>> >>
>> >>
>> >> Nfigs = ceil(size(comp.topo,1)/nsubplots);
>> >>
>> >> tot = Nfigs*nsubplots;
>> >>
>> >>
>> >>
>> >> rptvect = 1:size(comp.topo,1);
>> >>
>> >> rptvect = padarray(rptvect, [0 tot-size(comp.topo,1)], 0,'post');
>> >>
>> >> rptvect = reshape(rptvect,nsubplots,Nfigs)';
>> >>
>> >>
>> >>
>> >> for r=1:size(rptvect,1);
>> >>
>> >> figure;set(gcf,'units','normalized','outerposition',[0 0 1
>> >> 1]);%full screen
>> >>
>> >> k=0;
>> >>
>> >> for j=1:size(rptvect,2);
>> >>
>> >> if~(rptvect(r,j)==0);
>> >>
>> >> k=k+1;
>> >>
>> >> cfg=[];
>> >>
>> >> cfg.channel = rptvect(r,j);
>> >>
>> >> cfg.ylim =[0 500]
>> >>
>> >> cfg.xlim =[0 50]
>> >>
>> >> subplot(nbyn,nbyn,k);ft_singleplotER(cfg,obs_freq);
>> >>
>> >> end
>> >>
>> >> end
>> >>
>> >> end
>> >>
>> >>
>> >>
>> >> %For the IC topos you'll follow the same logic as above but with:
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [1:20]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [21:40]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [41:60]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [61:80]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Seperate observation conditions
>> >>
>> >>
>> >>
>> >> name1= strcat(num2str(sb),'_90.xls');
>> >>
>> >> list1=xlsread (name1)
>> >>
>> >>
>> >>
>> >> name2= strcat(num2str(sb),'_180.xls');
>> >>
>> >> list2=xlsread (name2)
>> >>
>> >>
>> >> %
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.trials = [list1]
>> >>
>> >> obs90_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.trials = [list2]
>> >>
>> >> obs180_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >>
>> >>
>> >>
>> >> %%PIPELINE FOR obs90
>> >>
>> >>
>> >>
>> >> %% Decompose original data according to the components found before
>> >>
>> >> load (strcat(sb,'comp_all'))
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.numcomponent = 128;
>> >>
>> >> cfg.unmixing = comp.unmixing;
>> >>
>> >> cfg.topolabel = comp.topolabel;
>> >>
>> >>
>> >>
>> >> comp_1 = ft_componentanalysis(cfg, obs90_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Reject component
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [];
>> >>
>> >> obs90_data_ica_cleaned = ft_rejectcomponent(cfg, comp_1,
>> >> obs90_data);
>> >>
>> >>
>> >>
>> >>
>> >> save (strcat(sb,'obs90_ica_cleaned') , 'obs90_data_ica_cleaned')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Artifacts - final detection
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs90_data_clean3 = ft_rejectvisual(cfg, obs90_data_ica_cleaned);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs90_data_clean3);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs90_data_clean2 = ft_rejectartifact (cfg,obs90_data_clean3);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> % Save clean data
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_clean') , 'obs90_data_clean2')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Rereferencing data
>> >>
>> >> cfg = [];
>> >>
>> >>
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.reref = 'yes';
>> >>
>> >> cfg.preproc.refchannel = 'all';
>> >>
>> >> obs90_data_ref= ft_preprocessing(cfg,obs90_data_clean2)
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_ref') , 'obs90_data_ref')
>> >>
>> >> %% Snap out smaller pieces (the third second)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.toilim = [0 1]
>> >>
>> >>
>> >>
>> >> obs90_data_small = ft_redefinetrial(cfg,obs90_data_ref)
>> >>
>> >>
>> >>
>> >> %% TIMELOCK ERP
>> >>
>> >> obs90_data_ERP = ft_timelockanalysis(cfg, obs90_data_small);
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_ERP') , 'obs90_data_ERP')
>> >>
>> >>
>> >>
>> >> %% plot it
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs90_data_ERP)
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% PIPELINE FOR obs180
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Decompose original data according to the components found before
>> >>
>> >> load (strcat(sb,'comp_all'))
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.numcomponent = 128
>> >>
>> >> cfg.unmixing = comp.unmixing;
>> >>
>> >> cfg.topolabel = comp.topolabel;
>> >>
>> >> comp_2 = ft_componentanalysis(cfg, obs180_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Reject component
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [];
>> >>
>> >> obs180_data_ica_cleaned = ft_rejectcomponent(cfg, comp_2,
>> >> obs180_data);
>> >>
>> >>
>> >>
>> >>
>> >> save (strcat(sb,'obs180_ica_cleaned') , 'obs180_data_ica_cleaned')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Artifacts final 180
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs180_data_clean3 = ft_rejectvisual(cfg, obs180_data_ica_cleaned);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs180_data_clean3);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs180_data_clean2 = ft_rejectartifact (cfg,obs180_data_clean3);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> % Save clean data
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_clean') , 'obs180_data_clean2')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Rereferencing data
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.reref = 'yes';
>> >>
>> >> cfg.preproc.refchannel = 'all';
>> >>
>> >> obs180_data_ref= ft_preprocessing(cfg,obs180_data_clean2)
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_ref') , 'obs180_data_ref')
>> >>
>> >> %% Snap out smaller pieces (the third second)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.toilim = [0 1]
>> >>
>> >>
>> >>
>> >> obs180_data_small = ft_redefinetrial(cfg,obs180_data_ref)
>> >>
>> >>
>> >>
>> >> %% TIMELOCK ERP
>> >>
>> >> obs180_data_ERP = ft_timelockanalysis(cfg, obs180_data_small);
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_ERP') , 'obs180_data_ERP')
>> >>
>> >>
>> >>
>> >> %% plot 180 ERP
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs180_data_ERP)
>> >>
>> >>
>> >>
>> >> %% plot both ERPs
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> cfg.showlabels = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs90_data_ERP, obs180_data_ERP)
>> >>
>> >>
>> >>
>> >> %% plot difference wave
>> >>
>> >>
>> >>
>> >> difference = obs180_data_ERP; % copy one of
>> >> the structures
>> >>
>> >> difference.avg = obs180_data_ERP.avg - obs90_data_ERP.avg; %
>> >> compute the difference ERP
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, difference)
>> >>
>> >>
>> >> 2014-09-07 7:53 GMT+02:00 Joseph Dien <jdien07 at mac.com>:
>> >> Hi,
>> >> I?m one of the main Fieldtrip contributors working on supporting
>> >> EGI data. A couple things. First of all, exactly which net do
>> >> you use? If they are 129-channel, there is still the question of
>> >> whether they are the Hydrocel or the older geodesic nets.
>> >> Regardless, that shouldn't cause an error like this. Could you
>> >> send me the file you are trying to process and the script you are
>> >> using and I?ll try troubleshooting it.
>> >>
>> >> Joe
>> >>
>> >>
>> >> On Aug 26, 2014, at 12:53 AM, Ana Laura Diez Martini
>> >> <diezmartini at gmail.com> wrote:
>> >>
>> >>> Which nets do you use? I use EGI. I tried both the ones you
>> >>> mention and they didn't work. It was hard to find that exact one
>> >>> online but it was the only file that actually worked.
>> >>>
>> >>>
>> >>> On Mon, Aug 25, 2014 at 7:52 AM, KatrinH Heimann
>> >>> <katrinheimann at gmail.com> wrote:
>> >>> Dear Ana, dear all,
>> >>> the layout you used does not correspond to our nets. I tried the
>> >>> GSN-HydroCel-128.sfp and the GSN-HydroCel-129.sfp. Both don't
>> >>> work. Please can anybody help?
>> >>> Cheers
>> >>> Katrin
>> >>>
>> >>>
>> >>> 2014-08-24 5:40 GMT+02:00 Ana Laura Diez Martini
>> >>> <diezmartini at gmail.com>:
>> >>>
>> >>> Try this one I use
>> >>>
>> >>>
>> >>> On Sun, Aug 24, 2014 at 4:08 AM, KatrinH Heimann
>> >>> <katrinheimann at gmail.com> wrote:
>> >>> Dear all, my problems seem neverending. This time however i really
>> >>> need help.
>> >>> I implemented a pipeline for ERPs. All works now, trialdefinition,
>> >>> preprocessing, channelreplacement, ica, componentrejection, final
>> >>> artifactdetection, timelock of the single subject data. However,
>> >>> when I wanna compute the grandaverage of the single subjects I get
>> >>> the following error message:
>> >>>
>> >>> computing average of avg over 19 subjects
>> >>> Warning: discarding electrode information because it cannot be
>> >>> averaged
>> >>> > In ft_timelockgrandaverage at 249
>> >>> the call to "ft_timelockgrandaverage" took 0 seconds and required
>> >>> the additional allocation of an estimated 3 MB
>> >>> computing average of avg over 2 subjects
>> >>> Warning: discarding electrode information because it cannot be
>> >>> averaged
>> >>> > In ft_timelockgrandaverage at 249
>> >>> the call to "ft_timelockgrandaverage" took 0 seconds and required
>> >>> the additional allocation of an estimated 0 MB
>> >>> the call to "ft_prepare_layout" took 0 seconds and required the
>> >>> additional allocation of an estimated 0 MB
>> >>> the call to "ft_topoplotER" took 0 seconds and required the
>> >>> additional allocation of an estimated 0 MB
>> >>>
>> >>> Furthermore in the plot of the significant clusters, the
>> >>> channelnames are mixed up (do not correspond to my net)
>> >>>
>> >>>
>> >>> I guess that there is a problem with the layout I use.
>> >>> Here the code that I use
>> >>>
>> >>> %For generating the layout (also in the single subjects):
>> >>> cfg = [];
>> >>>
>> >>> cfg.channel = obs_data.label;
>> >>>
>> >>> cfg.layout = 'GSN-HydroCel-129.sfp';
>> >>>
>> >>> cfg.feedback = 'yes';
>> >>>
>> >>> lay = ft_prepare_layout(cfg);
>> >>>
>> >>>
>> >>> cfg_neighb = [];
>> >>>
>> >>> cfg_neighb.feedback = 'yes';
>> >>>
>> >>> cfg_neighb.method = 'triangulation';
>> >>>
>> >>> cfg_neighb.layout = lay;
>> >>>
>> >>> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>>
>> >>>
>> >>> obs_data.elec = ft_read_sens('GSN-HydroCel-129.sfp');
>> >>>
>> >>>
>> >>>
>> >>> %For computing the grand average
>> >>>
>> >>> cfg = [];
>> >>>
>> >>> cfg.channel = 'all';
>> >>>
>> >>> cfg.latency = 'all';
>> >>>
>> >>> cfg.parameter = 'avg';
>> >>>
>> >>> cfg.keepindividual = 'no'
>> >>>
>> >>> GA_90 = ft_timelockgrandaverage(cfg,all90{:});
>> >>>
>> >>> GA_180 = ft_timelockgrandaverage(cfg,all180{:});
>> >>>
>> >>> % "{:}" means to use data from all elements of the variable
>> >>>
>> >>>
>> >>> For plotting the significant clusters
>> >>>
>> >>> cfg = [];
>> >>>
>> >>> cfg.style = 'blank';
>> >>>
>> >>> cfg.layout = lay;
>> >>>
>> >>> cfg.channellabels = 'yes';
>> >>>
>> >>> cfg.highlight = 'labels';
>> >>>
>> >>> cfg.highlightchannel = find(stat.mask);
>> >>>
>> >>> cfg.comment = 'no';
>> >>>
>> >>> figure; ft_topoplotER(cfg, GA_90)
>> >>>
>> >>> title('Nonparametric: significant with cluster multiple comparison
>> >>> correction')
>> >>>
>> >>>
>> >>>
>> >>> Do you have ANY idea to this? I am really completely helpless....
>> >>>
>> >>> thanks and best
>> >>>
>> >>> Katrin
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
>> --------------------------------------------------------------------------------
>> >>
>> >> Joseph Dien, PhD
>> >> Research Associate
>> >> Cognitive Neurology
>> >> The Johns Hopkins University School of Medicine
>> >>
>> >> Lab E-mail: jdien1 at jhmi.edu
>> >> Private E-mail: jdien07 at mac.com
>> >> Office Phone: 410-614-3115
>> >> Cell Phone: 202-297-8117
>> >> Fax: 410-955-0188
>> >> http://joedien.com
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> >
>> --------------------------------------------------------------------------------
>> >
>> > Joseph Dien, PhD
>> > Research Associate
>> > Cognitive Neurology
>> > The Johns Hopkins University School of Medicine
>> >
>> > Lab E-mail: jdien1 at jhmi.edu
>> > Private E-mail: jdien07 at mac.com
>> > Office Phone: 410-614-3115
>> > Cell Phone: 202-297-8117
>> > Fax: 410-955-0188
>> > http://joedien.com
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
>> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140908/81922ad2/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 4
>> > Date: Tue, 9 Sep 2014 06:37:13 +0000
>> > From: "Caspar, Emilie" <e.caspar at ucl.ac.uk>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] ft_rejectvisual: removing trials marked as
>> > bad
>> > Message-ID: <3C365782-4B07-4EFE-A921-99489BDD5DA6 at live.ucl.ac.uk>
>> > Content-Type: text/plain; charset="iso-8859-1"
>> >
>> > Hi Ashley,
>> >
>> > Maybe the mistake is you did not integrate in your script the
>> > command to save the rejection. Normally, with the following command,
>> > you will see in the Matlab Window that artifacts are correctly
>> > rejected after the "quit" button.
>> >
>> > clean_data = ft_rejectvisual(cfg, interpoldata);
>> > cfg.artfctdef.reject = 'complete';
>> > cfg.artfctdef.feedback = 'yes';
>> > cleandata = ft_rejectartifact(cfg,clean_data);
>> >
>> > Emilie
>> >
>> >
>> >
>> >
>> >
>> > Le 3 sept. 2014 ? 11:58, Ashley Greene
>> > <agreene24 at gmail.com<mailto:agreene24 at gmail.com>> a ?crit :
>> >
>> > Hello,
>> >
>> > I have used the rejectvisual function in attempt to remove bad
>> > trials, but after marking them and pressing the quit button,
>> > although the command window states that the selected trials have
>> > been removed, there is no obvious change in the data; all of the
>> > trials are still intact. Have I overlooked something?
>> >
>> > Thanks,
>> >
>> > Ashley
>> > _______________________________________________
>> > fieldtrip mailing list
>> > fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
>> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/4c2d0985/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 5
>> > Date: Tue, 09 Sep 2014 14:16:20 +0200
>> > From: Patricia Wollstadt <Patricia.Wollstadt at gmx.de>
>> > To: fieldtrip at science.ru.nl
>> > Subject: Re: [FieldTrip] TRENTOOL pipeline help
>> > Message-ID: <540EEF94.9080601 at gmx.de>
>> > Content-Type: text/plain; charset="windows-1252"
>> >
>> > Hello Max,
>> >
>> > I added a few comments to the questions regarding individual
>> > parameters
>> > below. To address the general problem of TRENTOOL telling you, that
>> > there are not enough sample points in your data: From what I can see
>> > in
>> > your script, you probably don't have enough data points in each time
>> > series to robustly estimate TE. You analyze 800 ms of data sampled
>> > at
>> > 300 Hz, which gives you 240 samples per time series. Can you maybe
>> > avoid
>> > downsampling to 300 Hz and downsample to 600 Hz instead? Or could
>> > you
>> > analyze a longer time window of interest?
>> > Note that you also 'lose' data to embedding and the interaction
>> > delay:
>> > The first point that can be used for TE estimation is at max.
>> > embedding
>> > length + max. interaction delay in samples. For example: max.
>> > embedding
>> > length = dim * tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the
>> > max
>> > interaction delay of 30 ms = 9 samples. In this example, you would
>> > be
>> > left with 240 - 29 samples for TE estimation per trial. There is
>> > also
>> > the possibility to estimate time resolved TE/TE for shorter time
>> > windows
>> > of interest (see section 4.4 in the manual); however, this method
>> > requires the use of a GPU for TE estimation.
>> >
>> > I would further recommend to use the new pipeline for group
>> > statistics
>> > described in the manual in section 4.5 (the function
>> > 'TEgroup_calculate'
>> > is deprecated). The new pipeline allows you to reconstruct the
>> > interaction delay and uses the following functions (see also
>> > comments in
>> > the script):
>> >
>> > TEgroup_prepare -> prepares all data sets (all subjects/all
>> > conditions) for group analysis (this means finding common embedding
>> > parameters such that estimates are not biased between groups)
>> > InteractionDelayReconstruction_calculate -> estimates TE for
>> > individual data sets and all assumed interaction delays u
>> > InteractionDelayReconstruction_analyze -> reconstructs the
>> > interaction delay by selecting the u that maximizes TE for each
>> > channel
>> > TEgroup_stats -> calculate group statistics using a permutation test
>> >
>> > I can send you an example script for group TE analysis using this
>> > pipeline to get you started. I hope this helps you to get the group
>> > analysis running. Just write again if you're having trouble setting
>> > up
>> > the pipeline or something is not clear about the parameters/my
>> > comments.
>> >
>> > Best,
>> > Patricia
>> >
>> >
>> >
>> >
>> > On 09/04/2014 08:30 PM, Max Cantor wrote:
>> >> Hi fieldtrippers,
>> >>
>> >> I know trentool is not produced by the Donders Institute, so I'm
>> >> not
>> >> 100% sure if it is appropriate to ask questions about it here, but
>> >> to
>> >> the best of my knowledge they do not have a mailing list and I saw
>> >> a
>> >> few trentool questions in the archives, so I'm going to assume it's
>> >> ok...
>> >>
>> >> In any case, below is my current pipeline (slightly modified for
>> >> comprehensibility):
>> >>
>> >> (notes in bold are comments/questions made in this email, not
>> >> present
>> >> in the pipeline. Sorry in advance for the long post! Any help would
>> >> be
>> >> greatly appreciated as I'm a bit over my head on this but I think
>> >> I'm
>> >> close!)
>> >>
>> >> *****
>> >>
>> >> % Prepare group TE data
>> >>
>> >> cfgP = [];
>> >> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> >> cfgP.TEcalctype = 'VW_ds';
>> >> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>> >>
>> >> *I'm trying to find the transfer entropy between the left and right
>> >> auditory cortices in my experiment. The input is virtual sensor
>> >> data
>> >> that was produced using SAM in fieldtrip on real MEG data. *
>> >>
>> >> % specify u to be scanned
>> >>
>> >> cfgP.predicttime_u = 30;
>> >> cfgP.toi = [-0.4 0.4];
>> >>
>> >> *For clarification, the predicttime_u is in seconds but the toi is
>> >> in
>> >> milliseconds. If I understand correctly, the predicttime_u must fit
>> >> within the toi, but beyond that are there any benefits to it being
>> >> earlier or later?* PW: The predictiontime_u is in milliseconds and
>> >> the
>> >> toi is in seconds. The prediction time is the assumed interaction
>> >> delay between your two sources and should fit within your toi. In
>> >> general it is preferable to use the method for interaction delay
>> >> reconstruction for TE estimation, because it allows you to
>> >> reconstruct
>> >> the actual delay between your source and target times series. A
>> >> non-optimal u/interaction delay may cause an underestimation of TE,
>> >> so
>> >> it is recommended to use the pipeline for interaction delay
>> >> reconstruction whenever estimating TE for unknown delays.
>> >> If you use the methods for interaction delay reconstruction
>> >> 'predicttime_u' is replaced by
>> >> cfgTEP.predicttimemin_u % minimum u to be scanned
>> >> cfgTEP.predicttimemax_u % maximum u to be scanned
>> >> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> >> A large range for u values to be scanned increases computing time a
>> >> lot, so it is best to limit the u range to values that are
>> >> physiologically plausible.
>> >>
>> >> % ACT (Autocorrelation Time) estimation and constraints
>> >>
>> >> cfgP.maxlag = 150;
>> >> cfgP.actthrvalue = 7.5;
>> >> cfgP.minnrtrials = 5;
>> >>
>> >> *My understanding is maxlag should be 1/2 the sampling rate, so
>> >> since
>> >> the data are downsampled to 300hz, it should be 150. I know that
>> >> the
>> >> sample rate and filters are used to determine the actthrvalue, but
>> >> I
>> >> don't actually know the calculation. 7.5 was a rough guess just to
>> >> test the pipeline. I'm also uncertain of what minnrtrials should
>> >> be.*
>> >> PW: You can set the actthrvalue based on the filtering you did
>> >> prior
>> >> to TE analysis. If you for example highpass filtered at 10 Hz, you
>> >> shouldn't find an ACT higher than 30 samples, because you filtered
>> >> out
>> >> any components of the signal slower than 10 Hz/30 samples (given
>> >> your
>> >> sampling frequency of 300 Hz). So in this scenario the actthrvalue
>> >> would be 30.
>> >> A good value for cfgP.minnrtrials is 12 (a minimum number of trials
>> >> is
>> >> needed to realize the permutation test for estimated TE values).
>> >>
>> >> % Optimization
>> >>
>> >> cfgP.optimizemethod = 'ragwitz';
>> >> cfgP.ragdim = 4:8;
>> >> cfgP.ragtaurange = [0.2 0.4];
>> >> cfgP.ragtausteps = 15;
>> >> cfgP.repPred = 100;
>> >> *
>> >> *
>> >> *I am completely at a loss for this. I've done some reading into
>> >> transfer entropy, mutual information, etc., cited in trentool, but
>> >> I'm
>> >> yet to understand how exactly this optimization works and what the
>> >> configuration should be, given my data and experimental
>> >> intentions.*
>> >> PW: The Ragwitz criterion tries to find optimal embedding
>> >> parameters
>> >> dim and tau for the data. To do that, the method iteratively takes
>> >> all
>> >> possible combinations of dim and tau values that are provided in
>> >> cfgP.ragdim and cfgP.ragtaurange/.ragtausteps and tests how well
>> >> these
>> >> combinations embed the data. To test an embedding, the method
>> >> builds
>> >> the embedding vectors from the data; it then tests for each point
>> >> how
>> >> well the next point in time can be predicted from the reference
>> >> point's nearest neighbours. So for each embedded point, the method
>> >> searches for the nearest neighbours and calculates the average of
>> >> those nearest neighbours. The difference between the
>> >> averaged/predicted point and the actual next point is the error of
>> >> the
>> >> local predictor. The Ragwitz criterion will then return the
>> >> parameter
>> >> combination for which this error over all points is minimal.
>> >> The parameters set the following: 'ragdim' are dimensions to be
>> >> tested
>> >> by the method (I would reccomend to start with 2:10), 'ragtaurange'
>> >> together with 'ragtausteps' specifies the tau values to be tested
>> >> (TRENTOOL will build a vector from 0.2 to 0.4 in 15 steps). Note,
>> >> that
>> >> the values here are factors that are later multiplied with the ACT
>> >> to
>> >> obtain the actual tau. 'repPred' is the number of points that will
>> >> be
>> >> used for the local prediction, i.e. the Ragwitz criterion will test
>> >> the local prediction and calculate the error for the first 100
>> >> points
>> >> in your time series. The two parameters 'flagNei' ans 'sizeNei'
>> >> below
>> >> specify the type of neighbour search conducted by the Ragwitz
>> >> criterion: 'flagNei' tells the method to either conduct a kNN or
>> >> range
>> >> search; 'sizeNei' specifies the number of neighbours or the radius
>> >> to
>> >> be searched by a range search.
>> >>
>> >>
>> >> % Kernel-based TE estimation
>> >>
>> >> cfgP.flagNei = 'Mass';
>> >> cfgP.sizeNei = 4; % Default
>> >>
>> >> cfgP.ensemblemethod = 'no';
>> >> cfgP.outputpath = *OUTPUT PATH*;
>> >>
>> >> if ~exist(*Path for TEprepare data object*)
>> >> load VSdat;
>> >> TE_Wrd = {};
>> >> for i = 1:nConds
>> >> for j = 1:Nsub
>> >> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> >> end
>> >> end
>> >> clear VSdat;
>> >> save('TE_Wrd', 'TE_Wrd');
>> >> end
>> >> *
>> >> *
>> >> *The configuration and virtual sensor data, organized in a 3 x 15
>> >> cell
>> >> of structures (condition by subject) are the input. The TEprepare
>> >> substructure is added to each individual condition x subject .mat
>> >> files' data structure which are stored on disk independently.*
>> >>
>> >> % Use object_to_mat_conversion.m to replace individual condition x
>> >> subject virtual sensor data
>> >> % .mat files with their TE_Wrd equivalent
>> >> *
>> >> *
>> >> *I'm using a separate script to make some manipulations to the
>> >> objects
>> >> from disk; this will all eventually be integrated into the main
>> >> pipeline*.*TRENTOOL seems to handle data output very differently
>> >> from
>> >> fieldtrip and I've had trouble thinking through the most logical
>> >> way
>> >> to handle the data so it's a bit haphazard right now.*
>> >>
>> >> load cond080sub01.mat
>> >>
>> >> cfgG = [];
>> >> cfgG.dim = cond080sub01.TEprepare.optdim;
>> >> cfgG.tau = cond080sub01.TEprepare.opttau;
>> >>
>> >> if isfield(cond080sub01, 'TEprepare')
>> >> TEgroup_prepare(cfgG, fileCell);
>> >> else
>> >> error('Need to run TEprepare before TEgroup_prepare');
>> >> end
>> >> *
>> >> *
>> >> *For clarification, fileCell is a cell with the name of each
>> >> condition
>> >> x subject .mat file, which as I said before is collectively the
>> >> same
>> >> as the 3 x 15 VSdat structure (condition x subject).*
>> >>
>> >> % Replace .mat files with '_for_TEgroup_calculate' version in
>> >> % object_to_mat_conversion.m
>> >>
>> >> % TE Group Calculate
>> >>
>> >> load cond080sub01.mat
>> >> if isfield(cond080sub01, 'TEgroupprepare')
>> >> for i = 1:length(fileCell)
>> >> TEgroup_calculate(fileCell{i});
>> >> end
>> >> else
>> >> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> >> end
>> >> *
>> >> *
>> >> *At this step I get the following error:
>> >>
>> >> Error using transferentropy (line 337)
>> >> \nTRENTOOL ERROR: not enough data points left after embedding
>> >>
>> >> Error in TEgroup_calculate (line 133)
>> >> [TEresult] = transferentropy(cfg,data);*
>> >>
>> >> % TE Group Stats
>> >>
>> >> cfgGSTAT = [];
>> >> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> >> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>> >>
>> >> cfgGSTAT.uvar = 1;
>> >> cfgGSTAT.ivar = 2;
>> >> cfgGSTAT.fileidout = 'test_groupstats';
>> >>
>> >> TEgroup_stats(cfgGSTAT, fileCell);
>> >>
>> >> *Given the error above, I am yet to get to this step, but it does
>> >> not
>> >> seem fundamentally different from normal fieldtrip stats.*
>> >>
>> >> *****
>> >>
>> >> In case my notes were not clear or you skipped to the bottom, *my
>> >> primary concern is whether the error I'm getting in
>> >> TEgroup_calculate
>> >> is a pipeline issue* (I noticed the example pipeline in trentool,
>> >> the
>> >> manual, and published methods articles all seem to have slightly or
>> >> significantly different pipeline compositions), *or if the error
>> >> is*
>> >> due to ACT, ragwitz optimization, or some other faulty
>> >> parameterization *on my part due to a lack of understanding of how
>> >> transfer entropy works on a more theoretical/mathematical level*.
>> >> If
>> >> the latter is the case, is there any relatively straightforward way
>> >> to
>> >> conceptualize this, or is this something where I'm just going to
>> >> have
>> >> to keep reading and rereading until it eventually makes sense? I've
>> >> already done quite a bit of that and it hasn't pierced my thick
>> >> skull
>> >> yet but I'm sure it will eventually!
>> >>
>> >> Thank you so much,
>> >>
>> >> Max Cantor
>> >>
>> >>
>> >> --
>> >> Max Cantor
>> >> Lab Manager
>> >> Computational Neurolinguistics Lab
>> >> University of Michigan
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > --
>> > ------------------------------------------------------------
>> >
>> > Patricia Wollstadt, PhD Student
>> >
>> > MEG Unit, Brain Imaging Center
>> >
>> > Goethe University, Frankfurt, Germany
>> >
>> > Heinrich Hoffmann Strasse 10, Haus 93 B
>> >
>> > D - 60528 Frankfurt am Main
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
>> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/ce6b297f/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 6
>> > Date: Tue, 9 Sep 2014 15:21:20 +0200
>> > From: Holger Krause <Holger.Krause at med.uni-duesseldorf.de>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: [FieldTrip] Latest FT version on ftp-server is from three
>> > days ago
>> > Message-ID:
>> > <201409091521.22589.Holger.Krause at med.uni-duesseldorf.de>
>> > Content-Type: text/plain; charset="us-ascii"
>> >
>> > Dear developers,
>> >
>> > Could you please check the process for creation/publication of
>> > zip-files on
>> > the FTP server? The latest file I can find there is
>> > fieldtrip-20140906.zip.
>> >
>> > Thanks in advance,
>> >
>> > Holger
>> >
>> >
>> > ------------------------------
>> >
>> > Message: 7
>> > Date: Tue, 9 Sep 2014 09:19:55 -0400
>> > From: Max Cantor <mcantor at umich.edu>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] TRENTOOL pipeline help
>> > Message-ID:
>> > <CAFTjRaUXzwca7rR3sm8iPLLyUxqHO9kkm++=zAU4LUW=r7NRBw at mail.gmail.com>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > This is immensely helpful, thank you. I was very confused about why
>> > some
>> > versions of the pipeline I saw were using group calculate and others
>> > were
>> > using interaction delay reconstruction and what that meant, and I
>> > think I
>> > have a more clear idea of what the different steps of the pipeline
>> > are
>> > doing. There are still a few things I'm a bit confused about though
>> > in
>> > terms of the pipeline. For instance, whether or not I need to do
>> > TEprepare
>> > before group prepare, and if I need to do graph analysis (which I'm
>> > not
>> > sure I fully understand but also haven't looked deeply into) before
>> > group
>> > stats.
>> >
>> > If you don't mind me taking you up on your offer, I think seeing
>> > your
>> > example script might help clarify some of these issues.
>> >
>> > Thank you!
>> >
>> > On Tue, Sep 9, 2014 at 8:16 AM, Patricia Wollstadt <
>> > Patricia.Wollstadt at gmx.de> wrote:
>> >
>> >> Hello Max,
>> >>
>> >> I added a few comments to the questions regarding individual
>> >> parameters
>> >> below. To address the general problem of TRENTOOL telling you, that
>> >> there
>> >> are not enough sample points in your data: From what I can see in
>> >> your
>> >> script, you probably don't have enough data points in each time
>> >> series to
>> >> robustly estimate TE. You analyze 800 ms of data sampled at 300 Hz,
>> >> which
>> >> gives you 240 samples per time series. Can you maybe avoid
>> >> downsampling to
>> >> 300 Hz and downsample to 600 Hz instead? Or could you analyze a
>> >> longer time
>> >> window of interest?
>> >> Note that you also 'lose' data to embedding and the interaction
>> >> delay: The
>> >> first point that can be used for TE estimation is at max. embedding
>> >> length
>> >> + max. interaction delay in samples. For example: max. embedding
>> >> length =
>> >> dim * tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the max
>> >> interaction
>> >> delay of 30 ms = 9 samples. In this example, you would be left with
>> >> 240 -
>> >> 29 samples for TE estimation per trial. There is also the
>> >> possibility to
>> >> estimate time resolved TE/TE for shorter time windows of interest
>> >> (see
>> >> section 4.4 in the manual); however, this method requires the use
>> >> of a GPU
>> >> for TE estimation.
>> >>
>> >> I would further recommend to use the new pipeline for group
>> >> statistics
>> >> described in the manual in section 4.5 (the function
>> >> 'TEgroup_calculate' is
>> >> deprecated). The new pipeline allows you to reconstruct the
>> >> interaction
>> >> delay and uses the following functions (see also comments in the
>> >> script):
>> >>
>> >> TEgroup_prepare -> prepares all data sets (all subjects/all
>> >> conditions)
>> >> for group analysis (this means finding common embedding parameters
>> >> such
>> >> that estimates are not biased between groups)
>> >> InteractionDelayReconstruction_calculate -> estimates TE for
>> >> individual data sets and all assumed interaction delays u
>> >> InteractionDelayReconstruction_analyze -> reconstructs the
>> >> interaction delay by selecting the u that maximizes TE for each
>> >> channel
>> >> TEgroup_stats -> calculate group statistics using a permutation
>> >> test
>> >>
>> >> I can send you an example script for group TE analysis using this
>> >> pipeline
>> >> to get you started. I hope this helps you to get the group analysis
>> >> running. Just write again if you're having trouble setting up the
>> >> pipeline
>> >> or something is not clear about the parameters/my comments.
>> >>
>> >> Best,
>> >> Patricia
>> >>
>> >>
>> >>
>> >>
>> >> On 09/04/2014 08:30 PM, Max Cantor wrote:
>> >>
>> >> Hi fieldtrippers,
>> >>
>> >> I know trentool is not produced by the Donders Institute, so I'm
>> >> not 100%
>> >> sure if it is appropriate to ask questions about it here, but to
>> >> the best
>> >> of my knowledge they do not have a mailing list and I saw a few
>> >> trentool
>> >> questions in the archives, so I'm going to assume it's ok...
>> >>
>> >> In any case, below is my current pipeline (slightly modified for
>> >> comprehensibility):
>> >>
>> >> (notes in bold are comments/questions made in this email, not
>> >> present in
>> >> the pipeline. Sorry in advance for the long post! Any help would be
>> >> greatly
>> >> appreciated as I'm a bit over my head on this but I think I'm
>> >> close!)
>> >>
>> >> *****
>> >>
>> >> % Prepare group TE data
>> >>
>> >> cfgP = [];
>> >> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> >> cfgP.TEcalctype = 'VW_ds';
>> >> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>> >>
>> >> *I'm trying to find the transfer entropy between the left and right
>> >> auditory cortices in my experiment. The input is virtual sensor
>> >> data that
>> >> was produced using SAM in fieldtrip on real MEG data. *
>> >>
>> >> % specify u to be scanned
>> >>
>> >> cfgP.predicttime_u = 30;
>> >> cfgP.toi = [-0.4 0.4];
>> >>
>> >> *For clarification, the predicttime_u is in seconds but the toi
>> >> is in
>> >> milliseconds. If I understand correctly, the predicttime_u must fit
>> >> within
>> >> the toi, but beyond that are there any benefits to it being earlier
>> >> or
>> >> later?* PW: The predictiontime_u is in milliseconds and the toi is
>> >> in
>> >> seconds. The prediction time is the assumed interaction delay
>> >> between your
>> >> two sources and should fit within your toi. In general it is
>> >> preferable to
>> >> use the method for interaction delay reconstruction for TE
>> >> estimation,
>> >> because it allows you to reconstruct the actual delay between your
>> >> source
>> >> and target times series. A non-optimal u/interaction delay may
>> >> cause an
>> >> underestimation of TE, so it is recommended to use the pipeline for
>> >> interaction delay reconstruction whenever estimating TE for unknown
>> >> delays.
>> >> If you use the methods for interaction delay reconstruction
>> >> 'predicttime_u' is replaced by
>> >> cfgTEP.predicttimemin_u % minimum u to be scanned
>> >> cfgTEP.predicttimemax_u % maximum u to be scanned
>> >> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> >> A large range for u values to be scanned increases computing time a
>> >> lot,
>> >> so it is best to limit the u range to values that are
>> >> physiologically
>> >> plausible.
>> >>
>> >>
>> >> % ACT (Autocorrelation Time) estimation and constraints
>> >>
>> >> cfgP.maxlag = 150;
>> >> cfgP.actthrvalue = 7.5;
>> >> cfgP.minnrtrials = 5;
>> >>
>> >> *My understanding is maxlag should be 1/2 the sampling rate, so
>> >> since
>> >> the data are downsampled to 300hz, it should be 150. I know that
>> >> the sample
>> >> rate and filters are used to determine the actthrvalue, but I don't
>> >> actually know the calculation. 7.5 was a rough guess just to test
>> >> the
>> >> pipeline. I'm also uncertain of what minnrtrials should be.* PW:
>> >> You can
>> >> set the actthrvalue based on the filtering you did prior to TE
>> >> analysis. If
>> >> you for example highpass filtered at 10 Hz, you shouldn't find an
>> >> ACT
>> >> higher than 30 samples, because you filtered out any components of
>> >> the
>> >> signal slower than 10 Hz/30 samples (given your sampling frequency
>> >> of 300
>> >> Hz). So in this scenario the actthrvalue would be 30.
>> >> A good value for cfgP.minnrtrials is 12 (a minimum number of trials
>> >> is
>> >> needed to realize the permutation test for estimated TE values).
>> >>
>> >>
>> >> % Optimization
>> >>
>> >> cfgP.optimizemethod = 'ragwitz';
>> >> cfgP.ragdim = 4:8;
>> >> cfgP.ragtaurange = [0.2 0.4];
>> >> cfgP.ragtausteps = 15;
>> >> cfgP.repPred = 100;
>> >>
>> >> *I am completely at a loss for this. I've done some reading into
>> >> transfer entropy, mutual information, etc., cited in trentool, but
>> >> I'm yet
>> >> to understand how exactly this optimization works and what the
>> >> configuration should be, given my data and experimental
>> >> intentions.* PW:
>> >> The Ragwitz criterion tries to find optimal embedding parameters
>> >> dim and
>> >> tau for the data. To do that, the method iteratively takes all
>> >> possible
>> >> combinations of dim and tau values that are provided in cfgP.ragdim
>> >> and
>> >> cfgP.ragtaurange/.ragtausteps and tests how well these combinations
>> >> embed
>> >> the data. To test an embedding, the method builds the embedding
>> >> vectors
>> >> from the data; it then tests for each point how well the next point
>> >> in time
>> >> can be predicted from the reference point's nearest neighbours. So
>> >> for each
>> >> embedded point, the method searches for the nearest neighbours and
>> >> calculates the average of those nearest neighbours. The difference
>> >> between
>> >> the averaged/predicted point and the actual next point is the error
>> >> of the
>> >> local predictor. The Ragwitz criterion will then return the
>> >> parameter
>> >> combination for which this error over all points is minimal.
>> >> The parameters set the following: 'ragdim' are dimensions to be
>> >> tested by
>> >> the method (I would reccomend to start with 2:10), 'ragtaurange'
>> >> together
>> >> with 'ragtausteps' specifies the tau values to be tested (TRENTOOL
>> >> will
>> >> build a vector from 0.2 to 0.4 in 15 steps). Note, that the values
>> >> here are
>> >> factors that are later multiplied with the ACT to obtain the actual
>> >> tau.
>> >> 'repPred' is the number of points that will be used for the local
>> >> prediction, i.e. the Ragwitz criterion will test the local
>> >> prediction and
>> >> calculate the error for the first 100 points in your time series.
>> >> The two
>> >> parameters 'flagNei' ans 'sizeNei' below specify the type of
>> >> neighbour
>> >> search conducted by the Ragwitz criterion: 'flagNei' tells the
>> >> method to
>> >> either conduct a kNN or range search; 'sizeNei' specifies the
>> >> number of
>> >> neighbours or the radius to be searched by a range search.
>> >>
>> >>
>> >> % Kernel-based TE estimation
>> >>
>> >> cfgP.flagNei = 'Mass';
>> >> cfgP.sizeNei = 4; % Default
>> >>
>> >> cfgP.ensemblemethod = 'no';
>> >> cfgP.outputpath = *OUTPUT PATH*;
>> >>
>> >> if ~exist(*Path for TEprepare data object*)
>> >> load VSdat;
>> >> TE_Wrd = {};
>> >> for i = 1:nConds
>> >> for j = 1:Nsub
>> >> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> >> end
>> >> end
>> >> clear VSdat;
>> >> save('TE_Wrd', 'TE_Wrd');
>> >> end
>> >>
>> >> *The configuration and virtual sensor data, organized in a 3 x 15
>> >> cell
>> >> of structures (condition by subject) are the input. The TEprepare
>> >> substructure is added to each individual condition x subject .mat
>> >> files'
>> >> data structure which are stored on disk independently.*
>> >>
>> >> % Use object_to_mat_conversion.m to replace individual condition x
>> >> subject
>> >> virtual sensor data
>> >> % .mat files with their TE_Wrd equivalent
>> >>
>> >> *I'm using a separate script to make some manipulations to the
>> >> objects
>> >> from disk; this will all eventually be integrated into the main
>> >> pipeline*.*
>> >> TRENTOOL seems to handle data output very differently from
>> >> fieldtrip and
>> >> I've had trouble thinking through the most logical way to handle
>> >> the data
>> >> so it's a bit haphazard right now.*
>> >>
>> >> load cond080sub01.mat
>> >>
>> >> cfgG = [];
>> >> cfgG.dim = cond080sub01.TEprepare.optdim;
>> >> cfgG.tau = cond080sub01.TEprepare.opttau;
>> >>
>> >> if isfield(cond080sub01, 'TEprepare')
>> >> TEgroup_prepare(cfgG, fileCell);
>> >> else
>> >> error('Need to run TEprepare before TEgroup_prepare');
>> >> end
>> >>
>> >> *For clarification, fileCell is a cell with the name of each
>> >> condition x
>> >> subject .mat file, which as I said before is collectively the same
>> >> as the 3
>> >> x 15 VSdat structure (condition x subject).*
>> >>
>> >> % Replace .mat files with '_for_TEgroup_calculate' version in
>> >> % object_to_mat_conversion.m
>> >>
>> >> % TE Group Calculate
>> >>
>> >> load cond080sub01.mat
>> >> if isfield(cond080sub01, 'TEgroupprepare')
>> >> for i = 1:length(fileCell)
>> >> TEgroup_calculate(fileCell{i});
>> >> end
>> >> else
>> >> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> >> end
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> *At this step I get the following error: Error using
>> >> transferentropy (line
>> >> 337) \nTRENTOOL ERROR: not enough data points left after embedding
>> >> Error in
>> >> TEgroup_calculate (line 133) [TEresult] =
>> >> transferentropy(cfg,data);*
>> >>
>> >> % TE Group Stats
>> >>
>> >> cfgGSTAT = [];
>> >> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> >> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>> >>
>> >> cfgGSTAT.uvar = 1;
>> >> cfgGSTAT.ivar = 2;
>> >> cfgGSTAT.fileidout = 'test_groupstats';
>> >>
>> >> TEgroup_stats(cfgGSTAT, fileCell);
>> >>
>> >> *Given the error above, I am yet to get to this step, but it does
>> >> not
>> >> seem fundamentally different from normal fieldtrip stats.*
>> >>
>> >> *****
>> >>
>> >> In case my notes were not clear or you skipped to the bottom, *my
>> >> primary concern is whether the error I'm getting in
>> >> TEgroup_calculate is a
>> >> pipeline issue* (I noticed the example pipeline in trentool, the
>> >> manual,
>> >> and published methods articles all seem to have slightly or
>> >> significantly
>> >> different pipeline compositions), *or if the error is* due to ACT,
>> >> ragwitz optimization, or some other faulty parameterization *on my
>> >> part
>> >> due to a lack of understanding of how transfer entropy works on a
>> >> more
>> >> theoretical/mathematical level*. If the latter is the case, is
>> >> there any
>> >> relatively straightforward way to conceptualize this, or is this
>> >> something
>> >> where I'm just going to have to keep reading and rereading until it
>> >> eventually makes sense? I've already done quite a bit of that and
>> >> it hasn't
>> >> pierced my thick skull yet but I'm sure it will eventually!
>> >>
>> >> Thank you so much,
>> >>
>> >> Max Cantor
>> >>
>> >>
>> >> --
>> >> Max Cantor
>> >> Lab Manager
>> >> Computational Neurolinguistics Lab
>> >> University of Michigan
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing
>> >>
>> listfieldtrip at donders.ru.nlhttp://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
>> >> --
>> >> ------------------------------------------------------------
>> >>
>> >> Patricia Wollstadt, PhD Student
>> >>
>> >> MEG Unit, Brain Imaging Center Goethe University, Frankfurt,
>> >> Germany
>> >>
>> >> Heinrich Hoffmann Strasse 10, Haus 93 B D - 60528 Frankfurt am
>> >> Main
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >
>> >
>> >
>> > --
>> > Max Cantor
>> > Lab Manager
>> > Computational Neurolinguistics Lab
>> > University of Michigan
>> > -------------- next part --------------
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>> >
>> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/29e18024/attachment.html>
>> >
>> > ------------------------------
>> >
>> > _______________________________________________
>> > fieldtrip mailing list
>> > fieldtrip at donders.ru.nl
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > End of fieldtrip Digest, Vol 46, Issue 3
>> > ****************************************
>> >
>>
>>
>>
>> Barbara Schorr, MSc
>> Clinical and Biological Psychology
>> University of Ulm
>> Albert-Einstein-Allee 47
>> 89069 Ulm
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> --
> Jim Herring, MSc.
> Neuronal Oscillations Group
> Centre for Cognitive Neuroimaging
> Donders Institute for Brain, Cognition and Behaviour
> Radboud University Nijmegen
>
>
> ------------------------------
>
> Message: 4
> Date: Mon, 15 Sep 2014 12:09:24 +0200 (CEST)
> From: Fr?d?ric Roux <f.roux at bcbl.eu>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: [FieldTrip] using ft_megplanar with sinle-trial TFRs
> Message-ID: <867006743.3066178.1410775764568.JavaMail.root at bcbl.eu>
> Content-Type: text/plain; charset=utf-8
>
> Dear all,
>
> my question relates to the computation of combined planar gradients
> using the ft_combineplanar function.
>
> Our data has been recorded with an Elekta Neuromag 306 channel system,
> and I would like to know if it is "safe" to compute combined gradients
> before calling ft_freqstatistics, if the input data contains single
> trial TFRs.
>
> The reason why I am asking is because in the documentation of the
> function it says
> % Use as
> % [data] = ft_combineplanar(cfg, data)
> % where data contains an averaged planar gradient (either ERF or TFR).
>
> However, the following tutorial suggests that this can be safely applied if
> the input data contains TFRs which have been computed with the
> cfg.keeptrials = 'yes' option:
> http://fieldtrip.fcdonders.nl/tutorial/cluster_permutation_freq
>
> I am bit confused as both information (the fieldtrip documentation
> and the tutorial)
> seem to contradict each other.
>
> The 2 options that are see are:
>
> 1) compute TFRs with cfg.keeptrials = 'yes', then call
> ft_frestatistics, then call ft_combineplanar
> by using the output of ft_freqstatistics as input for the
> ft_combineplanar function.
> 2) compute TFRs with cfg.keeptrials = 'yes', then call
> ft_combineplanar, then call ft_freqstatistics
>
> Could anyone with experience on this issue please let me know what
> the best way
> to proceed would be?
>
> Fred
> ---------------------------------------------------------------------------
>
>
>
>
> ------------------------------
>
> Message: 5
> Date: Mon, 15 Sep 2014 13:12:38 -0300
> From: gamaliel huerta urrea <gamaliel.ghu at gmail.com>
> To: fieldtrip at science.ru.nl
> Subject: [FieldTrip] dipole simulation on head model
> Message-ID:
> <CAPEf3EPXER4dMmY+BhrAO_S_+0mf_GZ8A335xydBMmTza5Y9+Q at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> hi all
>
> I hope you can help my problem.
>
> I have generated a model of head fieldtrip, and now I need to simulate
> sources at different positions within the crust generated. I followed the
> tutorial head model generation for EEG. However I have not found
> information on how to simulate a source. Any help would be welcome.
>
> Thank you
> greetings
>
> --
> *Gamaliel Huerta*
> *Ingenier?a Civil Biom?dica*
> *Universidad de Valpara?so*
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>
> ------------------------------
>
> Message: 6
> Date: Mon, 15 Sep 2014 18:38:26 +0200
> From: Tzvetan Popov <tzvetan.popov at uni-konstanz.de>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] dipole simulation on head model
> Message-ID: <6677301D-D72C-4C12-BC84-779D5E4DF8F3 at uni-konstanz.de>
> Content-Type: text/plain; charset="iso-8859-1"
>
>
> Dear Gamaliel,
> you might try something like this:
>
> http://fieldtrip.fcdonders.nl/example/compute_forward_simulated_data_and_apply_a_dipole_fit
>
> good luck
> tzvetan
>
> Am 15.09.2014 um 18:12 schrieb gamaliel huerta urrea
> <gamaliel.ghu at gmail.com>:
>
>> hi all
>>
>> I hope you can help my problem.
>>
>> I have generated a model of head fieldtrip, and now I need to
>> simulate sources at different positions within the crust generated.
>> I followed the tutorial head model generation for EEG. However I
>> have not found information on how to simulate a source. Any help
>> would be welcome.
>>
>> Thank you
>> greetings
>>
>> --
>> Gamaliel Huerta
>> Ingenier?a Civil Biom?dica
>> Universidad de Valpara?so
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL:
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140915/1325eec8/attachment-0001.html>
>
> ------------------------------
>
> Message: 7
> Date: Mon, 15 Sep 2014 12:40:41 -0500
> From: "Jing Fan" <jingfan.jf at gmail.com>
> To: "'FieldTrip discussion list'" <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] ft_globalmeanfield : Undefined function of
> variable 'abort'
> Message-ID: <000001cfd10c$2b7786a0$826693e0$@gmail.com>
> Content-Type: text/plain; charset="us-ascii"
>
> Hi Jim,
>
> I download the latest .zip file from FTP.
> I received the same error message when calling function ft_definetrial(cfg).
> It seems to me that ft_preamble init command will go to script ft_preamble.m
> instead of ft_preamble_init.m.
> Any help to resolve this issue will be appreciated.
>
> Best,
> Jing
>
>
>
> -----Original Message-----
> From: fieldtrip-bounces at science.ru.nl
> [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Herring, J.D. (Jim)
> Sent: Monday, September 15, 2014 5:01 AM
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] ft_globalmeanfield : Undefined function of variable
> 'abort'
>
> Dear Barbara,
>
> The error seems to be related to a bug that pops up now and then
> (http://bugzilla.fcdonders.nl/show_bug.cgi?id=2580) which could be due to a
> corrupt installation of fieldtrip and/or the need to update fieldtrip.
>
> Could you please try downloading the most recent version of fieldtrip and
> see if the problem persists?
>
> For an example of what can be expected from this function see: Esser SK,
> Huber R, Massimini M, Peterson MJ, Ferrarelli F, Tononi G. A direct
> demonstration of cortical LTP in humans: a combined TMS/EEG study. Brain Res
> Bull. 2006 Mar 15;69(1):86-94. Epub 2005 Dec 1. PubMed PMID: 16464689.
>
> Best,
>
> Jim
>
>
> ----- Oorspronkelijk bericht -----
>> Van: "barbara schorr" <barbara.schorr at uni-ulm.de>
>> Aan: fieldtrip at science.ru.nl
>> Verzonden: Woensdag 10 september 2014 14:07:14
>> Onderwerp: [FieldTrip] ft_globalmeanfield : Undefined function of variable
> 'abort'
>> Dear fieldtripers.
>>
>> as mentioned in the subject line I would like to anaylse my timelocked
>> ERP Data with the function ft_globalmeanfield.
>>
>> I get the Error: Undefined function or variable 'abort'
>> Error in ft_globalmeanfield (line84)
>> if abort
>>
>> in the function it says:
>>
>> %the abort variable is set to true or false in ft_preamble_inti
>> if abort
>> % do not continue function execution in case the outputfile is
>> present and the user indicated to keep it
>> return
>> end
>>
>>
>> I don't understand what this means. I would really appreciate your
>> help to get this running.
>>
>> Also, what is the result when I run this function on my data? (This
>> method was proposed from a reviewer. I never used it before, so I
>> don't exactly know what it does and what the result will look like).
>>
>> Thank you very much in advance!
>>
>> Best,
>>
>> Barbara Schorr
>>
>>
>>
>>
>> Zitat von fieldtrip-request at science.ru.nl:
>>
>> > Send fieldtrip mailing list submissions to
>> > fieldtrip at science.ru.nl
>> >
>> > To subscribe or unsubscribe via the World Wide Web, visit
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> > or, via email, send a message with subject or body 'help' to
>> > fieldtrip-request at science.ru.nl
>> >
>> > You can reach the person managing the list at
>> > fieldtrip-owner at science.ru.nl
>> >
>> > When replying, please edit your Subject line so it is more specific
>> > than "Re: Contents of fieldtrip digest..."
>> >
>> >
>> > Today's Topics:
>> >
>> > 1. Re: Question regarding nonparametric testing for coherence
>> > differences (Eric Maris)
>> > 2. Re: Problem with Geodesic 129 layout!! (KatrinH Heimann)
>> > 3. Re: Problem with Geodesic 129 layout!! (Joseph Dien)
>> > 4. Re: ft_rejectvisual: removing trials marked as bad
>> > (Caspar, Emilie)
>> > 5. Re: TRENTOOL pipeline help (Patricia Wollstadt)
>> > 6. Latest FT version on ftp-server is from three days ago
>> > (Holger Krause)
>> > 7. Re: TRENTOOL pipeline help (Max Cantor)
>> >
>> >
>> > ----------------------------------------------------------------------
>> >
>> > Message: 1
>> > Date: Mon, 8 Sep 2014 15:46:11 +0200 (CEST)
>> > From: "Eric Maris" <e.maris at psych.ru.nl>
>> > To: <hweeling.lee at gmail.com>, "'FieldTrip discussion list'"
>> > <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Question regarding nonparametric testing
>> > for
>> > coherence differences
>> > Message-ID: <015901cfcb6b$3fdf1f00$bf9d5d00$@maris at psych.ru.nl>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > Dear Hwee Ling,
>> >
>> >
>> >
>> > In the paper you refer to (Maris et al, JNM, 2007), I did not
>> > perform a
>> > cluster-based permutation test at the level of the channel pairs.
>> > Instead,
>> > all coherences mentioned in the paper are coherence between a single
>> > EMG
>> > channel (2 electrodes mounted on the forearm) and all MEG
>> > electrodes. Thus,
>> > there are as many EMG-MEG coherences as there are MEG channels (275
>> > in our
>> > lab), and the clustering of these coherences involves the same
>> > neighbourhood
>> > structure as the clustering of channel-specific statistics on power
>> > or
>> > evoked response. The analysis you propose involves clustering in a
>> > much more
>> > complex space, namely the formed by all (MEG,MEG) channel pairs
>> > (275*275=75625 pairs). In the early days of cluster-based
>> > permutation
>> > statistics, I have worked on this problem but did not pursue it
>> > because the
>> > resulting clusters are very hard to interpret.
>> >
>> >
>> >
>> > Best,
>> >
>> >
>> >
>> > Eric Maris
>> >
>> >
>> >
>> >
>> >
>> > Dear all and Prof Maris,
>> >
>> > I'm re-posting this question again, as I didn't get any replies
>> > previously.
>> >
>> > I?m interested to investigate if there are any differences in phase
>> > synchronization in resting state data at timepoint 2 versus
>> > timepoint 1. The
>> > EEG data was collected using 64 EEG channels, resting state, eyes
>> > opened and
>> > eyes closed. I?ve arbitrarily segmented the resting state data into
>> > epochs
>> > of 2s each, and the epochs with artifacts are excluded completely
>> > from
>> > further analyses. I then performed mtmfft to get the fourier
>> > representation
>> > of the data, extracted the coherence, and compared the coherence
>> > difference
>> > of timepoint 2 versus timepoint 1 of all channels paired with all
>> > other
>> > channels.
>> >
>> > I figured that if I extract the connectivity analyses without
>> > specifying the
>> > channelcmb, I get a 'chan_chan_freq' dimord variable that would
>> > allow me to
>> > perform cluster-based statistical analyses. I get an output with
>> > ?chan_chan?
>> > dimord variable. However, I was not 100% sure that this is correct,
>> > hence, I?ve
>> > inserted my code (see below). It?ll be great if you could tell me if
>> > what I?m
>> > doing makes any sense at all. Also, I don?t know how I can plot the
>> > results
>> > to see if it make any sense at all.
>> >
>> > Also, I checked the values obtained from when I specified
>> > "cfg.channelcmb"
>> > and when I did not specify "cfg.channelcmb", I noticed that the
>> > values were
>> > somehow different. I would assume that the values to be similar,
>> > although
>> > I'm not sure why they would have differences in the values obtained
>> > from
>> > specifying "cfg.channelcmb".
>> >
>> > This is my code that I've used thus far:
>> >
>> > for sub = 1:5
>> >
>> > cfg = [];
>> >
>> > cfg.output = 'fourier';
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.method = 'mtmfft';
>> >
>> > cfg.keeptrials = 'yes';
>> >
>> > cfg.tapsmofrq = 5;
>> >
>> > cfg.foilim = [0 100];
>> >
>> > cfg.taper = 'dpss';
>> >
>> > % find the index for the c200 condition
>> >
>> > pre_c200_idx = find(data5.trialinfo == 201);
>> >
>> > cfg.trials = pre_c200_idx;
>> >
>> > HLF_pre_c200 = ft_freqanalysis(cfg, data5);
>> >
>> > post_c200_idx = find(data5.trialinfo == 200);
>> >
>> > cfg.trials = post_c200_idx;
>> >
>> > HLF_post_c200 = ft_freqanalysis(cfg, data5);
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.keeptrials = 'no';
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.removemean = 'yes';
>> >
>> > cfg.method = 'coh';
>> >
>> > HLF_pre_c200coh{sub} = ft_connectivityanalysis(cfg,
>> > HLF_pre_c200);
>> >
>> > HLF_post_c200coh{sub} = ft_connectivityanalysis(cfg,
>> > HLF_post_c200);
>> >
>> > end
>> >
>> >
>> >
>> >
> load('D:\Hweeling_Programs\fieldtrip-20140330\template\layout\easycapM11.mat
> ');
>> >
>> > cfg_neighb.method = 'template';
>> >
>> > cfg_neighb.layout = lay;
>> >
>> > cfg_neighb.channel = 'all';
>> >
>> > neighbours = ft_prepare_neighbours(cfg_neighb,
>> > sub_HLF_pre_c200coh{1});
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay;
>> >
>> > cfg.neighbours = neighbours;
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.channelcmb = {cfg.channel, cfg.channel};
>> >
>> > cfg.latency = 'all';
>> >
>> > cfg.avgovertime = 'no';
>> >
>> > cfg.avgoverchan = 'no';
>> >
>> > cfg.parameter = 'cohspctrm';
>> >
>> > cfg.method = 'montecarlo';
>> >
>> > cfg.statistic = 'depsamplesT';
>> >
>> > cfg.correctm = 'cluster';
>> >
>> > cfg.tail = 0;
>> >
>> > % cfg.clustertail = 0;
>> >
>> > cfg.alpha = 0.05/8; % to correct for multiple comparisons across 8
>> > frequency
>> > bands.
>> >
>> > cfg.numrandomization = 10000;
>> >
>> > cfg.ivar = 2;
>> >
>> > cfg.uvar = 1;
>> >
>> >
>> >
>> > % design matrices
>> >
>> > clear design;
>> >
>> > design(1,:) = [1:5, 1:5];
>> >
>> > design(2,:) = [ones(1,5), ones(1,5) * 2];
>> >
>> > cfg.design = design;
>> >
>> > % for theta band
>> >
>> > cfg.avgoverfreq = 'yes';
>> >
>> > cfg.frequency = [4 8]; % I also performed the statistics for delta
>> > (2-4),
>> > alpha (8-10.5), alpha (10.5-13), beta (13-20), beta (20-30), gamma
>> > (30-40),
>> > and gamma (40-100).
>> >
>> > [diffc200_theta_stat] = ft_freqstatistics(cfg,
>> > sub_HLF_post_c200coh{:},
>> > sub_HLF_pre_c200coh{:});
>> >
>> >
>> >
>> > When I tried to plot the results, I used this code:
>> >
>> > cfg = [];
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.layout = 'lay';
>> >
>> > cfg.zlim = [-1 1];
>> >
>> > cfg.alpha = 0.05;
>> >
>> > cfg.refchannel = 'all';
>> >
>> > ft_clusterplot(cfg, diffc200_theta_stat);
>> >
>> > However, I was not sure how I could plot the results. I get an error
>> > message
>> > from Fieldtrip when using ft_clusterplot:
>> >
>> > Error using topoplot_common (line 366)
>> >
>> > no reference channel is specified
>> >
>> > Error in ft_topoplotTFR (line 192)
>> >
>> > [cfg] = topoplot_common(cfg, varargin{:});
>> >
>> > Error in ft_clusterplot (line 372)
>> >
>> > ft_topoplotTFR(cfgtopo, stat);
>> >
>> >
>> >
>> > According to your paper in 2007, the topoplot of the results were
>> > masked by
>> > the spatio-spectral pattern of the significant clusters. I don't
>> > know how to
>> > do this, and I would really appreciate if you can show me how to
>> > make such a
>> > plot.
>> >
>> > Thank you very much.
>> >
>> > Kind regards,
>> >
>> > Hweeling
>> >
>> >
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140908/98b85
> 280/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 2
>> > Date: Mon, 8 Sep 2014 21:38:01 +0200
>> > From: KatrinH Heimann <katrinheimann at gmail.com>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Problem with Geodesic 129 layout!!
>> > Message-ID:
>> > <CACnHcBeqEoQMirfZ-hCKK8rSmbfcNYzxFQH1mmSaeGCuKMN-uA at mail.gmail.com>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > Dear Joseph, thanks so much, I really appreciate your help. I was
>> > also
>> > wandering, if maybe there is another bug in my code.
>> > Our nets are 128 channels, Hydrocel, but as a colleague of me
>> > adviced me to
>> > do so, I also tried the 129 layout.
>> > My code you find below (without selection of bad channels etc.)
>> > If you need also the data of two subjects to have a look, let me
>> > know!!!
>> > Best and thanks really!
>> > Katrin
>> >
>> >
>> > subject=input('Which subject do you want to analyse? ','s');
>> >
>> > name = strcat(subject,'.raw');
>> >
>> > subjectnumber=input('Which is the subjectnumber?', 's');
>> >
>> > sb=subjectnumber
>> >
>> > %subjectnumber = strcat(subjectnumber, '.txt')
>> >
>> > %%
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.dataset = name;
>> >
>> > cfg.trialfun = 'ft_trialfun_general'; % this is the default
>> >
>> > cfg.trialdef.eventtype = 'trigger';
>> >
>> > cfg.trialdef.eventvalue = 'stim'; % the value of the stimulus
>> > trigger
>> >
>> > cfg.trialdef.prestim = 0.234; % in seconds
>> >
>> > cfg.trialdef.poststim = 7.266; % in seconds (whole time of video
>> > observation, but maybe we need less)
>> >
>> > cfg = ft_definetrial(cfg);
>> >
>> >
>> > cfg.trl([1:7],:) = [];
>> >
>> >
>> > %change timeline according to constant offset of 16 ms = 8 samples
>> > (because
>> > recorded with 500 hz)in
>> >
>> > %structure trial
>> >
>> > cfg.trl(:,3)=cfg.trl(:,3)-8
>> >
>> >
>> > %change timeline to make the cut the zeropoint
>> >
>> >
>> > cfg.trl(:,3)=cfg.trl(:,3)- 1000;
>> >
>> >
>> >
>> > %% preprocess data
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.detrend = 'yes';
>> >
>> > cfg.preproc.demean = 'yes';
>> >
>> > cfg.preproc.baselinewindow = [-2.1 -2.0]
>> >
>> > %cfg.preproc.bpfilter = 'yes'; - the data here has to be already
>> > filtered
>> >
>> > %(0.1-45) and bad channels have to be replaced!!!!
>> >
>> > %cfg.preproc.bpfreq = [6 32];
>> >
>> > %
>> >
>> > %
>> >
>> > obs_data = ft_preprocessing(cfg);
>> >
>> > %
>> >
>> > save (strcat(sb,'obs_data') , 'obs_data')
>> >
>> >
>> > %% determining channel neighbours (necessary for Artifact detection)
>> >
>> > cfg = [];
>> >
>> > cfg.channel = obs_data.label;
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.feedback = 'yes';
>> >
>> > lay = ft_prepare_layout(cfg);
>> >
>> >
>> >
>> > cfg_neighb = [];
>> >
>> > cfg_neighb.feedback = 'yes';
>> >
>> >
>> > cfg_neighb.method = 'triangulation';
>> >
>> > cfg_neighb.layout = 'GSN128.sfp';
>> >
>> > neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >
>> >
>> > obs_data.elec = ft_read_sens('GSN128.sfp');
>> >
>> >
>> > %% Artifacts - to detect bad channels - is not saved!!!
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs_data_try = ft_rejectvisual(cfg, obs_data);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_try = ft_rejectartifact (cfg,obs_data);
>> >
>> >
>> >
>> > %% Preparing neighbours for channel repair - but bad channel info
>> > in!!!
>> >
>> >
>> > % cfg_neighb = [];
>> >
>> > % cfg_neighb.feedback = 'yes';
>> >
>> > % cfg_neighb.method = 'triangulation';
>> >
>> > % cfg_neighb.layout = 'GSN-HydroCel-128.sfp';
>> >
>> > % neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >
>> >
>> > % Interpolate and put into new data structure
>> >
>> > cfg = [];
>> >
>> > cfg.badchannel = {};
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.method = 'nearest';
>> >
>> > cfg.neighbours = neighbours;
>> >
>> > obs_data_channelrepaired = ft_channelrepair(cfg,obs_data)
>> >
>> >
>> > % Check reconstruction
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >
>> >
>> > % dat1=obs_data;
>> >
>> > % dat2=obs_data_channelrepaired;
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(62,:); % 68 is channel index of E68
>> >
>> > % y=dat2.trial{1}(62,:);
>> >
>> > % plot(x);hold on;plot(y,'r');
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(72,:);
>> >
>> > % y=dat2.trial{1}(75,:);
>> >
>> > % figure;
>> >
>> > % plot(x);hold on;plot(y,'r')
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(75,:);
>> >
>> > % y=dat2.trial{1}(75,:);
>> >
>> > % figure;
>> >
>> > % plot(x);hold on;plot(y,'r')
>> >
>> > %% artifact rejection/trial inspection - throw out electrode jumps
>> > etc.
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >
>> >
>> > %% ICA - Anzahl der Komponenten anpassen!- adapt numcomponent
>> > according to
>> > channels interpolated %% 128-number of interp. channels)
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.numcomponent = 128
>> >
>> > comp = ft_componentanalysis(cfg,obs_data_clean1);
>> >
>> > save (strcat(sb,'comp_all') , 'comp')
>> >
>> >
>> > %%
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'component';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='some';
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > ft_databrowser(cfg,comp);
>> >
>> >
>> > %% poweranalysis components
>> >
>> > cfg = [];
>> >
>> > cfg.output = 'pow';
>> >
>> > cfg.channel = 'all';%compute the power spectrum in all ICs
>> >
>> > cfg.method = 'mtmfft';
>> >
>> > cfg.taper = 'hanning';
>> >
>> > cfg.foi = 0:0.2:50;
>> >
>> > obs_freq = ft_freqanalysis(cfg, comp);
>> >
>> >
>> > %And you can plot the spectra:
>> >
>> >
>> > %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>> >
>> > nsubplots = 16;
>> >
>> > nbyn = sqrt(nsubplots);% sqrt(nsubplots) should not contain
>> > decimals, type
>> > doc subplot
>> >
>> >
>> > Nfigs = ceil(size(comp.topo,1)/nsubplots);
>> >
>> > tot = Nfigs*nsubplots;
>> >
>> >
>> > rptvect = 1:size(comp.topo,1);
>> >
>> > rptvect = padarray(rptvect, [0 tot-size(comp.topo,1)], 0,'post');
>> >
>> > rptvect = reshape(rptvect,nsubplots,Nfigs)';
>> >
>> >
>> > for r=1:size(rptvect,1);
>> >
>> > figure;set(gcf,'units','normalized','outerposition',[0 0 1
>> > 1]);%full
>> > screen
>> >
>> > k=0;
>> >
>> > for j=1:size(rptvect,2);
>> >
>> > if~(rptvect(r,j)==0);
>> >
>> > k=k+1;
>> >
>> > cfg=[];
>> >
>> > cfg.channel = rptvect(r,j);
>> >
>> > cfg.ylim =[0 500]
>> >
>> > cfg.xlim =[0 50]
>> >
>> > subplot(nbyn,nbyn,k);ft_singleplotER(cfg,obs_freq);
>> >
>> > end
>> >
>> > end
>> >
>> > end
>> >
>> >
>> > %For the IC topos you'll follow the same logic as above but with:
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [1:20]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [21:40]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [41:60]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [61:80]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> >
>> >
>> >
>> >
>> > %% Seperate observation conditions
>> >
>> >
>> > name1= strcat(num2str(sb),'_90.xls');
>> >
>> > list1=xlsread (name1)
>> >
>> >
>> >
>> >
>> >
>> > name2= strcat(num2str(sb),'_180.xls');
>> >
>> > list2=xlsread (name2)
>> >
>> >
>> >
>> > %
>> >
>> >
>> > cfg = []
>> >
>> > cfg.trials = [list1]
>> >
>> > obs90_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.trials = [list2]
>> >
>> > obs180_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >
>> >
>> > %%PIPELINE FOR obs90
>> >
>> >
>> > %% Decompose original data according to the components found before
>> >
>> > load (strcat(sb,'comp_all'))
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.numcomponent = 128;
>> >
>> > cfg.unmixing = comp.unmixing;
>> >
>> > cfg.topolabel = comp.topolabel;
>> >
>> >
>> > comp_1 = ft_componentanalysis(cfg, obs90_data);
>> >
>> >
>> >
>> > %% Reject component
>> >
>> > cfg = [];
>> >
>> > cfg.component = [];
>> >
>> > obs90_data_ica_cleaned = ft_rejectcomponent(cfg, comp_1,
>> > obs90_data);
>> >
>> >
>> >
>> >
>> > save (strcat(sb,'obs90_ica_cleaned') , 'obs90_data_ica_cleaned')
>> >
>> >
>> >
>> >
>> > %% Artifacts - final detection
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs90_data_clean3 = ft_rejectvisual(cfg, obs90_data_ica_cleaned);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs90_data_clean3);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs90_data_clean2 = ft_rejectartifact (cfg,obs90_data_clean3);
>> >
>> >
>> >
>> > % Save clean data
>> >
>> >
>> > save (strcat(subject,'obs90_clean') , 'obs90_data_clean2')
>> >
>> >
>> >
>> > %% Rereferencing data
>> >
>> > cfg = [];
>> >
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.reref = 'yes';
>> >
>> > cfg.preproc.refchannel = 'all';
>> >
>> > obs90_data_ref= ft_preprocessing(cfg,obs90_data_clean2)
>> >
>> >
>> > save (strcat(subject,'obs90_ref') , 'obs90_data_ref')
>> >
>> > %% Snap out smaller pieces (the third second)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.toilim = [0 1]
>> >
>> >
>> > obs90_data_small = ft_redefinetrial(cfg,obs90_data_ref)
>> >
>> >
>> > %% TIMELOCK ERP
>> >
>> > obs90_data_ERP = ft_timelockanalysis(cfg, obs90_data_small);
>> >
>> >
>> > save (strcat(subject,'obs90_ERP') , 'obs90_data_ERP')
>> >
>> >
>> > %% plot it
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, obs90_data_ERP)
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > %% PIPELINE FOR obs180
>> >
>> >
>> >
>> > %% Decompose original data according to the components found before
>> >
>> > load (strcat(sb,'comp_all'))
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.numcomponent = 128
>> >
>> > cfg.unmixing = comp.unmixing;
>> >
>> > cfg.topolabel = comp.topolabel;
>> >
>> > comp_2 = ft_componentanalysis(cfg, obs180_data);
>> >
>> >
>> >
>> > %% Reject component
>> >
>> > cfg = [];
>> >
>> > cfg.component = [];
>> >
>> > obs180_data_ica_cleaned = ft_rejectcomponent(cfg, comp_2,
>> > obs180_data);
>> >
>> >
>> >
>> >
>> > save (strcat(sb,'obs180_ica_cleaned') , 'obs180_data_ica_cleaned')
>> >
>> >
>> >
>> >
>> > %% Artifacts final 180
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs180_data_clean3 = ft_rejectvisual(cfg, obs180_data_ica_cleaned);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs180_data_clean3);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs180_data_clean2 = ft_rejectartifact (cfg,obs180_data_clean3);
>> >
>> >
>> >
>> > % Save clean data
>> >
>> >
>> > save (strcat(subject,'obs180_clean') , 'obs180_data_clean2')
>> >
>> >
>> >
>> > %% Rereferencing data
>> >
>> > cfg = [];
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.reref = 'yes';
>> >
>> > cfg.preproc.refchannel = 'all';
>> >
>> > obs180_data_ref= ft_preprocessing(cfg,obs180_data_clean2)
>> >
>> >
>> > save (strcat(subject,'obs180_ref') , 'obs180_data_ref')
>> >
>> > %% Snap out smaller pieces (the third second)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.toilim = [0 1]
>> >
>> >
>> > obs180_data_small = ft_redefinetrial(cfg,obs180_data_ref)
>> >
>> >
>> > %% TIMELOCK ERP
>> >
>> > obs180_data_ERP = ft_timelockanalysis(cfg, obs180_data_small);
>> >
>> >
>> > save (strcat(subject,'obs180_ERP') , 'obs180_data_ERP')
>> >
>> >
>> > %% plot 180 ERP
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, obs180_data_ERP)
>> >
>> >
>> > %% plot both ERPs
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > cfg.showlabels = 'yes';
>> >
>> > ft_multiplotER(cfg, obs90_data_ERP, obs180_data_ERP)
>> >
>> >
>> > %% plot difference wave
>> >
>> >
>> > difference = obs180_data_ERP; % copy one of the
>> > structures
>> >
>> > difference.avg = obs180_data_ERP.avg - obs90_data_ERP.avg; % compute
>> > the
>> > difference ERP
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, difference)
>> >
>> > 2014-09-07 7:53 GMT+02:00 Joseph Dien <jdien07 at mac.com>:
>> >
>> >> Hi,
>> >> I?m one of the main Fieldtrip contributors working on supporting
>> >> EGI
>> >> data. A couple things. First of all, exactly which net do you use?
>> >> If
>> >> they are 129-channel, there is still the question of whether they
>> >> are the
>> >> Hydrocel or the older geodesic nets. Regardless, that shouldn't
>> >> cause an
>> >> error like this. Could you send me the file you are trying to
>> >> process and
>> >> the script you are using and I?ll try troubleshooting it.
>> >>
>> >> Joe
>> >>
>> >>
>> >> On Aug 26, 2014, at 12:53 AM, Ana Laura Diez Martini <
>> >> diezmartini at gmail.com> wrote:
>> >>
>> >> Which nets do you use? I use EGI. I tried both the ones you mention
>> >> and
>> >> they didn't work. It was hard to find that exact one online but it
>> >> was the
>> >> only file that actually worked.
>> >>
>> >>
>> >> On Mon, Aug 25, 2014 at 7:52 AM, KatrinH Heimann
>> >> <katrinheimann at gmail.com>
>> >> wrote:
>> >>
>> >>> Dear Ana, dear all,
>> >>> the layout you used does not correspond to our nets. I tried the
>> >>> GSN-HydroCel-128.sfp and the GSN-HydroCel-129.sfp. Both don't
>> >>> work.
>> >>> Please can anybody help?
>> >>> Cheers
>> >>> Katrin
>> >>>
>> >>>
>> >>> 2014-08-24 5:40 GMT+02:00 Ana Laura Diez Martini
>> >>> <diezmartini at gmail.com>:
>> >>>
>> >>> Try this one
>> >>> <https://www.dropbox.com/s/58zv658rtdant2c/GSN129.sfp?dl=0>
>> >>>> I use
>> >>>>
>> >>>>
>> >>>> On Sun, Aug 24, 2014 at 4:08 AM, KatrinH Heimann <
>> >>>> katrinheimann at gmail.com> wrote:
>> >>>>
>> >>>>> Dear all, my problems seem neverending. This time however i
>> >>>>> really need
>> >>>>> help.
>> >>>>> I implemented a pipeline for ERPs. All works now,
>> >>>>> trialdefinition,
>> >>>>> preprocessing, channelreplacement, ica, componentrejection,
>> >>>>> final
>> >>>>> artifactdetection, timelock of the single subject data. However,
>> >>>>> when I
>> >>>>> wanna compute the grandaverage of the single subjects I get the
>> >>>>> following
>> >>>>> error message:
>> >>>>>
>> >>>>> computing average of avg over 19 subjects
>> >>>>> Warning: discarding electrode information because it cannot be
>> >>>>> averaged
>> >>>>> > In ft_timelockgrandaverage at 249
>> >>>>> the call to "ft_timelockgrandaverage" took 0 seconds and
>> >>>>> required the
>> >>>>> additional allocation of an estimated 3 MB
>> >>>>> computing average of avg over 2 subjects
>> >>>>> Warning: discarding electrode information because it cannot be
>> >>>>> averaged
>> >>>>> > In ft_timelockgrandaverage at 249
>> >>>>> the call to "ft_timelockgrandaverage" took 0 seconds and
>> >>>>> required the
>> >>>>> additional allocation of an estimated 0 MB
>> >>>>> the call to "ft_prepare_layout" took 0 seconds and required the
>> >>>>> additional allocation of an estimated 0 MB
>> >>>>> the call to "ft_topoplotER" took 0 seconds and required the
>> >>>>> additional
>> >>>>> allocation of an estimated 0 MB
>> >>>>>
>> >>>>> Furthermore in the plot of the significant clusters, the
>> >>>>> channelnames
>> >>>>> are mixed up (do not correspond to my net)
>> >>>>>
>> >>>>>
>> >>>>> I guess that there is a problem with the layout I use.
>> >>>>> Here the code that I use
>> >>>>>
>> >>>>> %For generating the layout (also in the single subjects):
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.channel = obs_data.label;
>> >>>>>
>> >>>>> cfg.layout = 'GSN-HydroCel-129.sfp';
>> >>>>>
>> >>>>> cfg.feedback = 'yes';
>> >>>>>
>> >>>>> lay = ft_prepare_layout(cfg);
>> >>>>>
>> >>>>>
>> >>>>> cfg_neighb = [];
>> >>>>>
>> >>>>> cfg_neighb.feedback = 'yes';
>> >>>>>
>> >>>>> cfg_neighb.method = 'triangulation';
>> >>>>>
>> >>>>> cfg_neighb.layout = lay;
>> >>>>>
>> >>>>> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>>>>
>> >>>>>
>> >>>>> obs_data.elec = ft_read_sens('GSN-HydroCel-129.sfp');
>> >>>>>
>> >>>>>
>> >>>>> %For computing the grand average
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.channel = 'all';
>> >>>>>
>> >>>>> cfg.latency = 'all';
>> >>>>>
>> >>>>> cfg.parameter = 'avg';
>> >>>>>
>> >>>>> cfg.keepindividual = 'no'
>> >>>>>
>> >>>>> GA_90 = ft_timelockgrandaverage(cfg,all90{:});
>> >>>>>
>> >>>>> GA_180 = ft_timelockgrandaverage(cfg,all180{:});
>> >>>>>
>> >>>>> % "{:}" means to use data from all elements of the variable
>> >>>>>
>> >>>>>
>> >>>>> For plotting the significant clusters
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.style = 'blank';
>> >>>>>
>> >>>>> cfg.layout = lay;
>> >>>>>
>> >>>>> cfg.channellabels = 'yes';
>> >>>>>
>> >>>>> cfg.highlight = 'labels';
>> >>>>>
>> >>>>> cfg.highlightchannel = find(stat.mask);
>> >>>>>
>> >>>>> cfg.comment = 'no';
>> >>>>>
>> >>>>> figure; ft_topoplotER(cfg, GA_90)
>> >>>>>
>> >>>>> title('Nonparametric: significant with cluster multiple
>> >>>>> comparison
>> >>>>> correction')
>> >>>>>
>> >>>>>
>> >>>>> Do you have ANY idea to this? I am really completely
>> >>>>> helpless....
>> >>>>>
>> >>>>> thanks and best
>> >>>>>
>> >>>>> Katrin
>> >>>>>
>> >>>>> _______________________________________________
>> >>>>> fieldtrip mailing list
>> >>>>> fieldtrip at donders.ru.nl
>> >>>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>>>
>> >>>>
>> >>>>
>> >>>> _______________________________________________
>> >>>> fieldtrip mailing list
>> >>>> fieldtrip at donders.ru.nl
>> >>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>>
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
>> >>
>> >>
> ----------------------------------------------------------------------------
> ----
>> >>
>> >> Joseph Dien, PhD
>> >> Research Associate
>> >> Cognitive Neurology
>> >> The Johns Hopkins University School of Medicine
>> >>
>> >> Lab E-mail: jdien1 at jhmi.edu
>> >> Private E-mail: jdien07 at mac.com
>> >> Office Phone: 410-614-3115
>> >> Cell Phone: 202-297-8117
>> >> Fax: 410-955-0188
>> >> http://joedien.com
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
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> b61/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 3
>> > Date: Mon, 08 Sep 2014 21:55:44 -0400
>> > From: Joseph Dien <jdien07 at mac.com>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Problem with Geodesic 129 layout!!
>> > Message-ID: <148A801A-8CEA-4F88-A7D0-130637BDAC03 at mac.com>
>> > Content-Type: text/plain; charset="windows-1252"
>> >
>> > yes, please do send me the data.
>> >
>> > Thanks!
>> >
>> > Joe
>> >
>> > On Sep 8, 2014, at 3:38 PM, KatrinH Heimann
>> > <katrinheimann at gmail.com> wrote:
>> >
>> >> Dear Joseph, thanks so much, I really appreciate your help. I was
>> >> also wandering, if maybe there is another bug in my code.
>> >> Our nets are 128 channels, Hydrocel, but as a colleague of me
>> >> adviced me to do so, I also tried the 129 layout.
>> >> My code you find below (without selection of bad channels etc.)
>> >> If you need also the data of two subjects to have a look, let me
>> >> know!!!
>> >> Best and thanks really!
>> >> Katrin
>> >>
>> >>
>> >>
>> >> subject=input('Which subject do you want to analyse? ','s');
>> >>
>> >> name = strcat(subject,'.raw');
>> >>
>> >> subjectnumber=input('Which is the subjectnumber?', 's');
>> >>
>> >> sb=subjectnumber
>> >>
>> >> %subjectnumber = strcat(subjectnumber, '.txt')
>> >>
>> >> %%
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.dataset = name;
>> >>
>> >> cfg.trialfun = 'ft_trialfun_general'; % this is the default
>> >>
>> >> cfg.trialdef.eventtype = 'trigger';
>> >>
>> >> cfg.trialdef.eventvalue = 'stim'; % the value of the stimulus
>> >> trigger
>> >>
>> >> cfg.trialdef.prestim = 0.234; % in seconds
>> >>
>> >> cfg.trialdef.poststim = 7.266; % in seconds (whole time of video
>> >> observation, but maybe we need less)
>> >>
>> >> cfg = ft_definetrial(cfg);
>> >>
>> >>
>> >>
>> >> cfg.trl([1:7],:) = [];
>> >>
>> >>
>> >>
>> >> %change timeline according to constant offset of 16 ms = 8 samples
>> >> (because recorded with 500 hz)in
>> >>
>> >> %structure trial
>> >>
>> >> cfg.trl(:,3)=cfg.trl(:,3)-8
>> >>
>> >>
>> >>
>> >> %change timeline to make the cut the zeropoint
>> >>
>> >>
>> >>
>> >> cfg.trl(:,3)=cfg.trl(:,3)- 1000;
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% preprocess data
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.detrend = 'yes';
>> >>
>> >> cfg.preproc.demean = 'yes';
>> >>
>> >> cfg.preproc.baselinewindow = [-2.1 -2.0]
>> >>
>> >> %cfg.preproc.bpfilter = 'yes'; - the data here has to be already
>> >> filtered
>> >>
>> >> %(0.1-45) and bad channels have to be replaced!!!!
>> >>
>> >> %cfg.preproc.bpfreq = [6 32];
>> >>
>> >> %
>> >>
>> >> %
>> >>
>> >> obs_data = ft_preprocessing(cfg);
>> >>
>> >> %
>> >>
>> >> save (strcat(sb,'obs_data') , 'obs_data')
>> >>
>> >>
>> >>
>> >> %% determining channel neighbours (necessary for Artifact
>> >> detection)
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = obs_data.label;
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.feedback = 'yes';
>> >>
>> >> lay = ft_prepare_layout(cfg);
>> >>
>> >>
>> >> cfg_neighb = [];
>> >>
>> >> cfg_neighb.feedback = 'yes';
>> >>
>> >>
>> >>
>> >> cfg_neighb.method = 'triangulation';
>> >>
>> >> cfg_neighb.layout = 'GSN128.sfp';
>> >>
>> >> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>
>> >>
>> >>
>> >> obs_data.elec = ft_read_sens('GSN128.sfp');
>> >>
>> >>
>> >>
>> >> %% Artifacts - to detect bad channels - is not saved!!!
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs_data_try = ft_rejectvisual(cfg, obs_data);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_try = ft_rejectartifact (cfg,obs_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Preparing neighbours for channel repair - but bad channel info
>> >> in!!!
>> >>
>> >>
>> >>
>> >> % cfg_neighb = [];
>> >>
>> >> % cfg_neighb.feedback = 'yes';
>> >>
>> >> % cfg_neighb.method = 'triangulation';
>> >>
>> >> % cfg_neighb.layout = 'GSN-HydroCel-128.sfp';
>> >>
>> >> % neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>
>> >>
>> >>
>> >> % Interpolate and put into new data structure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.badchannel = {};
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.method = 'nearest';
>> >>
>> >> cfg.neighbours = neighbours;
>> >>
>> >> obs_data_channelrepaired = ft_channelrepair(cfg,obs_data)
>> >>
>> >>
>> >>
>> >> % Check reconstruction
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >>
>> >>
>> >>
>> >> % dat1=obs_data;
>> >>
>> >> % dat2=obs_data_channelrepaired;
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(62,:); % 68 is channel index of E68
>> >>
>> >> % y=dat2.trial{1}(62,:);
>> >>
>> >> % plot(x);hold on;plot(y,'r');
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(72,:);
>> >>
>> >> % y=dat2.trial{1}(75,:);
>> >>
>> >> % figure;
>> >>
>> >> % plot(x);hold on;plot(y,'r')
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(75,:);
>> >>
>> >> % y=dat2.trial{1}(75,:);
>> >>
>> >> % figure;
>> >>
>> >> % plot(x);hold on;plot(y,'r')
>> >>
>> >> %% artifact rejection/trial inspection - throw out electrode jumps
>> >> etc.
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >>
>> >>
>> >>
>> >> %% ICA - Anzahl der Komponenten anpassen!- adapt numcomponent
>> >> according to channels interpolated %% 128-number of interp.
>> >> channels)
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = {'all'};
>> >>
>> >> cfg.numcomponent = 128
>> >>
>> >> comp = ft_componentanalysis(cfg,obs_data_clean1);
>> >>
>> >> save (strcat(sb,'comp_all') , 'comp')
>> >>
>> >>
>> >>
>> >> %%
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'component';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='some';
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> ft_databrowser(cfg,comp);
>> >>
>> >>
>> >>
>> >> %% poweranalysis components
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.output = 'pow';
>> >>
>> >> cfg.channel = 'all';%compute the power spectrum in all ICs
>> >>
>> >> cfg.method = 'mtmfft';
>> >>
>> >> cfg.taper = 'hanning';
>> >>
>> >> cfg.foi = 0:0.2:50;
>> >>
>> >> obs_freq = ft_freqanalysis(cfg, comp);
>> >>
>> >>
>> >>
>> >> %And you can plot the spectra:
>> >>
>> >>
>> >>
>> >> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>> >>
>> >> nsubplots = 16;
>> >>
>> >> nbyn = sqrt(nsubplots);% sqrt(nsubplots) should not contain
>> >> decimals, type doc subplot
>> >>
>> >>
>> >>
>> >> Nfigs = ceil(size(comp.topo,1)/nsubplots);
>> >>
>> >> tot = Nfigs*nsubplots;
>> >>
>> >>
>> >>
>> >> rptvect = 1:size(comp.topo,1);
>> >>
>> >> rptvect = padarray(rptvect, [0 tot-size(comp.topo,1)], 0,'post');
>> >>
>> >> rptvect = reshape(rptvect,nsubplots,Nfigs)';
>> >>
>> >>
>> >>
>> >> for r=1:size(rptvect,1);
>> >>
>> >> figure;set(gcf,'units','normalized','outerposition',[0 0 1
>> >> 1]);%full screen
>> >>
>> >> k=0;
>> >>
>> >> for j=1:size(rptvect,2);
>> >>
>> >> if~(rptvect(r,j)==0);
>> >>
>> >> k=k+1;
>> >>
>> >> cfg=[];
>> >>
>> >> cfg.channel = rptvect(r,j);
>> >>
>> >> cfg.ylim =[0 500]
>> >>
>> >> cfg.xlim =[0 50]
>> >>
>> >> subplot(nbyn,nbyn,k);ft_singleplotER(cfg,obs_freq);
>> >>
>> >> end
>> >>
>> >> end
>> >>
>> >> end
>> >>
>> >>
>> >>
>> >> %For the IC topos you'll follow the same logic as above but with:
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [1:20]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [21:40]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [41:60]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [61:80]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Seperate observation conditions
>> >>
>> >>
>> >>
>> >> name1= strcat(num2str(sb),'_90.xls');
>> >>
>> >> list1=xlsread (name1)
>> >>
>> >>
>> >>
>> >> name2= strcat(num2str(sb),'_180.xls');
>> >>
>> >> list2=xlsread (name2)
>> >>
>> >>
>> >> %
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.trials = [list1]
>> >>
>> >> obs90_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.trials = [list2]
>> >>
>> >> obs180_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >>
>> >>
>> >>
>> >> %%PIPELINE FOR obs90
>> >>
>> >>
>> >>
>> >> %% Decompose original data according to the components found before
>> >>
>> >> load (strcat(sb,'comp_all'))
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.numcomponent = 128;
>> >>
>> >> cfg.unmixing = comp.unmixing;
>> >>
>> >> cfg.topolabel = comp.topolabel;
>> >>
>> >>
>> >>
>> >> comp_1 = ft_componentanalysis(cfg, obs90_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Reject component
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [];
>> >>
>> >> obs90_data_ica_cleaned = ft_rejectcomponent(cfg, comp_1,
>> >> obs90_data);
>> >>
>> >>
>> >>
>> >>
>> >> save (strcat(sb,'obs90_ica_cleaned') , 'obs90_data_ica_cleaned')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Artifacts - final detection
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs90_data_clean3 = ft_rejectvisual(cfg, obs90_data_ica_cleaned);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs90_data_clean3);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs90_data_clean2 = ft_rejectartifact (cfg,obs90_data_clean3);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> % Save clean data
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_clean') , 'obs90_data_clean2')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Rereferencing data
>> >>
>> >> cfg = [];
>> >>
>> >>
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.reref = 'yes';
>> >>
>> >> cfg.preproc.refchannel = 'all';
>> >>
>> >> obs90_data_ref= ft_preprocessing(cfg,obs90_data_clean2)
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_ref') , 'obs90_data_ref')
>> >>
>> >> %% Snap out smaller pieces (the third second)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.toilim = [0 1]
>> >>
>> >>
>> >>
>> >> obs90_data_small = ft_redefinetrial(cfg,obs90_data_ref)
>> >>
>> >>
>> >>
>> >> %% TIMELOCK ERP
>> >>
>> >> obs90_data_ERP = ft_timelockanalysis(cfg, obs90_data_small);
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_ERP') , 'obs90_data_ERP')
>> >>
>> >>
>> >>
>> >> %% plot it
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs90_data_ERP)
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% PIPELINE FOR obs180
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Decompose original data according to the components found before
>> >>
>> >> load (strcat(sb,'comp_all'))
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.numcomponent = 128
>> >>
>> >> cfg.unmixing = comp.unmixing;
>> >>
>> >> cfg.topolabel = comp.topolabel;
>> >>
>> >> comp_2 = ft_componentanalysis(cfg, obs180_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Reject component
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [];
>> >>
>> >> obs180_data_ica_cleaned = ft_rejectcomponent(cfg, comp_2,
>> >> obs180_data);
>> >>
>> >>
>> >>
>> >>
>> >> save (strcat(sb,'obs180_ica_cleaned') , 'obs180_data_ica_cleaned')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Artifacts final 180
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs180_data_clean3 = ft_rejectvisual(cfg, obs180_data_ica_cleaned);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs180_data_clean3);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs180_data_clean2 = ft_rejectartifact (cfg,obs180_data_clean3);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> % Save clean data
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_clean') , 'obs180_data_clean2')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Rereferencing data
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.reref = 'yes';
>> >>
>> >> cfg.preproc.refchannel = 'all';
>> >>
>> >> obs180_data_ref= ft_preprocessing(cfg,obs180_data_clean2)
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_ref') , 'obs180_data_ref')
>> >>
>> >> %% Snap out smaller pieces (the third second)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.toilim = [0 1]
>> >>
>> >>
>> >>
>> >> obs180_data_small = ft_redefinetrial(cfg,obs180_data_ref)
>> >>
>> >>
>> >>
>> >> %% TIMELOCK ERP
>> >>
>> >> obs180_data_ERP = ft_timelockanalysis(cfg, obs180_data_small);
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_ERP') , 'obs180_data_ERP')
>> >>
>> >>
>> >>
>> >> %% plot 180 ERP
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs180_data_ERP)
>> >>
>> >>
>> >>
>> >> %% plot both ERPs
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> cfg.showlabels = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs90_data_ERP, obs180_data_ERP)
>> >>
>> >>
>> >>
>> >> %% plot difference wave
>> >>
>> >>
>> >>
>> >> difference = obs180_data_ERP; % copy one of
>> >> the structures
>> >>
>> >> difference.avg = obs180_data_ERP.avg - obs90_data_ERP.avg; %
>> >> compute the difference ERP
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, difference)
>> >>
>> >>
>> >> 2014-09-07 7:53 GMT+02:00 Joseph Dien <jdien07 at mac.com>:
>> >> Hi,
>> >> I?m one of the main Fieldtrip contributors working on supporting
>> >> EGI data. A couple things. First of all, exactly which net do
>> >> you use? If they are 129-channel, there is still the question of
>> >> whether they are the Hydrocel or the older geodesic nets.
>> >> Regardless, that shouldn't cause an error like this. Could you
>> >> send me the file you are trying to process and the script you are
>> >> using and I?ll try troubleshooting it.
>> >>
>> >> Joe
>> >>
>> >>
>> >> On Aug 26, 2014, at 12:53 AM, Ana Laura Diez Martini
>> >> <diezmartini at gmail.com> wrote:
>> >>
>> >>> Which nets do you use? I use EGI. I tried both the ones you
>> >>> mention and they didn't work. It was hard to find that exact one
>> >>> online but it was the only file that actually worked.
>> >>>
>> >>>
>> >>> On Mon, Aug 25, 2014 at 7:52 AM, KatrinH Heimann
>> >>> <katrinheimann at gmail.com> wrote:
>> >>> Dear Ana, dear all,
>> >>> the layout you used does not correspond to our nets. I tried the
>> >>> GSN-HydroCel-128.sfp and the GSN-HydroCel-129.sfp. Both don't
>> >>> work. Please can anybody help?
>> >>> Cheers
>> >>> Katrin
>> >>>
>> >>>
>> >>> 2014-08-24 5:40 GMT+02:00 Ana Laura Diez Martini
>> >>> <diezmartini at gmail.com>:
>> >>>
>> >>> Try this one I use
>> >>>
>> >>>
>> >>> On Sun, Aug 24, 2014 at 4:08 AM, KatrinH Heimann
>> >>> <katrinheimann at gmail.com> wrote:
>> >>> Dear all, my problems seem neverending. This time however i really
>> >>> need help.
>> >>> I implemented a pipeline for ERPs. All works now, trialdefinition,
>> >>> preprocessing, channelreplacement, ica, componentrejection, final
>> >>> artifactdetection, timelock of the single subject data. However,
>> >>> when I wanna compute the grandaverage of the single subjects I get
>> >>> the following error message:
>> >>>
>> >>> computing average of avg over 19 subjects
>> >>> Warning: discarding electrode information because it cannot be
>> >>> averaged
>> >>> > In ft_timelockgrandaverage at 249
>> >>> the call to "ft_timelockgrandaverage" took 0 seconds and required
>> >>> the additional allocation of an estimated 3 MB
>> >>> computing average of avg over 2 subjects
>> >>> Warning: discarding electrode information because it cannot be
>> >>> averaged
>> >>> > In ft_timelockgrandaverage at 249
>> >>> the call to "ft_timelockgrandaverage" took 0 seconds and required
>> >>> the additional allocation of an estimated 0 MB
>> >>> the call to "ft_prepare_layout" took 0 seconds and required the
>> >>> additional allocation of an estimated 0 MB
>> >>> the call to "ft_topoplotER" took 0 seconds and required the
>> >>> additional allocation of an estimated 0 MB
>> >>>
>> >>> Furthermore in the plot of the significant clusters, the
>> >>> channelnames are mixed up (do not correspond to my net)
>> >>>
>> >>>
>> >>> I guess that there is a problem with the layout I use.
>> >>> Here the code that I use
>> >>>
>> >>> %For generating the layout (also in the single subjects):
>> >>> cfg = [];
>> >>>
>> >>> cfg.channel = obs_data.label;
>> >>>
>> >>> cfg.layout = 'GSN-HydroCel-129.sfp';
>> >>>
>> >>> cfg.feedback = 'yes';
>> >>>
>> >>> lay = ft_prepare_layout(cfg);
>> >>>
>> >>>
>> >>> cfg_neighb = [];
>> >>>
>> >>> cfg_neighb.feedback = 'yes';
>> >>>
>> >>> cfg_neighb.method = 'triangulation';
>> >>>
>> >>> cfg_neighb.layout = lay;
>> >>>
>> >>> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>>
>> >>>
>> >>> obs_data.elec = ft_read_sens('GSN-HydroCel-129.sfp');
>> >>>
>> >>>
>> >>>
>> >>> %For computing the grand average
>> >>>
>> >>> cfg = [];
>> >>>
>> >>> cfg.channel = 'all';
>> >>>
>> >>> cfg.latency = 'all';
>> >>>
>> >>> cfg.parameter = 'avg';
>> >>>
>> >>> cfg.keepindividual = 'no'
>> >>>
>> >>> GA_90 = ft_timelockgrandaverage(cfg,all90{:});
>> >>>
>> >>> GA_180 = ft_timelockgrandaverage(cfg,all180{:});
>> >>>
>> >>> % "{:}" means to use data from all elements of the variable
>> >>>
>> >>>
>> >>> For plotting the significant clusters
>> >>>
>> >>> cfg = [];
>> >>>
>> >>> cfg.style = 'blank';
>> >>>
>> >>> cfg.layout = lay;
>> >>>
>> >>> cfg.channellabels = 'yes';
>> >>>
>> >>> cfg.highlight = 'labels';
>> >>>
>> >>> cfg.highlightchannel = find(stat.mask);
>> >>>
>> >>> cfg.comment = 'no';
>> >>>
>> >>> figure; ft_topoplotER(cfg, GA_90)
>> >>>
>> >>> title('Nonparametric: significant with cluster multiple comparison
>> >>> correction')
>> >>>
>> >>>
>> >>>
>> >>> Do you have ANY idea to this? I am really completely helpless....
>> >>>
>> >>> thanks and best
>> >>>
>> >>> Katrin
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
> ----------------------------------------------------------------------------
> ----
>> >>
>> >> Joseph Dien, PhD
>> >> Research Associate
>> >> Cognitive Neurology
>> >> The Johns Hopkins University School of Medicine
>> >>
>> >> Lab E-mail: jdien1 at jhmi.edu
>> >> Private E-mail: jdien07 at mac.com
>> >> Office Phone: 410-614-3115
>> >> Cell Phone: 202-297-8117
>> >> Fax: 410-955-0188
>> >> http://joedien.com
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> >
> ----------------------------------------------------------------------------
> ----
>> >
>> > Joseph Dien, PhD
>> > Research Associate
>> > Cognitive Neurology
>> > The Johns Hopkins University School of Medicine
>> >
>> > Lab E-mail: jdien1 at jhmi.edu
>> > Private E-mail: jdien07 at mac.com
>> > Office Phone: 410-614-3115
>> > Cell Phone: 202-297-8117
>> > Fax: 410-955-0188
>> > http://joedien.com
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140908/81922
> ad2/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 4
>> > Date: Tue, 9 Sep 2014 06:37:13 +0000
>> > From: "Caspar, Emilie" <e.caspar at ucl.ac.uk>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] ft_rejectvisual: removing trials marked as
>> > bad
>> > Message-ID: <3C365782-4B07-4EFE-A921-99489BDD5DA6 at live.ucl.ac.uk>
>> > Content-Type: text/plain; charset="iso-8859-1"
>> >
>> > Hi Ashley,
>> >
>> > Maybe the mistake is you did not integrate in your script the
>> > command to save the rejection. Normally, with the following command,
>> > you will see in the Matlab Window that artifacts are correctly
>> > rejected after the "quit" button.
>> >
>> > clean_data = ft_rejectvisual(cfg, interpoldata);
>> > cfg.artfctdef.reject = 'complete';
>> > cfg.artfctdef.feedback = 'yes';
>> > cleandata = ft_rejectartifact(cfg,clean_data);
>> >
>> > Emilie
>> >
>> >
>> >
>> >
>> >
>> > Le 3 sept. 2014 ? 11:58, Ashley Greene
>> > <agreene24 at gmail.com<mailto:agreene24 at gmail.com>> a ?crit :
>> >
>> > Hello,
>> >
>> > I have used the rejectvisual function in attempt to remove bad
>> > trials, but after marking them and pressing the quit button,
>> > although the command window states that the selected trials have
>> > been removed, there is no obvious change in the data; all of the
>> > trials are still intact. Have I overlooked something?
>> >
>> > Thanks,
>> >
>> > Ashley
>> > _______________________________________________
>> > fieldtrip mailing list
>> > fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/4c2d0
> 985/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 5
>> > Date: Tue, 09 Sep 2014 14:16:20 +0200
>> > From: Patricia Wollstadt <Patricia.Wollstadt at gmx.de>
>> > To: fieldtrip at science.ru.nl
>> > Subject: Re: [FieldTrip] TRENTOOL pipeline help
>> > Message-ID: <540EEF94.9080601 at gmx.de>
>> > Content-Type: text/plain; charset="windows-1252"
>> >
>> > Hello Max,
>> >
>> > I added a few comments to the questions regarding individual
>> > parameters
>> > below. To address the general problem of TRENTOOL telling you, that
>> > there are not enough sample points in your data: From what I can see
>> > in
>> > your script, you probably don't have enough data points in each time
>> > series to robustly estimate TE. You analyze 800 ms of data sampled
>> > at
>> > 300 Hz, which gives you 240 samples per time series. Can you maybe
>> > avoid
>> > downsampling to 300 Hz and downsample to 600 Hz instead? Or could
>> > you
>> > analyze a longer time window of interest?
>> > Note that you also 'lose' data to embedding and the interaction
>> > delay:
>> > The first point that can be used for TE estimation is at max.
>> > embedding
>> > length + max. interaction delay in samples. For example: max.
>> > embedding
>> > length = dim * tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the
>> > max
>> > interaction delay of 30 ms = 9 samples. In this example, you would
>> > be
>> > left with 240 - 29 samples for TE estimation per trial. There is
>> > also
>> > the possibility to estimate time resolved TE/TE for shorter time
>> > windows
>> > of interest (see section 4.4 in the manual); however, this method
>> > requires the use of a GPU for TE estimation.
>> >
>> > I would further recommend to use the new pipeline for group
>> > statistics
>> > described in the manual in section 4.5 (the function
>> > 'TEgroup_calculate'
>> > is deprecated). The new pipeline allows you to reconstruct the
>> > interaction delay and uses the following functions (see also
>> > comments in
>> > the script):
>> >
>> > TEgroup_prepare -> prepares all data sets (all subjects/all
>> > conditions) for group analysis (this means finding common embedding
>> > parameters such that estimates are not biased between groups)
>> > InteractionDelayReconstruction_calculate -> estimates TE for
>> > individual data sets and all assumed interaction delays u
>> > InteractionDelayReconstruction_analyze -> reconstructs the
>> > interaction delay by selecting the u that maximizes TE for each
>> > channel
>> > TEgroup_stats -> calculate group statistics using a permutation test
>> >
>> > I can send you an example script for group TE analysis using this
>> > pipeline to get you started. I hope this helps you to get the group
>> > analysis running. Just write again if you're having trouble setting
>> > up
>> > the pipeline or something is not clear about the parameters/my
>> > comments.
>> >
>> > Best,
>> > Patricia
>> >
>> >
>> >
>> >
>> > On 09/04/2014 08:30 PM, Max Cantor wrote:
>> >> Hi fieldtrippers,
>> >>
>> >> I know trentool is not produced by the Donders Institute, so I'm
>> >> not
>> >> 100% sure if it is appropriate to ask questions about it here, but
>> >> to
>> >> the best of my knowledge they do not have a mailing list and I saw
>> >> a
>> >> few trentool questions in the archives, so I'm going to assume it's
>> >> ok...
>> >>
>> >> In any case, below is my current pipeline (slightly modified for
>> >> comprehensibility):
>> >>
>> >> (notes in bold are comments/questions made in this email, not
>> >> present
>> >> in the pipeline. Sorry in advance for the long post! Any help would
>> >> be
>> >> greatly appreciated as I'm a bit over my head on this but I think
>> >> I'm
>> >> close!)
>> >>
>> >> *****
>> >>
>> >> % Prepare group TE data
>> >>
>> >> cfgP = [];
>> >> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> >> cfgP.TEcalctype = 'VW_ds';
>> >> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>> >>
>> >> *I'm trying to find the transfer entropy between the left and right
>> >> auditory cortices in my experiment. The input is virtual sensor
>> >> data
>> >> that was produced using SAM in fieldtrip on real MEG data. *
>> >>
>> >> % specify u to be scanned
>> >>
>> >> cfgP.predicttime_u = 30;
>> >> cfgP.toi = [-0.4 0.4];
>> >>
>> >> *For clarification, the predicttime_u is in seconds but the toi is
>> >> in
>> >> milliseconds. If I understand correctly, the predicttime_u must fit
>> >> within the toi, but beyond that are there any benefits to it being
>> >> earlier or later?* PW: The predictiontime_u is in milliseconds and
>> >> the
>> >> toi is in seconds. The prediction time is the assumed interaction
>> >> delay between your two sources and should fit within your toi. In
>> >> general it is preferable to use the method for interaction delay
>> >> reconstruction for TE estimation, because it allows you to
>> >> reconstruct
>> >> the actual delay between your source and target times series. A
>> >> non-optimal u/interaction delay may cause an underestimation of TE,
>> >> so
>> >> it is recommended to use the pipeline for interaction delay
>> >> reconstruction whenever estimating TE for unknown delays.
>> >> If you use the methods for interaction delay reconstruction
>> >> 'predicttime_u' is replaced by
>> >> cfgTEP.predicttimemin_u % minimum u to be scanned
>> >> cfgTEP.predicttimemax_u % maximum u to be scanned
>> >> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> >> A large range for u values to be scanned increases computing time a
>> >> lot, so it is best to limit the u range to values that are
>> >> physiologically plausible.
>> >>
>> >> % ACT (Autocorrelation Time) estimation and constraints
>> >>
>> >> cfgP.maxlag = 150;
>> >> cfgP.actthrvalue = 7.5;
>> >> cfgP.minnrtrials = 5;
>> >>
>> >> *My understanding is maxlag should be 1/2 the sampling rate, so
>> >> since
>> >> the data are downsampled to 300hz, it should be 150. I know that
>> >> the
>> >> sample rate and filters are used to determine the actthrvalue, but
>> >> I
>> >> don't actually know the calculation. 7.5 was a rough guess just to
>> >> test the pipeline. I'm also uncertain of what minnrtrials should
>> >> be.*
>> >> PW: You can set the actthrvalue based on the filtering you did
>> >> prior
>> >> to TE analysis. If you for example highpass filtered at 10 Hz, you
>> >> shouldn't find an ACT higher than 30 samples, because you filtered
>> >> out
>> >> any components of the signal slower than 10 Hz/30 samples (given
>> >> your
>> >> sampling frequency of 300 Hz). So in this scenario the actthrvalue
>> >> would be 30.
>> >> A good value for cfgP.minnrtrials is 12 (a minimum number of trials
>> >> is
>> >> needed to realize the permutation test for estimated TE values).
>> >>
>> >> % Optimization
>> >>
>> >> cfgP.optimizemethod = 'ragwitz';
>> >> cfgP.ragdim = 4:8;
>> >> cfgP.ragtaurange = [0.2 0.4];
>> >> cfgP.ragtausteps = 15;
>> >> cfgP.repPred = 100;
>> >> *
>> >> *
>> >> *I am completely at a loss for this. I've done some reading into
>> >> transfer entropy, mutual information, etc., cited in trentool, but
>> >> I'm
>> >> yet to understand how exactly this optimization works and what the
>> >> configuration should be, given my data and experimental
>> >> intentions.*
>> >> PW: The Ragwitz criterion tries to find optimal embedding
>> >> parameters
>> >> dim and tau for the data. To do that, the method iteratively takes
>> >> all
>> >> possible combinations of dim and tau values that are provided in
>> >> cfgP.ragdim and cfgP.ragtaurange/.ragtausteps and tests how well
>> >> these
>> >> combinations embed the data. To test an embedding, the method
>> >> builds
>> >> the embedding vectors from the data; it then tests for each point
>> >> how
>> >> well the next point in time can be predicted from the reference
>> >> point's nearest neighbours. So for each embedded point, the method
>> >> searches for the nearest neighbours and calculates the average of
>> >> those nearest neighbours. The difference between the
>> >> averaged/predicted point and the actual next point is the error of
>> >> the
>> >> local predictor. The Ragwitz criterion will then return the
>> >> parameter
>> >> combination for which this error over all points is minimal.
>> >> The parameters set the following: 'ragdim' are dimensions to be
>> >> tested
>> >> by the method (I would reccomend to start with 2:10), 'ragtaurange'
>> >> together with 'ragtausteps' specifies the tau values to be tested
>> >> (TRENTOOL will build a vector from 0.2 to 0.4 in 15 steps). Note,
>> >> that
>> >> the values here are factors that are later multiplied with the ACT
>> >> to
>> >> obtain the actual tau. 'repPred' is the number of points that will
>> >> be
>> >> used for the local prediction, i.e. the Ragwitz criterion will test
>> >> the local prediction and calculate the error for the first 100
>> >> points
>> >> in your time series. The two parameters 'flagNei' ans 'sizeNei'
>> >> below
>> >> specify the type of neighbour search conducted by the Ragwitz
>> >> criterion: 'flagNei' tells the method to either conduct a kNN or
>> >> range
>> >> search; 'sizeNei' specifies the number of neighbours or the radius
>> >> to
>> >> be searched by a range search.
>> >>
>> >>
>> >> % Kernel-based TE estimation
>> >>
>> >> cfgP.flagNei = 'Mass';
>> >> cfgP.sizeNei = 4; % Default
>> >>
>> >> cfgP.ensemblemethod = 'no';
>> >> cfgP.outputpath = *OUTPUT PATH*;
>> >>
>> >> if ~exist(*Path for TEprepare data object*)
>> >> load VSdat;
>> >> TE_Wrd = {};
>> >> for i = 1:nConds
>> >> for j = 1:Nsub
>> >> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> >> end
>> >> end
>> >> clear VSdat;
>> >> save('TE_Wrd', 'TE_Wrd');
>> >> end
>> >> *
>> >> *
>> >> *The configuration and virtual sensor data, organized in a 3 x 15
>> >> cell
>> >> of structures (condition by subject) are the input. The TEprepare
>> >> substructure is added to each individual condition x subject .mat
>> >> files' data structure which are stored on disk independently.*
>> >>
>> >> % Use object_to_mat_conversion.m to replace individual condition x
>> >> subject virtual sensor data
>> >> % .mat files with their TE_Wrd equivalent
>> >> *
>> >> *
>> >> *I'm using a separate script to make some manipulations to the
>> >> objects
>> >> from disk; this will all eventually be integrated into the main
>> >> pipeline*.*TRENTOOL seems to handle data output very differently
>> >> from
>> >> fieldtrip and I've had trouble thinking through the most logical
>> >> way
>> >> to handle the data so it's a bit haphazard right now.*
>> >>
>> >> load cond080sub01.mat
>> >>
>> >> cfgG = [];
>> >> cfgG.dim = cond080sub01.TEprepare.optdim;
>> >> cfgG.tau = cond080sub01.TEprepare.opttau;
>> >>
>> >> if isfield(cond080sub01, 'TEprepare')
>> >> TEgroup_prepare(cfgG, fileCell);
>> >> else
>> >> error('Need to run TEprepare before TEgroup_prepare');
>> >> end
>> >> *
>> >> *
>> >> *For clarification, fileCell is a cell with the name of each
>> >> condition
>> >> x subject .mat file, which as I said before is collectively the
>> >> same
>> >> as the 3 x 15 VSdat structure (condition x subject).*
>> >>
>> >> % Replace .mat files with '_for_TEgroup_calculate' version in
>> >> % object_to_mat_conversion.m
>> >>
>> >> % TE Group Calculate
>> >>
>> >> load cond080sub01.mat
>> >> if isfield(cond080sub01, 'TEgroupprepare')
>> >> for i = 1:length(fileCell)
>> >> TEgroup_calculate(fileCell{i});
>> >> end
>> >> else
>> >> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> >> end
>> >> *
>> >> *
>> >> *At this step I get the following error:
>> >>
>> >> Error using transferentropy (line 337)
>> >> \nTRENTOOL ERROR: not enough data points left after embedding
>> >>
>> >> Error in TEgroup_calculate (line 133)
>> >> [TEresult] = transferentropy(cfg,data);*
>> >>
>> >> % TE Group Stats
>> >>
>> >> cfgGSTAT = [];
>> >> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> >> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>> >>
>> >> cfgGSTAT.uvar = 1;
>> >> cfgGSTAT.ivar = 2;
>> >> cfgGSTAT.fileidout = 'test_groupstats';
>> >>
>> >> TEgroup_stats(cfgGSTAT, fileCell);
>> >>
>> >> *Given the error above, I am yet to get to this step, but it does
>> >> not
>> >> seem fundamentally different from normal fieldtrip stats.*
>> >>
>> >> *****
>> >>
>> >> In case my notes were not clear or you skipped to the bottom, *my
>> >> primary concern is whether the error I'm getting in
>> >> TEgroup_calculate
>> >> is a pipeline issue* (I noticed the example pipeline in trentool,
>> >> the
>> >> manual, and published methods articles all seem to have slightly or
>> >> significantly different pipeline compositions), *or if the error
>> >> is*
>> >> due to ACT, ragwitz optimization, or some other faulty
>> >> parameterization *on my part due to a lack of understanding of how
>> >> transfer entropy works on a more theoretical/mathematical level*.
>> >> If
>> >> the latter is the case, is there any relatively straightforward way
>> >> to
>> >> conceptualize this, or is this something where I'm just going to
>> >> have
>> >> to keep reading and rereading until it eventually makes sense? I've
>> >> already done quite a bit of that and it hasn't pierced my thick
>> >> skull
>> >> yet but I'm sure it will eventually!
>> >>
>> >> Thank you so much,
>> >>
>> >> Max Cantor
>> >>
>> >>
>> >> --
>> >> Max Cantor
>> >> Lab Manager
>> >> Computational Neurolinguistics Lab
>> >> University of Michigan
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > --
>> > ------------------------------------------------------------
>> >
>> > Patricia Wollstadt, PhD Student
>> >
>> > MEG Unit, Brain Imaging Center
>> >
>> > Goethe University, Frankfurt, Germany
>> >
>> > Heinrich Hoffmann Strasse 10, Haus 93 B
>> >
>> > D - 60528 Frankfurt am Main
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/ce6b2
> 97f/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 6
>> > Date: Tue, 9 Sep 2014 15:21:20 +0200
>> > From: Holger Krause <Holger.Krause at med.uni-duesseldorf.de>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: [FieldTrip] Latest FT version on ftp-server is from three
>> > days ago
>> > Message-ID:
>> > <201409091521.22589.Holger.Krause at med.uni-duesseldorf.de>
>> > Content-Type: text/plain; charset="us-ascii"
>> >
>> > Dear developers,
>> >
>> > Could you please check the process for creation/publication of
>> > zip-files on
>> > the FTP server? The latest file I can find there is
>> > fieldtrip-20140906.zip.
>> >
>> > Thanks in advance,
>> >
>> > Holger
>> >
>> >
>> > ------------------------------
>> >
>> > Message: 7
>> > Date: Tue, 9 Sep 2014 09:19:55 -0400
>> > From: Max Cantor <mcantor at umich.edu>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] TRENTOOL pipeline help
>> > Message-ID:
>> > <CAFTjRaUXzwca7rR3sm8iPLLyUxqHO9kkm++=zAU4LUW=r7NRBw at mail.gmail.com>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > This is immensely helpful, thank you. I was very confused about why
>> > some
>> > versions of the pipeline I saw were using group calculate and others
>> > were
>> > using interaction delay reconstruction and what that meant, and I
>> > think I
>> > have a more clear idea of what the different steps of the pipeline
>> > are
>> > doing. There are still a few things I'm a bit confused about though
>> > in
>> > terms of the pipeline. For instance, whether or not I need to do
>> > TEprepare
>> > before group prepare, and if I need to do graph analysis (which I'm
>> > not
>> > sure I fully understand but also haven't looked deeply into) before
>> > group
>> > stats.
>> >
>> > If you don't mind me taking you up on your offer, I think seeing
>> > your
>> > example script might help clarify some of these issues.
>> >
>> > Thank you!
>> >
>> > On Tue, Sep 9, 2014 at 8:16 AM, Patricia Wollstadt <
>> > Patricia.Wollstadt at gmx.de> wrote:
>> >
>> >> Hello Max,
>> >>
>> >> I added a few comments to the questions regarding individual
>> >> parameters
>> >> below. To address the general problem of TRENTOOL telling you, that
>> >> there
>> >> are not enough sample points in your data: From what I can see in
>> >> your
>> >> script, you probably don't have enough data points in each time
>> >> series to
>> >> robustly estimate TE. You analyze 800 ms of data sampled at 300 Hz,
>> >> which
>> >> gives you 240 samples per time series. Can you maybe avoid
>> >> downsampling to
>> >> 300 Hz and downsample to 600 Hz instead? Or could you analyze a
>> >> longer time
>> >> window of interest?
>> >> Note that you also 'lose' data to embedding and the interaction
>> >> delay: The
>> >> first point that can be used for TE estimation is at max. embedding
>> >> length
>> >> + max. interaction delay in samples. For example: max. embedding
>> >> length =
>> >> dim * tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the max
>> >> interaction
>> >> delay of 30 ms = 9 samples. In this example, you would be left with
>> >> 240 -
>> >> 29 samples for TE estimation per trial. There is also the
>> >> possibility to
>> >> estimate time resolved TE/TE for shorter time windows of interest
>> >> (see
>> >> section 4.4 in the manual); however, this method requires the use
>> >> of a GPU
>> >> for TE estimation.
>> >>
>> >> I would further recommend to use the new pipeline for group
>> >> statistics
>> >> described in the manual in section 4.5 (the function
>> >> 'TEgroup_calculate' is
>> >> deprecated). The new pipeline allows you to reconstruct the
>> >> interaction
>> >> delay and uses the following functions (see also comments in the
>> >> script):
>> >>
>> >> TEgroup_prepare -> prepares all data sets (all subjects/all
>> >> conditions)
>> >> for group analysis (this means finding common embedding parameters
>> >> such
>> >> that estimates are not biased between groups)
>> >> InteractionDelayReconstruction_calculate -> estimates TE for
>> >> individual data sets and all assumed interaction delays u
>> >> InteractionDelayReconstruction_analyze -> reconstructs the
>> >> interaction delay by selecting the u that maximizes TE for each
>> >> channel
>> >> TEgroup_stats -> calculate group statistics using a permutation
>> >> test
>> >>
>> >> I can send you an example script for group TE analysis using this
>> >> pipeline
>> >> to get you started. I hope this helps you to get the group analysis
>> >> running. Just write again if you're having trouble setting up the
>> >> pipeline
>> >> or something is not clear about the parameters/my comments.
>> >>
>> >> Best,
>> >> Patricia
>> >>
>> >>
>> >>
>> >>
>> >> On 09/04/2014 08:30 PM, Max Cantor wrote:
>> >>
>> >> Hi fieldtrippers,
>> >>
>> >> I know trentool is not produced by the Donders Institute, so I'm
>> >> not 100%
>> >> sure if it is appropriate to ask questions about it here, but to
>> >> the best
>> >> of my knowledge they do not have a mailing list and I saw a few
>> >> trentool
>> >> questions in the archives, so I'm going to assume it's ok...
>> >>
>> >> In any case, below is my current pipeline (slightly modified for
>> >> comprehensibility):
>> >>
>> >> (notes in bold are comments/questions made in this email, not
>> >> present in
>> >> the pipeline. Sorry in advance for the long post! Any help would be
>> >> greatly
>> >> appreciated as I'm a bit over my head on this but I think I'm
>> >> close!)
>> >>
>> >> *****
>> >>
>> >> % Prepare group TE data
>> >>
>> >> cfgP = [];
>> >> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> >> cfgP.TEcalctype = 'VW_ds';
>> >> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>> >>
>> >> *I'm trying to find the transfer entropy between the left and right
>> >> auditory cortices in my experiment. The input is virtual sensor
>> >> data that
>> >> was produced using SAM in fieldtrip on real MEG data. *
>> >>
>> >> % specify u to be scanned
>> >>
>> >> cfgP.predicttime_u = 30;
>> >> cfgP.toi = [-0.4 0.4];
>> >>
>> >> *For clarification, the predicttime_u is in seconds but the toi
>> >> is in
>> >> milliseconds. If I understand correctly, the predicttime_u must fit
>> >> within
>> >> the toi, but beyond that are there any benefits to it being earlier
>> >> or
>> >> later?* PW: The predictiontime_u is in milliseconds and the toi is
>> >> in
>> >> seconds. The prediction time is the assumed interaction delay
>> >> between your
>> >> two sources and should fit within your toi. In general it is
>> >> preferable to
>> >> use the method for interaction delay reconstruction for TE
>> >> estimation,
>> >> because it allows you to reconstruct the actual delay between your
>> >> source
>> >> and target times series. A non-optimal u/interaction delay may
>> >> cause an
>> >> underestimation of TE, so it is recommended to use the pipeline for
>> >> interaction delay reconstruction whenever estimating TE for unknown
>> >> delays.
>> >> If you use the methods for interaction delay reconstruction
>> >> 'predicttime_u' is replaced by
>> >> cfgTEP.predicttimemin_u % minimum u to be scanned
>> >> cfgTEP.predicttimemax_u % maximum u to be scanned
>> >> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> >> A large range for u values to be scanned increases computing time a
>> >> lot,
>> >> so it is best to limit the u range to values that are
>> >> physiologically
>> >> plausible.
>> >>
>> >>
>> >> % ACT (Autocorrelation Time) estimation and constraints
>> >>
>> >> cfgP.maxlag = 150;
>> >> cfgP.actthrvalue = 7.5;
>> >> cfgP.minnrtrials = 5;
>> >>
>> >> *My understanding is maxlag should be 1/2 the sampling rate, so
>> >> since
>> >> the data are downsampled to 300hz, it should be 150. I know that
>> >> the sample
>> >> rate and filters are used to determine the actthrvalue, but I don't
>> >> actually know the calculation. 7.5 was a rough guess just to test
>> >> the
>> >> pipeline. I'm also uncertain of what minnrtrials should be.* PW:
>> >> You can
>> >> set the actthrvalue based on the filtering you did prior to TE
>> >> analysis. If
>> >> you for example highpass filtered at 10 Hz, you shouldn't find an
>> >> ACT
>> >> higher than 30 samples, because you filtered out any components of
>> >> the
>> >> signal slower than 10 Hz/30 samples (given your sampling frequency
>> >> of 300
>> >> Hz). So in this scenario the actthrvalue would be 30.
>> >> A good value for cfgP.minnrtrials is 12 (a minimum number of trials
>> >> is
>> >> needed to realize the permutation test for estimated TE values).
>> >>
>> >>
>> >> % Optimization
>> >>
>> >> cfgP.optimizemethod = 'ragwitz';
>> >> cfgP.ragdim = 4:8;
>> >> cfgP.ragtaurange = [0.2 0.4];
>> >> cfgP.ragtausteps = 15;
>> >> cfgP.repPred = 100;
>> >>
>> >> *I am completely at a loss for this. I've done some reading into
>> >> transfer entropy, mutual information, etc., cited in trentool, but
>> >> I'm yet
>> >> to understand how exactly this optimization works and what the
>> >> configuration should be, given my data and experimental
>> >> intentions.* PW:
>> >> The Ragwitz criterion tries to find optimal embedding parameters
>> >> dim and
>> >> tau for the data. To do that, the method iteratively takes all
>> >> possible
>> >> combinations of dim and tau values that are provided in cfgP.ragdim
>> >> and
>> >> cfgP.ragtaurange/.ragtausteps and tests how well these combinations
>> >> embed
>> >> the data. To test an embedding, the method builds the embedding
>> >> vectors
>> >> from the data; it then tests for each point how well the next point
>> >> in time
>> >> can be predicted from the reference point's nearest neighbours. So
>> >> for each
>> >> embedded point, the method searches for the nearest neighbours and
>> >> calculates the average of those nearest neighbours. The difference
>> >> between
>> >> the averaged/predicted point and the actual next point is the error
>> >> of the
>> >> local predictor. The Ragwitz criterion will then return the
>> >> parameter
>> >> combination for which this error over all points is minimal.
>> >> The parameters set the following: 'ragdim' are dimensions to be
>> >> tested by
>> >> the method (I would reccomend to start with 2:10), 'ragtaurange'
>> >> together
>> >> with 'ragtausteps' specifies the tau values to be tested (TRENTOOL
>> >> will
>> >> build a vector from 0.2 to 0.4 in 15 steps). Note, that the values
>> >> here are
>> >> factors that are later multiplied with the ACT to obtain the actual
>> >> tau.
>> >> 'repPred' is the number of points that will be used for the local
>> >> prediction, i.e. the Ragwitz criterion will test the local
>> >> prediction and
>> >> calculate the error for the first 100 points in your time series.
>> >> The two
>> >> parameters 'flagNei' ans 'sizeNei' below specify the type of
>> >> neighbour
>> >> search conducted by the Ragwitz criterion: 'flagNei' tells the
>> >> method to
>> >> either conduct a kNN or range search; 'sizeNei' specifies the
>> >> number of
>> >> neighbours or the radius to be searched by a range search.
>> >>
>> >>
>> >> % Kernel-based TE estimation
>> >>
>> >> cfgP.flagNei = 'Mass';
>> >> cfgP.sizeNei = 4; % Default
>> >>
>> >> cfgP.ensemblemethod = 'no';
>> >> cfgP.outputpath = *OUTPUT PATH*;
>> >>
>> >> if ~exist(*Path for TEprepare data object*)
>> >> load VSdat;
>> >> TE_Wrd = {};
>> >> for i = 1:nConds
>> >> for j = 1:Nsub
>> >> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> >> end
>> >> end
>> >> clear VSdat;
>> >> save('TE_Wrd', 'TE_Wrd');
>> >> end
>> >>
>> >> *The configuration and virtual sensor data, organized in a 3 x 15
>> >> cell
>> >> of structures (condition by subject) are the input. The TEprepare
>> >> substructure is added to each individual condition x subject .mat
>> >> files'
>> >> data structure which are stored on disk independently.*
>> >>
>> >> % Use object_to_mat_conversion.m to replace individual condition x
>> >> subject
>> >> virtual sensor data
>> >> % .mat files with their TE_Wrd equivalent
>> >>
>> >> *I'm using a separate script to make some manipulations to the
>> >> objects
>> >> from disk; this will all eventually be integrated into the main
>> >> pipeline*.*
>> >> TRENTOOL seems to handle data output very differently from
>> >> fieldtrip and
>> >> I've had trouble thinking through the most logical way to handle
>> >> the data
>> >> so it's a bit haphazard right now.*
>> >>
>> >> load cond080sub01.mat
>> >>
>> >> cfgG = [];
>> >> cfgG.dim = cond080sub01.TEprepare.optdim;
>> >> cfgG.tau = cond080sub01.TEprepare.opttau;
>> >>
>> >> if isfield(cond080sub01, 'TEprepare')
>> >> TEgroup_prepare(cfgG, fileCell);
>> >> else
>> >> error('Need to run TEprepare before TEgroup_prepare');
>> >> end
>> >>
>> >> *For clarification, fileCell is a cell with the name of each
>> >> condition x
>> >> subject .mat file, which as I said before is collectively the same
>> >> as the 3
>> >> x 15 VSdat structure (condition x subject).*
>> >>
>> >> % Replace .mat files with '_for_TEgroup_calculate' version in
>> >> % object_to_mat_conversion.m
>> >>
>> >> % TE Group Calculate
>> >>
>> >> load cond080sub01.mat
>> >> if isfield(cond080sub01, 'TEgroupprepare')
>> >> for i = 1:length(fileCell)
>> >> TEgroup_calculate(fileCell{i});
>> >> end
>> >> else
>> >> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> >> end
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> *At this step I get the following error: Error using
>> >> transferentropy (line
>> >> 337) \nTRENTOOL ERROR: not enough data points left after embedding
>> >> Error in
>> >> TEgroup_calculate (line 133) [TEresult] =
>> >> transferentropy(cfg,data);*
>> >>
>> >> % TE Group Stats
>> >>
>> >> cfgGSTAT = [];
>> >> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> >> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>> >>
>> >> cfgGSTAT.uvar = 1;
>> >> cfgGSTAT.ivar = 2;
>> >> cfgGSTAT.fileidout = 'test_groupstats';
>> >>
>> >> TEgroup_stats(cfgGSTAT, fileCell);
>> >>
>> >> *Given the error above, I am yet to get to this step, but it does
>> >> not
>> >> seem fundamentally different from normal fieldtrip stats.*
>> >>
>> >> *****
>> >>
>> >> In case my notes were not clear or you skipped to the bottom, *my
>> >> primary concern is whether the error I'm getting in
>> >> TEgroup_calculate is a
>> >> pipeline issue* (I noticed the example pipeline in trentool, the
>> >> manual,
>> >> and published methods articles all seem to have slightly or
>> >> significantly
>> >> different pipeline compositions), *or if the error is* due to ACT,
>> >> ragwitz optimization, or some other faulty parameterization *on my
>> >> part
>> >> due to a lack of understanding of how transfer entropy works on a
>> >> more
>> >> theoretical/mathematical level*. If the latter is the case, is
>> >> there any
>> >> relatively straightforward way to conceptualize this, or is this
>> >> something
>> >> where I'm just going to have to keep reading and rereading until it
>> >> eventually makes sense? I've already done quite a bit of that and
>> >> it hasn't
>> >> pierced my thick skull yet but I'm sure it will eventually!
>> >>
>> >> Thank you so much,
>> >>
>> >> Max Cantor
>> >>
>> >>
>> >> --
>> >> Max Cantor
>> >> Lab Manager
>> >> Computational Neurolinguistics Lab
>> >> University of Michigan
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing
>> >>
> listfieldtrip at donders.ru.nlhttp://mailman.science.ru.nl/mailman/listinfo/fie
> ldtrip
>> >>
>> >>
>> >> --
>> >> ------------------------------------------------------------
>> >>
>> >> Patricia Wollstadt, PhD Student
>> >>
>> >> MEG Unit, Brain Imaging Center Goethe University, Frankfurt,
>> >> Germany
>> >>
>> >> Heinrich Hoffmann Strasse 10, Haus 93 B D - 60528 Frankfurt am
>> >> Main
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >
>> >
>> >
>> > --
>> > Max Cantor
>> > Lab Manager
>> > Computational Neurolinguistics Lab
>> > University of Michigan
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/29e18
> 024/attachment.html>
>> >
>> > ------------------------------
>> >
>> > _______________________________________________
>> > fieldtrip mailing list
>> > fieldtrip at donders.ru.nl
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > End of fieldtrip Digest, Vol 46, Issue 3
>> > ****************************************
>> >
>>
>>
>>
>> Barbara Schorr, MSc
>> Clinical and Biological Psychology
>> University of Ulm
>> Albert-Einstein-Allee 47
>> 89069 Ulm
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> --
> Jim Herring, MSc.
> Neuronal Oscillations Group
> Centre for Cognitive Neuroimaging
> Donders Institute for Brain, Cognition and Behaviour
> Radboud University Nijmegen
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> ------------------------------
>
> Message: 8
> Date: Mon, 15 Sep 2014 12:53:19 -0500
> From: "Jing Fan" <jingfan.jf at gmail.com>
> To: "'FieldTrip discussion list'" <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] ft_globalmeanfield : Undefined function of
> variable 'abort'
> Message-ID: <000301cfd10d$ef53cba0$cdfb62e0$@gmail.com>
> Content-Type: text/plain; charset="us-ascii"
>
> Hi Barbara and Jim,
>
> I added path your/path/to/fieldtrip/utilities and now it worked.
> Hope it helps solve the problem.
>
> Best,
> Jing
>
> -----Original Message-----
> From: fieldtrip-bounces at science.ru.nl
> [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Herring, J.D. (Jim)
> Sent: Monday, September 15, 2014 5:01 AM
> To: FieldTrip discussion list
> Subject: Re: [FieldTrip] ft_globalmeanfield : Undefined function of variable
> 'abort'
>
> Dear Barbara,
>
> The error seems to be related to a bug that pops up now and then
> (http://bugzilla.fcdonders.nl/show_bug.cgi?id=2580) which could be due to a
> corrupt installation of fieldtrip and/or the need to update fieldtrip.
>
> Could you please try downloading the most recent version of fieldtrip and
> see if the problem persists?
>
> For an example of what can be expected from this function see: Esser SK,
> Huber R, Massimini M, Peterson MJ, Ferrarelli F, Tononi G. A direct
> demonstration of cortical LTP in humans: a combined TMS/EEG study. Brain Res
> Bull. 2006 Mar 15;69(1):86-94. Epub 2005 Dec 1. PubMed PMID: 16464689.
>
> Best,
>
> Jim
>
>
> ----- Oorspronkelijk bericht -----
>> Van: "barbara schorr" <barbara.schorr at uni-ulm.de>
>> Aan: fieldtrip at science.ru.nl
>> Verzonden: Woensdag 10 september 2014 14:07:14
>> Onderwerp: [FieldTrip] ft_globalmeanfield : Undefined function of variable
> 'abort'
>> Dear fieldtripers.
>>
>> as mentioned in the subject line I would like to anaylse my timelocked
>> ERP Data with the function ft_globalmeanfield.
>>
>> I get the Error: Undefined function or variable 'abort'
>> Error in ft_globalmeanfield (line84)
>> if abort
>>
>> in the function it says:
>>
>> %the abort variable is set to true or false in ft_preamble_inti
>> if abort
>> % do not continue function execution in case the outputfile is
>> present and the user indicated to keep it
>> return
>> end
>>
>>
>> I don't understand what this means. I would really appreciate your
>> help to get this running.
>>
>> Also, what is the result when I run this function on my data? (This
>> method was proposed from a reviewer. I never used it before, so I
>> don't exactly know what it does and what the result will look like).
>>
>> Thank you very much in advance!
>>
>> Best,
>>
>> Barbara Schorr
>>
>>
>>
>>
>> Zitat von fieldtrip-request at science.ru.nl:
>>
>> > Send fieldtrip mailing list submissions to
>> > fieldtrip at science.ru.nl
>> >
>> > To subscribe or unsubscribe via the World Wide Web, visit
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> > or, via email, send a message with subject or body 'help' to
>> > fieldtrip-request at science.ru.nl
>> >
>> > You can reach the person managing the list at
>> > fieldtrip-owner at science.ru.nl
>> >
>> > When replying, please edit your Subject line so it is more specific
>> > than "Re: Contents of fieldtrip digest..."
>> >
>> >
>> > Today's Topics:
>> >
>> > 1. Re: Question regarding nonparametric testing for coherence
>> > differences (Eric Maris)
>> > 2. Re: Problem with Geodesic 129 layout!! (KatrinH Heimann)
>> > 3. Re: Problem with Geodesic 129 layout!! (Joseph Dien)
>> > 4. Re: ft_rejectvisual: removing trials marked as bad
>> > (Caspar, Emilie)
>> > 5. Re: TRENTOOL pipeline help (Patricia Wollstadt)
>> > 6. Latest FT version on ftp-server is from three days ago
>> > (Holger Krause)
>> > 7. Re: TRENTOOL pipeline help (Max Cantor)
>> >
>> >
>> > ----------------------------------------------------------------------
>> >
>> > Message: 1
>> > Date: Mon, 8 Sep 2014 15:46:11 +0200 (CEST)
>> > From: "Eric Maris" <e.maris at psych.ru.nl>
>> > To: <hweeling.lee at gmail.com>, "'FieldTrip discussion list'"
>> > <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Question regarding nonparametric testing
>> > for
>> > coherence differences
>> > Message-ID: <015901cfcb6b$3fdf1f00$bf9d5d00$@maris at psych.ru.nl>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > Dear Hwee Ling,
>> >
>> >
>> >
>> > In the paper you refer to (Maris et al, JNM, 2007), I did not
>> > perform a
>> > cluster-based permutation test at the level of the channel pairs.
>> > Instead,
>> > all coherences mentioned in the paper are coherence between a single
>> > EMG
>> > channel (2 electrodes mounted on the forearm) and all MEG
>> > electrodes. Thus,
>> > there are as many EMG-MEG coherences as there are MEG channels (275
>> > in our
>> > lab), and the clustering of these coherences involves the same
>> > neighbourhood
>> > structure as the clustering of channel-specific statistics on power
>> > or
>> > evoked response. The analysis you propose involves clustering in a
>> > much more
>> > complex space, namely the formed by all (MEG,MEG) channel pairs
>> > (275*275=75625 pairs). In the early days of cluster-based
>> > permutation
>> > statistics, I have worked on this problem but did not pursue it
>> > because the
>> > resulting clusters are very hard to interpret.
>> >
>> >
>> >
>> > Best,
>> >
>> >
>> >
>> > Eric Maris
>> >
>> >
>> >
>> >
>> >
>> > Dear all and Prof Maris,
>> >
>> > I'm re-posting this question again, as I didn't get any replies
>> > previously.
>> >
>> > I?m interested to investigate if there are any differences in phase
>> > synchronization in resting state data at timepoint 2 versus
>> > timepoint 1. The
>> > EEG data was collected using 64 EEG channels, resting state, eyes
>> > opened and
>> > eyes closed. I?ve arbitrarily segmented the resting state data into
>> > epochs
>> > of 2s each, and the epochs with artifacts are excluded completely
>> > from
>> > further analyses. I then performed mtmfft to get the fourier
>> > representation
>> > of the data, extracted the coherence, and compared the coherence
>> > difference
>> > of timepoint 2 versus timepoint 1 of all channels paired with all
>> > other
>> > channels.
>> >
>> > I figured that if I extract the connectivity analyses without
>> > specifying the
>> > channelcmb, I get a 'chan_chan_freq' dimord variable that would
>> > allow me to
>> > perform cluster-based statistical analyses. I get an output with
>> > ?chan_chan?
>> > dimord variable. However, I was not 100% sure that this is correct,
>> > hence, I?ve
>> > inserted my code (see below). It?ll be great if you could tell me if
>> > what I?m
>> > doing makes any sense at all. Also, I don?t know how I can plot the
>> > results
>> > to see if it make any sense at all.
>> >
>> > Also, I checked the values obtained from when I specified
>> > "cfg.channelcmb"
>> > and when I did not specify "cfg.channelcmb", I noticed that the
>> > values were
>> > somehow different. I would assume that the values to be similar,
>> > although
>> > I'm not sure why they would have differences in the values obtained
>> > from
>> > specifying "cfg.channelcmb".
>> >
>> > This is my code that I've used thus far:
>> >
>> > for sub = 1:5
>> >
>> > cfg = [];
>> >
>> > cfg.output = 'fourier';
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.method = 'mtmfft';
>> >
>> > cfg.keeptrials = 'yes';
>> >
>> > cfg.tapsmofrq = 5;
>> >
>> > cfg.foilim = [0 100];
>> >
>> > cfg.taper = 'dpss';
>> >
>> > % find the index for the c200 condition
>> >
>> > pre_c200_idx = find(data5.trialinfo == 201);
>> >
>> > cfg.trials = pre_c200_idx;
>> >
>> > HLF_pre_c200 = ft_freqanalysis(cfg, data5);
>> >
>> > post_c200_idx = find(data5.trialinfo == 200);
>> >
>> > cfg.trials = post_c200_idx;
>> >
>> > HLF_post_c200 = ft_freqanalysis(cfg, data5);
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.keeptrials = 'no';
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.removemean = 'yes';
>> >
>> > cfg.method = 'coh';
>> >
>> > HLF_pre_c200coh{sub} = ft_connectivityanalysis(cfg,
>> > HLF_pre_c200);
>> >
>> > HLF_post_c200coh{sub} = ft_connectivityanalysis(cfg,
>> > HLF_post_c200);
>> >
>> > end
>> >
>> >
>> >
>> >
> load('D:\Hweeling_Programs\fieldtrip-20140330\template\layout\easycapM11.mat
> ');
>> >
>> > cfg_neighb.method = 'template';
>> >
>> > cfg_neighb.layout = lay;
>> >
>> > cfg_neighb.channel = 'all';
>> >
>> > neighbours = ft_prepare_neighbours(cfg_neighb,
>> > sub_HLF_pre_c200coh{1});
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay;
>> >
>> > cfg.neighbours = neighbours;
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.channelcmb = {cfg.channel, cfg.channel};
>> >
>> > cfg.latency = 'all';
>> >
>> > cfg.avgovertime = 'no';
>> >
>> > cfg.avgoverchan = 'no';
>> >
>> > cfg.parameter = 'cohspctrm';
>> >
>> > cfg.method = 'montecarlo';
>> >
>> > cfg.statistic = 'depsamplesT';
>> >
>> > cfg.correctm = 'cluster';
>> >
>> > cfg.tail = 0;
>> >
>> > % cfg.clustertail = 0;
>> >
>> > cfg.alpha = 0.05/8; % to correct for multiple comparisons across 8
>> > frequency
>> > bands.
>> >
>> > cfg.numrandomization = 10000;
>> >
>> > cfg.ivar = 2;
>> >
>> > cfg.uvar = 1;
>> >
>> >
>> >
>> > % design matrices
>> >
>> > clear design;
>> >
>> > design(1,:) = [1:5, 1:5];
>> >
>> > design(2,:) = [ones(1,5), ones(1,5) * 2];
>> >
>> > cfg.design = design;
>> >
>> > % for theta band
>> >
>> > cfg.avgoverfreq = 'yes';
>> >
>> > cfg.frequency = [4 8]; % I also performed the statistics for delta
>> > (2-4),
>> > alpha (8-10.5), alpha (10.5-13), beta (13-20), beta (20-30), gamma
>> > (30-40),
>> > and gamma (40-100).
>> >
>> > [diffc200_theta_stat] = ft_freqstatistics(cfg,
>> > sub_HLF_post_c200coh{:},
>> > sub_HLF_pre_c200coh{:});
>> >
>> >
>> >
>> > When I tried to plot the results, I used this code:
>> >
>> > cfg = [];
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.layout = 'lay';
>> >
>> > cfg.zlim = [-1 1];
>> >
>> > cfg.alpha = 0.05;
>> >
>> > cfg.refchannel = 'all';
>> >
>> > ft_clusterplot(cfg, diffc200_theta_stat);
>> >
>> > However, I was not sure how I could plot the results. I get an error
>> > message
>> > from Fieldtrip when using ft_clusterplot:
>> >
>> > Error using topoplot_common (line 366)
>> >
>> > no reference channel is specified
>> >
>> > Error in ft_topoplotTFR (line 192)
>> >
>> > [cfg] = topoplot_common(cfg, varargin{:});
>> >
>> > Error in ft_clusterplot (line 372)
>> >
>> > ft_topoplotTFR(cfgtopo, stat);
>> >
>> >
>> >
>> > According to your paper in 2007, the topoplot of the results were
>> > masked by
>> > the spatio-spectral pattern of the significant clusters. I don't
>> > know how to
>> > do this, and I would really appreciate if you can show me how to
>> > make such a
>> > plot.
>> >
>> > Thank you very much.
>> >
>> > Kind regards,
>> >
>> > Hweeling
>> >
>> >
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140908/98b85
> 280/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 2
>> > Date: Mon, 8 Sep 2014 21:38:01 +0200
>> > From: KatrinH Heimann <katrinheimann at gmail.com>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Problem with Geodesic 129 layout!!
>> > Message-ID:
>> > <CACnHcBeqEoQMirfZ-hCKK8rSmbfcNYzxFQH1mmSaeGCuKMN-uA at mail.gmail.com>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > Dear Joseph, thanks so much, I really appreciate your help. I was
>> > also
>> > wandering, if maybe there is another bug in my code.
>> > Our nets are 128 channels, Hydrocel, but as a colleague of me
>> > adviced me to
>> > do so, I also tried the 129 layout.
>> > My code you find below (without selection of bad channels etc.)
>> > If you need also the data of two subjects to have a look, let me
>> > know!!!
>> > Best and thanks really!
>> > Katrin
>> >
>> >
>> > subject=input('Which subject do you want to analyse? ','s');
>> >
>> > name = strcat(subject,'.raw');
>> >
>> > subjectnumber=input('Which is the subjectnumber?', 's');
>> >
>> > sb=subjectnumber
>> >
>> > %subjectnumber = strcat(subjectnumber, '.txt')
>> >
>> > %%
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.dataset = name;
>> >
>> > cfg.trialfun = 'ft_trialfun_general'; % this is the default
>> >
>> > cfg.trialdef.eventtype = 'trigger';
>> >
>> > cfg.trialdef.eventvalue = 'stim'; % the value of the stimulus
>> > trigger
>> >
>> > cfg.trialdef.prestim = 0.234; % in seconds
>> >
>> > cfg.trialdef.poststim = 7.266; % in seconds (whole time of video
>> > observation, but maybe we need less)
>> >
>> > cfg = ft_definetrial(cfg);
>> >
>> >
>> > cfg.trl([1:7],:) = [];
>> >
>> >
>> > %change timeline according to constant offset of 16 ms = 8 samples
>> > (because
>> > recorded with 500 hz)in
>> >
>> > %structure trial
>> >
>> > cfg.trl(:,3)=cfg.trl(:,3)-8
>> >
>> >
>> > %change timeline to make the cut the zeropoint
>> >
>> >
>> > cfg.trl(:,3)=cfg.trl(:,3)- 1000;
>> >
>> >
>> >
>> > %% preprocess data
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.detrend = 'yes';
>> >
>> > cfg.preproc.demean = 'yes';
>> >
>> > cfg.preproc.baselinewindow = [-2.1 -2.0]
>> >
>> > %cfg.preproc.bpfilter = 'yes'; - the data here has to be already
>> > filtered
>> >
>> > %(0.1-45) and bad channels have to be replaced!!!!
>> >
>> > %cfg.preproc.bpfreq = [6 32];
>> >
>> > %
>> >
>> > %
>> >
>> > obs_data = ft_preprocessing(cfg);
>> >
>> > %
>> >
>> > save (strcat(sb,'obs_data') , 'obs_data')
>> >
>> >
>> > %% determining channel neighbours (necessary for Artifact detection)
>> >
>> > cfg = [];
>> >
>> > cfg.channel = obs_data.label;
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.feedback = 'yes';
>> >
>> > lay = ft_prepare_layout(cfg);
>> >
>> >
>> >
>> > cfg_neighb = [];
>> >
>> > cfg_neighb.feedback = 'yes';
>> >
>> >
>> > cfg_neighb.method = 'triangulation';
>> >
>> > cfg_neighb.layout = 'GSN128.sfp';
>> >
>> > neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >
>> >
>> > obs_data.elec = ft_read_sens('GSN128.sfp');
>> >
>> >
>> > %% Artifacts - to detect bad channels - is not saved!!!
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs_data_try = ft_rejectvisual(cfg, obs_data);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_try = ft_rejectartifact (cfg,obs_data);
>> >
>> >
>> >
>> > %% Preparing neighbours for channel repair - but bad channel info
>> > in!!!
>> >
>> >
>> > % cfg_neighb = [];
>> >
>> > % cfg_neighb.feedback = 'yes';
>> >
>> > % cfg_neighb.method = 'triangulation';
>> >
>> > % cfg_neighb.layout = 'GSN-HydroCel-128.sfp';
>> >
>> > % neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >
>> >
>> > % Interpolate and put into new data structure
>> >
>> > cfg = [];
>> >
>> > cfg.badchannel = {};
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.method = 'nearest';
>> >
>> > cfg.neighbours = neighbours;
>> >
>> > obs_data_channelrepaired = ft_channelrepair(cfg,obs_data)
>> >
>> >
>> > % Check reconstruction
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >
>> >
>> > % dat1=obs_data;
>> >
>> > % dat2=obs_data_channelrepaired;
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(62,:); % 68 is channel index of E68
>> >
>> > % y=dat2.trial{1}(62,:);
>> >
>> > % plot(x);hold on;plot(y,'r');
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(72,:);
>> >
>> > % y=dat2.trial{1}(75,:);
>> >
>> > % figure;
>> >
>> > % plot(x);hold on;plot(y,'r')
>> >
>> > %
>> >
>> > % x=dat1.trial{1}(75,:);
>> >
>> > % y=dat2.trial{1}(75,:);
>> >
>> > % figure;
>> >
>> > % plot(x);hold on;plot(y,'r')
>> >
>> > %% artifact rejection/trial inspection - throw out electrode jumps
>> > etc.
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >
>> >
>> > %% ICA - Anzahl der Komponenten anpassen!- adapt numcomponent
>> > according to
>> > channels interpolated %% 128-number of interp. channels)
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.channel = {'all'};
>> >
>> > cfg.numcomponent = 128
>> >
>> > comp = ft_componentanalysis(cfg,obs_data_clean1);
>> >
>> > save (strcat(sb,'comp_all') , 'comp')
>> >
>> >
>> > %%
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'component';
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='some';
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > ft_databrowser(cfg,comp);
>> >
>> >
>> > %% poweranalysis components
>> >
>> > cfg = [];
>> >
>> > cfg.output = 'pow';
>> >
>> > cfg.channel = 'all';%compute the power spectrum in all ICs
>> >
>> > cfg.method = 'mtmfft';
>> >
>> > cfg.taper = 'hanning';
>> >
>> > cfg.foi = 0:0.2:50;
>> >
>> > obs_freq = ft_freqanalysis(cfg, comp);
>> >
>> >
>> > %And you can plot the spectra:
>> >
>> >
>> > %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>> >
>> > nsubplots = 16;
>> >
>> > nbyn = sqrt(nsubplots);% sqrt(nsubplots) should not contain
>> > decimals, type
>> > doc subplot
>> >
>> >
>> > Nfigs = ceil(size(comp.topo,1)/nsubplots);
>> >
>> > tot = Nfigs*nsubplots;
>> >
>> >
>> > rptvect = 1:size(comp.topo,1);
>> >
>> > rptvect = padarray(rptvect, [0 tot-size(comp.topo,1)], 0,'post');
>> >
>> > rptvect = reshape(rptvect,nsubplots,Nfigs)';
>> >
>> >
>> > for r=1:size(rptvect,1);
>> >
>> > figure;set(gcf,'units','normalized','outerposition',[0 0 1
>> > 1]);%full
>> > screen
>> >
>> > k=0;
>> >
>> > for j=1:size(rptvect,2);
>> >
>> > if~(rptvect(r,j)==0);
>> >
>> > k=k+1;
>> >
>> > cfg=[];
>> >
>> > cfg.channel = rptvect(r,j);
>> >
>> > cfg.ylim =[0 500]
>> >
>> > cfg.xlim =[0 50]
>> >
>> > subplot(nbyn,nbyn,k);ft_singleplotER(cfg,obs_freq);
>> >
>> > end
>> >
>> > end
>> >
>> > end
>> >
>> >
>> > %For the IC topos you'll follow the same logic as above but with:
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [1:20]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [21:40]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [41:60]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> > figure
>> >
>> > cfg = [];
>> >
>> > cfg.component = [61:80]; % specify the component(s) that should be
>> > plotted
>> >
>> > cfg.layout = 'GSN128.sfp';
>> >
>> > cfg.comment = 'no';
>> >
>> > ft_topoplotIC(cfg, comp)
>> >
>> >
>> >
>> >
>> >
>> >
>> > %% Seperate observation conditions
>> >
>> >
>> > name1= strcat(num2str(sb),'_90.xls');
>> >
>> > list1=xlsread (name1)
>> >
>> >
>> >
>> >
>> >
>> > name2= strcat(num2str(sb),'_180.xls');
>> >
>> > list2=xlsread (name2)
>> >
>> >
>> >
>> > %
>> >
>> >
>> > cfg = []
>> >
>> > cfg.trials = [list1]
>> >
>> > obs90_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.trials = [list2]
>> >
>> > obs180_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >
>> >
>> > %%PIPELINE FOR obs90
>> >
>> >
>> > %% Decompose original data according to the components found before
>> >
>> > load (strcat(sb,'comp_all'))
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.numcomponent = 128;
>> >
>> > cfg.unmixing = comp.unmixing;
>> >
>> > cfg.topolabel = comp.topolabel;
>> >
>> >
>> > comp_1 = ft_componentanalysis(cfg, obs90_data);
>> >
>> >
>> >
>> > %% Reject component
>> >
>> > cfg = [];
>> >
>> > cfg.component = [];
>> >
>> > obs90_data_ica_cleaned = ft_rejectcomponent(cfg, comp_1,
>> > obs90_data);
>> >
>> >
>> >
>> >
>> > save (strcat(sb,'obs90_ica_cleaned') , 'obs90_data_ica_cleaned')
>> >
>> >
>> >
>> >
>> > %% Artifacts - final detection
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs90_data_clean3 = ft_rejectvisual(cfg, obs90_data_ica_cleaned);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs90_data_clean3);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs90_data_clean2 = ft_rejectartifact (cfg,obs90_data_clean3);
>> >
>> >
>> >
>> > % Save clean data
>> >
>> >
>> > save (strcat(subject,'obs90_clean') , 'obs90_data_clean2')
>> >
>> >
>> >
>> > %% Rereferencing data
>> >
>> > cfg = [];
>> >
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.reref = 'yes';
>> >
>> > cfg.preproc.refchannel = 'all';
>> >
>> > obs90_data_ref= ft_preprocessing(cfg,obs90_data_clean2)
>> >
>> >
>> > save (strcat(subject,'obs90_ref') , 'obs90_data_ref')
>> >
>> > %% Snap out smaller pieces (the third second)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.toilim = [0 1]
>> >
>> >
>> > obs90_data_small = ft_redefinetrial(cfg,obs90_data_ref)
>> >
>> >
>> > %% TIMELOCK ERP
>> >
>> > obs90_data_ERP = ft_timelockanalysis(cfg, obs90_data_small);
>> >
>> >
>> > save (strcat(subject,'obs90_ERP') , 'obs90_data_ERP')
>> >
>> >
>> > %% plot it
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, obs90_data_ERP)
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > %% PIPELINE FOR obs180
>> >
>> >
>> >
>> > %% Decompose original data according to the components found before
>> >
>> > load (strcat(sb,'comp_all'))
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.numcomponent = 128
>> >
>> > cfg.unmixing = comp.unmixing;
>> >
>> > cfg.topolabel = comp.topolabel;
>> >
>> > comp_2 = ft_componentanalysis(cfg, obs180_data);
>> >
>> >
>> >
>> > %% Reject component
>> >
>> > cfg = [];
>> >
>> > cfg.component = [];
>> >
>> > obs180_data_ica_cleaned = ft_rejectcomponent(cfg, comp_2,
>> > obs180_data);
>> >
>> >
>> >
>> >
>> > save (strcat(sb,'obs180_ica_cleaned') , 'obs180_data_ica_cleaned')
>> >
>> >
>> >
>> >
>> > %% Artifacts final 180
>> >
>> >
>> >
>> > cfg.method = 'summary';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >
>> > obs180_data_clean3 = ft_rejectvisual(cfg, obs180_data_ica_cleaned);
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.viewmode = 'vertical';
>> >
>> > cfg.latency=[0 1];
>> >
>> > cfg.continuous = 'no';
>> >
>> > cfg.plotlabels='yes'
>> >
>> > cfg = ft_databrowser(cfg,obs180_data_clean3);
>> >
>> > %cfg.artfctdef.reject = 'complete';
>> >
>> > obs180_data_clean2 = ft_rejectartifact (cfg,obs180_data_clean3);
>> >
>> >
>> >
>> > % Save clean data
>> >
>> >
>> > save (strcat(subject,'obs180_clean') , 'obs180_data_clean2')
>> >
>> >
>> >
>> > %% Rereferencing data
>> >
>> > cfg = [];
>> >
>> > cfg.channel = 'all';
>> >
>> > cfg.preproc.reref = 'yes';
>> >
>> > cfg.preproc.refchannel = 'all';
>> >
>> > obs180_data_ref= ft_preprocessing(cfg,obs180_data_clean2)
>> >
>> >
>> > save (strcat(subject,'obs180_ref') , 'obs180_data_ref')
>> >
>> > %% Snap out smaller pieces (the third second)
>> >
>> >
>> > cfg = []
>> >
>> > cfg.toilim = [0 1]
>> >
>> >
>> > obs180_data_small = ft_redefinetrial(cfg,obs180_data_ref)
>> >
>> >
>> > %% TIMELOCK ERP
>> >
>> > obs180_data_ERP = ft_timelockanalysis(cfg, obs180_data_small);
>> >
>> >
>> > save (strcat(subject,'obs180_ERP') , 'obs180_data_ERP')
>> >
>> >
>> > %% plot 180 ERP
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, obs180_data_ERP)
>> >
>> >
>> > %% plot both ERPs
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay ;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > cfg.showlabels = 'yes';
>> >
>> > ft_multiplotER(cfg, obs90_data_ERP, obs180_data_ERP)
>> >
>> >
>> > %% plot difference wave
>> >
>> >
>> > difference = obs180_data_ERP; % copy one of the
>> > structures
>> >
>> > difference.avg = obs180_data_ERP.avg - obs90_data_ERP.avg; % compute
>> > the
>> > difference ERP
>> >
>> >
>> >
>> > cfg = [];
>> >
>> > cfg.layout = lay;
>> >
>> > cfg.interactive = 'yes';
>> >
>> > ft_multiplotER(cfg, difference)
>> >
>> > 2014-09-07 7:53 GMT+02:00 Joseph Dien <jdien07 at mac.com>:
>> >
>> >> Hi,
>> >> I?m one of the main Fieldtrip contributors working on supporting
>> >> EGI
>> >> data. A couple things. First of all, exactly which net do you use?
>> >> If
>> >> they are 129-channel, there is still the question of whether they
>> >> are the
>> >> Hydrocel or the older geodesic nets. Regardless, that shouldn't
>> >> cause an
>> >> error like this. Could you send me the file you are trying to
>> >> process and
>> >> the script you are using and I?ll try troubleshooting it.
>> >>
>> >> Joe
>> >>
>> >>
>> >> On Aug 26, 2014, at 12:53 AM, Ana Laura Diez Martini <
>> >> diezmartini at gmail.com> wrote:
>> >>
>> >> Which nets do you use? I use EGI. I tried both the ones you mention
>> >> and
>> >> they didn't work. It was hard to find that exact one online but it
>> >> was the
>> >> only file that actually worked.
>> >>
>> >>
>> >> On Mon, Aug 25, 2014 at 7:52 AM, KatrinH Heimann
>> >> <katrinheimann at gmail.com>
>> >> wrote:
>> >>
>> >>> Dear Ana, dear all,
>> >>> the layout you used does not correspond to our nets. I tried the
>> >>> GSN-HydroCel-128.sfp and the GSN-HydroCel-129.sfp. Both don't
>> >>> work.
>> >>> Please can anybody help?
>> >>> Cheers
>> >>> Katrin
>> >>>
>> >>>
>> >>> 2014-08-24 5:40 GMT+02:00 Ana Laura Diez Martini
>> >>> <diezmartini at gmail.com>:
>> >>>
>> >>> Try this one
>> >>> <https://www.dropbox.com/s/58zv658rtdant2c/GSN129.sfp?dl=0>
>> >>>> I use
>> >>>>
>> >>>>
>> >>>> On Sun, Aug 24, 2014 at 4:08 AM, KatrinH Heimann <
>> >>>> katrinheimann at gmail.com> wrote:
>> >>>>
>> >>>>> Dear all, my problems seem neverending. This time however i
>> >>>>> really need
>> >>>>> help.
>> >>>>> I implemented a pipeline for ERPs. All works now,
>> >>>>> trialdefinition,
>> >>>>> preprocessing, channelreplacement, ica, componentrejection,
>> >>>>> final
>> >>>>> artifactdetection, timelock of the single subject data. However,
>> >>>>> when I
>> >>>>> wanna compute the grandaverage of the single subjects I get the
>> >>>>> following
>> >>>>> error message:
>> >>>>>
>> >>>>> computing average of avg over 19 subjects
>> >>>>> Warning: discarding electrode information because it cannot be
>> >>>>> averaged
>> >>>>> > In ft_timelockgrandaverage at 249
>> >>>>> the call to "ft_timelockgrandaverage" took 0 seconds and
>> >>>>> required the
>> >>>>> additional allocation of an estimated 3 MB
>> >>>>> computing average of avg over 2 subjects
>> >>>>> Warning: discarding electrode information because it cannot be
>> >>>>> averaged
>> >>>>> > In ft_timelockgrandaverage at 249
>> >>>>> the call to "ft_timelockgrandaverage" took 0 seconds and
>> >>>>> required the
>> >>>>> additional allocation of an estimated 0 MB
>> >>>>> the call to "ft_prepare_layout" took 0 seconds and required the
>> >>>>> additional allocation of an estimated 0 MB
>> >>>>> the call to "ft_topoplotER" took 0 seconds and required the
>> >>>>> additional
>> >>>>> allocation of an estimated 0 MB
>> >>>>>
>> >>>>> Furthermore in the plot of the significant clusters, the
>> >>>>> channelnames
>> >>>>> are mixed up (do not correspond to my net)
>> >>>>>
>> >>>>>
>> >>>>> I guess that there is a problem with the layout I use.
>> >>>>> Here the code that I use
>> >>>>>
>> >>>>> %For generating the layout (also in the single subjects):
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.channel = obs_data.label;
>> >>>>>
>> >>>>> cfg.layout = 'GSN-HydroCel-129.sfp';
>> >>>>>
>> >>>>> cfg.feedback = 'yes';
>> >>>>>
>> >>>>> lay = ft_prepare_layout(cfg);
>> >>>>>
>> >>>>>
>> >>>>> cfg_neighb = [];
>> >>>>>
>> >>>>> cfg_neighb.feedback = 'yes';
>> >>>>>
>> >>>>> cfg_neighb.method = 'triangulation';
>> >>>>>
>> >>>>> cfg_neighb.layout = lay;
>> >>>>>
>> >>>>> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>>>>
>> >>>>>
>> >>>>> obs_data.elec = ft_read_sens('GSN-HydroCel-129.sfp');
>> >>>>>
>> >>>>>
>> >>>>> %For computing the grand average
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.channel = 'all';
>> >>>>>
>> >>>>> cfg.latency = 'all';
>> >>>>>
>> >>>>> cfg.parameter = 'avg';
>> >>>>>
>> >>>>> cfg.keepindividual = 'no'
>> >>>>>
>> >>>>> GA_90 = ft_timelockgrandaverage(cfg,all90{:});
>> >>>>>
>> >>>>> GA_180 = ft_timelockgrandaverage(cfg,all180{:});
>> >>>>>
>> >>>>> % "{:}" means to use data from all elements of the variable
>> >>>>>
>> >>>>>
>> >>>>> For plotting the significant clusters
>> >>>>>
>> >>>>> cfg = [];
>> >>>>>
>> >>>>> cfg.style = 'blank';
>> >>>>>
>> >>>>> cfg.layout = lay;
>> >>>>>
>> >>>>> cfg.channellabels = 'yes';
>> >>>>>
>> >>>>> cfg.highlight = 'labels';
>> >>>>>
>> >>>>> cfg.highlightchannel = find(stat.mask);
>> >>>>>
>> >>>>> cfg.comment = 'no';
>> >>>>>
>> >>>>> figure; ft_topoplotER(cfg, GA_90)
>> >>>>>
>> >>>>> title('Nonparametric: significant with cluster multiple
>> >>>>> comparison
>> >>>>> correction')
>> >>>>>
>> >>>>>
>> >>>>> Do you have ANY idea to this? I am really completely
>> >>>>> helpless....
>> >>>>>
>> >>>>> thanks and best
>> >>>>>
>> >>>>> Katrin
>> >>>>>
>> >>>>> _______________________________________________
>> >>>>> fieldtrip mailing list
>> >>>>> fieldtrip at donders.ru.nl
>> >>>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>>>
>> >>>>
>> >>>>
>> >>>> _______________________________________________
>> >>>> fieldtrip mailing list
>> >>>> fieldtrip at donders.ru.nl
>> >>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>>
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
>> >>
>> >>
> ----------------------------------------------------------------------------
> ----
>> >>
>> >> Joseph Dien, PhD
>> >> Research Associate
>> >> Cognitive Neurology
>> >> The Johns Hopkins University School of Medicine
>> >>
>> >> Lab E-mail: jdien1 at jhmi.edu
>> >> Private E-mail: jdien07 at mac.com
>> >> Office Phone: 410-614-3115
>> >> Cell Phone: 202-297-8117
>> >> Fax: 410-955-0188
>> >> http://joedien.com
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
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> b61/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 3
>> > Date: Mon, 08 Sep 2014 21:55:44 -0400
>> > From: Joseph Dien <jdien07 at mac.com>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] Problem with Geodesic 129 layout!!
>> > Message-ID: <148A801A-8CEA-4F88-A7D0-130637BDAC03 at mac.com>
>> > Content-Type: text/plain; charset="windows-1252"
>> >
>> > yes, please do send me the data.
>> >
>> > Thanks!
>> >
>> > Joe
>> >
>> > On Sep 8, 2014, at 3:38 PM, KatrinH Heimann
>> > <katrinheimann at gmail.com> wrote:
>> >
>> >> Dear Joseph, thanks so much, I really appreciate your help. I was
>> >> also wandering, if maybe there is another bug in my code.
>> >> Our nets are 128 channels, Hydrocel, but as a colleague of me
>> >> adviced me to do so, I also tried the 129 layout.
>> >> My code you find below (without selection of bad channels etc.)
>> >> If you need also the data of two subjects to have a look, let me
>> >> know!!!
>> >> Best and thanks really!
>> >> Katrin
>> >>
>> >>
>> >>
>> >> subject=input('Which subject do you want to analyse? ','s');
>> >>
>> >> name = strcat(subject,'.raw');
>> >>
>> >> subjectnumber=input('Which is the subjectnumber?', 's');
>> >>
>> >> sb=subjectnumber
>> >>
>> >> %subjectnumber = strcat(subjectnumber, '.txt')
>> >>
>> >> %%
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.dataset = name;
>> >>
>> >> cfg.trialfun = 'ft_trialfun_general'; % this is the default
>> >>
>> >> cfg.trialdef.eventtype = 'trigger';
>> >>
>> >> cfg.trialdef.eventvalue = 'stim'; % the value of the stimulus
>> >> trigger
>> >>
>> >> cfg.trialdef.prestim = 0.234; % in seconds
>> >>
>> >> cfg.trialdef.poststim = 7.266; % in seconds (whole time of video
>> >> observation, but maybe we need less)
>> >>
>> >> cfg = ft_definetrial(cfg);
>> >>
>> >>
>> >>
>> >> cfg.trl([1:7],:) = [];
>> >>
>> >>
>> >>
>> >> %change timeline according to constant offset of 16 ms = 8 samples
>> >> (because recorded with 500 hz)in
>> >>
>> >> %structure trial
>> >>
>> >> cfg.trl(:,3)=cfg.trl(:,3)-8
>> >>
>> >>
>> >>
>> >> %change timeline to make the cut the zeropoint
>> >>
>> >>
>> >>
>> >> cfg.trl(:,3)=cfg.trl(:,3)- 1000;
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% preprocess data
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.detrend = 'yes';
>> >>
>> >> cfg.preproc.demean = 'yes';
>> >>
>> >> cfg.preproc.baselinewindow = [-2.1 -2.0]
>> >>
>> >> %cfg.preproc.bpfilter = 'yes'; - the data here has to be already
>> >> filtered
>> >>
>> >> %(0.1-45) and bad channels have to be replaced!!!!
>> >>
>> >> %cfg.preproc.bpfreq = [6 32];
>> >>
>> >> %
>> >>
>> >> %
>> >>
>> >> obs_data = ft_preprocessing(cfg);
>> >>
>> >> %
>> >>
>> >> save (strcat(sb,'obs_data') , 'obs_data')
>> >>
>> >>
>> >>
>> >> %% determining channel neighbours (necessary for Artifact
>> >> detection)
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = obs_data.label;
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.feedback = 'yes';
>> >>
>> >> lay = ft_prepare_layout(cfg);
>> >>
>> >>
>> >> cfg_neighb = [];
>> >>
>> >> cfg_neighb.feedback = 'yes';
>> >>
>> >>
>> >>
>> >> cfg_neighb.method = 'triangulation';
>> >>
>> >> cfg_neighb.layout = 'GSN128.sfp';
>> >>
>> >> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>
>> >>
>> >>
>> >> obs_data.elec = ft_read_sens('GSN128.sfp');
>> >>
>> >>
>> >>
>> >> %% Artifacts - to detect bad channels - is not saved!!!
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs_data_try = ft_rejectvisual(cfg, obs_data);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_try = ft_rejectartifact (cfg,obs_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Preparing neighbours for channel repair - but bad channel info
>> >> in!!!
>> >>
>> >>
>> >>
>> >> % cfg_neighb = [];
>> >>
>> >> % cfg_neighb.feedback = 'yes';
>> >>
>> >> % cfg_neighb.method = 'triangulation';
>> >>
>> >> % cfg_neighb.layout = 'GSN-HydroCel-128.sfp';
>> >>
>> >> % neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>
>> >>
>> >>
>> >> % Interpolate and put into new data structure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.badchannel = {};
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.method = 'nearest';
>> >>
>> >> cfg.neighbours = neighbours;
>> >>
>> >> obs_data_channelrepaired = ft_channelrepair(cfg,obs_data)
>> >>
>> >>
>> >>
>> >> % Check reconstruction
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >>
>> >>
>> >>
>> >> % dat1=obs_data;
>> >>
>> >> % dat2=obs_data_channelrepaired;
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(62,:); % 68 is channel index of E68
>> >>
>> >> % y=dat2.trial{1}(62,:);
>> >>
>> >> % plot(x);hold on;plot(y,'r');
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(72,:);
>> >>
>> >> % y=dat2.trial{1}(75,:);
>> >>
>> >> % figure;
>> >>
>> >> % plot(x);hold on;plot(y,'r')
>> >>
>> >> %
>> >>
>> >> % x=dat1.trial{1}(75,:);
>> >>
>> >> % y=dat2.trial{1}(75,:);
>> >>
>> >> % figure;
>> >>
>> >> % plot(x);hold on;plot(y,'r')
>> >>
>> >> %% artifact rejection/trial inspection - throw out electrode jumps
>> >> etc.
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs_data_channelrepaired);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs_data_clean1 = ft_rejectartifact (cfg,obs_data_channelrepaired);
>> >>
>> >>
>> >>
>> >> %% ICA - Anzahl der Komponenten anpassen!- adapt numcomponent
>> >> according to channels interpolated %% 128-number of interp.
>> >> channels)
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = {'all'};
>> >>
>> >> cfg.numcomponent = 128
>> >>
>> >> comp = ft_componentanalysis(cfg,obs_data_clean1);
>> >>
>> >> save (strcat(sb,'comp_all') , 'comp')
>> >>
>> >>
>> >>
>> >> %%
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'component';
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='some';
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> ft_databrowser(cfg,comp);
>> >>
>> >>
>> >>
>> >> %% poweranalysis components
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.output = 'pow';
>> >>
>> >> cfg.channel = 'all';%compute the power spectrum in all ICs
>> >>
>> >> cfg.method = 'mtmfft';
>> >>
>> >> cfg.taper = 'hanning';
>> >>
>> >> cfg.foi = 0:0.2:50;
>> >>
>> >> obs_freq = ft_freqanalysis(cfg, comp);
>> >>
>> >>
>> >>
>> >> %And you can plot the spectra:
>> >>
>> >>
>> >>
>> >> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>> >>
>> >> nsubplots = 16;
>> >>
>> >> nbyn = sqrt(nsubplots);% sqrt(nsubplots) should not contain
>> >> decimals, type doc subplot
>> >>
>> >>
>> >>
>> >> Nfigs = ceil(size(comp.topo,1)/nsubplots);
>> >>
>> >> tot = Nfigs*nsubplots;
>> >>
>> >>
>> >>
>> >> rptvect = 1:size(comp.topo,1);
>> >>
>> >> rptvect = padarray(rptvect, [0 tot-size(comp.topo,1)], 0,'post');
>> >>
>> >> rptvect = reshape(rptvect,nsubplots,Nfigs)';
>> >>
>> >>
>> >>
>> >> for r=1:size(rptvect,1);
>> >>
>> >> figure;set(gcf,'units','normalized','outerposition',[0 0 1
>> >> 1]);%full screen
>> >>
>> >> k=0;
>> >>
>> >> for j=1:size(rptvect,2);
>> >>
>> >> if~(rptvect(r,j)==0);
>> >>
>> >> k=k+1;
>> >>
>> >> cfg=[];
>> >>
>> >> cfg.channel = rptvect(r,j);
>> >>
>> >> cfg.ylim =[0 500]
>> >>
>> >> cfg.xlim =[0 50]
>> >>
>> >> subplot(nbyn,nbyn,k);ft_singleplotER(cfg,obs_freq);
>> >>
>> >> end
>> >>
>> >> end
>> >>
>> >> end
>> >>
>> >>
>> >>
>> >> %For the IC topos you'll follow the same logic as above but with:
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [1:20]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [21:40]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [41:60]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >> figure
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [61:80]; % specify the component(s) that
>> >> should be plotted
>> >>
>> >> cfg.layout = 'GSN128.sfp';
>> >>
>> >> cfg.comment = 'no';
>> >>
>> >> ft_topoplotIC(cfg, comp)
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Seperate observation conditions
>> >>
>> >>
>> >>
>> >> name1= strcat(num2str(sb),'_90.xls');
>> >>
>> >> list1=xlsread (name1)
>> >>
>> >>
>> >>
>> >> name2= strcat(num2str(sb),'_180.xls');
>> >>
>> >> list2=xlsread (name2)
>> >>
>> >>
>> >> %
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.trials = [list1]
>> >>
>> >> obs90_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.trials = [list2]
>> >>
>> >> obs180_data = ft_redefinetrial(cfg,obs_data_channelrepaired)
>> >>
>> >>
>> >>
>> >> %%PIPELINE FOR obs90
>> >>
>> >>
>> >>
>> >> %% Decompose original data according to the components found before
>> >>
>> >> load (strcat(sb,'comp_all'))
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.numcomponent = 128;
>> >>
>> >> cfg.unmixing = comp.unmixing;
>> >>
>> >> cfg.topolabel = comp.topolabel;
>> >>
>> >>
>> >>
>> >> comp_1 = ft_componentanalysis(cfg, obs90_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Reject component
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [];
>> >>
>> >> obs90_data_ica_cleaned = ft_rejectcomponent(cfg, comp_1,
>> >> obs90_data);
>> >>
>> >>
>> >>
>> >>
>> >> save (strcat(sb,'obs90_ica_cleaned') , 'obs90_data_ica_cleaned')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Artifacts - final detection
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs90_data_clean3 = ft_rejectvisual(cfg, obs90_data_ica_cleaned);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs90_data_clean3);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs90_data_clean2 = ft_rejectartifact (cfg,obs90_data_clean3);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> % Save clean data
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_clean') , 'obs90_data_clean2')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Rereferencing data
>> >>
>> >> cfg = [];
>> >>
>> >>
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.reref = 'yes';
>> >>
>> >> cfg.preproc.refchannel = 'all';
>> >>
>> >> obs90_data_ref= ft_preprocessing(cfg,obs90_data_clean2)
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_ref') , 'obs90_data_ref')
>> >>
>> >> %% Snap out smaller pieces (the third second)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.toilim = [0 1]
>> >>
>> >>
>> >>
>> >> obs90_data_small = ft_redefinetrial(cfg,obs90_data_ref)
>> >>
>> >>
>> >>
>> >> %% TIMELOCK ERP
>> >>
>> >> obs90_data_ERP = ft_timelockanalysis(cfg, obs90_data_small);
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs90_ERP') , 'obs90_data_ERP')
>> >>
>> >>
>> >>
>> >> %% plot it
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs90_data_ERP)
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% PIPELINE FOR obs180
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Decompose original data according to the components found before
>> >>
>> >> load (strcat(sb,'comp_all'))
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.numcomponent = 128
>> >>
>> >> cfg.unmixing = comp.unmixing;
>> >>
>> >> cfg.topolabel = comp.topolabel;
>> >>
>> >> comp_2 = ft_componentanalysis(cfg, obs180_data);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Reject component
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.component = [];
>> >>
>> >> obs180_data_ica_cleaned = ft_rejectcomponent(cfg, comp_2,
>> >> obs180_data);
>> >>
>> >>
>> >>
>> >>
>> >> save (strcat(sb,'obs180_ica_cleaned') , 'obs180_data_ica_cleaned')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Artifacts final 180
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> cfg.method = 'summary';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.layout = 'GSN128.sfp'; % this allows for plotting
>> >>
>> >> obs180_data_clean3 = ft_rejectvisual(cfg, obs180_data_ica_cleaned);
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.viewmode = 'vertical';
>> >>
>> >> cfg.latency=[0 1];
>> >>
>> >> cfg.continuous = 'no';
>> >>
>> >> cfg.plotlabels='yes'
>> >>
>> >> cfg = ft_databrowser(cfg,obs180_data_clean3);
>> >>
>> >> %cfg.artfctdef.reject = 'complete';
>> >>
>> >> obs180_data_clean2 = ft_rejectartifact (cfg,obs180_data_clean3);
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> % Save clean data
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_clean') , 'obs180_data_clean2')
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> %% Rereferencing data
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.channel = 'all';
>> >>
>> >> cfg.preproc.reref = 'yes';
>> >>
>> >> cfg.preproc.refchannel = 'all';
>> >>
>> >> obs180_data_ref= ft_preprocessing(cfg,obs180_data_clean2)
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_ref') , 'obs180_data_ref')
>> >>
>> >> %% Snap out smaller pieces (the third second)
>> >>
>> >>
>> >>
>> >> cfg = []
>> >>
>> >> cfg.toilim = [0 1]
>> >>
>> >>
>> >>
>> >> obs180_data_small = ft_redefinetrial(cfg,obs180_data_ref)
>> >>
>> >>
>> >>
>> >> %% TIMELOCK ERP
>> >>
>> >> obs180_data_ERP = ft_timelockanalysis(cfg, obs180_data_small);
>> >>
>> >>
>> >>
>> >> save (strcat(subject,'obs180_ERP') , 'obs180_data_ERP')
>> >>
>> >>
>> >>
>> >> %% plot 180 ERP
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs180_data_ERP)
>> >>
>> >>
>> >>
>> >> %% plot both ERPs
>> >>
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay ;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> cfg.showlabels = 'yes';
>> >>
>> >> ft_multiplotER(cfg, obs90_data_ERP, obs180_data_ERP)
>> >>
>> >>
>> >>
>> >> %% plot difference wave
>> >>
>> >>
>> >>
>> >> difference = obs180_data_ERP; % copy one of
>> >> the structures
>> >>
>> >> difference.avg = obs180_data_ERP.avg - obs90_data_ERP.avg; %
>> >> compute the difference ERP
>> >>
>> >>
>> >> cfg = [];
>> >>
>> >> cfg.layout = lay;
>> >>
>> >> cfg.interactive = 'yes';
>> >>
>> >> ft_multiplotER(cfg, difference)
>> >>
>> >>
>> >> 2014-09-07 7:53 GMT+02:00 Joseph Dien <jdien07 at mac.com>:
>> >> Hi,
>> >> I?m one of the main Fieldtrip contributors working on supporting
>> >> EGI data. A couple things. First of all, exactly which net do
>> >> you use? If they are 129-channel, there is still the question of
>> >> whether they are the Hydrocel or the older geodesic nets.
>> >> Regardless, that shouldn't cause an error like this. Could you
>> >> send me the file you are trying to process and the script you are
>> >> using and I?ll try troubleshooting it.
>> >>
>> >> Joe
>> >>
>> >>
>> >> On Aug 26, 2014, at 12:53 AM, Ana Laura Diez Martini
>> >> <diezmartini at gmail.com> wrote:
>> >>
>> >>> Which nets do you use? I use EGI. I tried both the ones you
>> >>> mention and they didn't work. It was hard to find that exact one
>> >>> online but it was the only file that actually worked.
>> >>>
>> >>>
>> >>> On Mon, Aug 25, 2014 at 7:52 AM, KatrinH Heimann
>> >>> <katrinheimann at gmail.com> wrote:
>> >>> Dear Ana, dear all,
>> >>> the layout you used does not correspond to our nets. I tried the
>> >>> GSN-HydroCel-128.sfp and the GSN-HydroCel-129.sfp. Both don't
>> >>> work. Please can anybody help?
>> >>> Cheers
>> >>> Katrin
>> >>>
>> >>>
>> >>> 2014-08-24 5:40 GMT+02:00 Ana Laura Diez Martini
>> >>> <diezmartini at gmail.com>:
>> >>>
>> >>> Try this one I use
>> >>>
>> >>>
>> >>> On Sun, Aug 24, 2014 at 4:08 AM, KatrinH Heimann
>> >>> <katrinheimann at gmail.com> wrote:
>> >>> Dear all, my problems seem neverending. This time however i really
>> >>> need help.
>> >>> I implemented a pipeline for ERPs. All works now, trialdefinition,
>> >>> preprocessing, channelreplacement, ica, componentrejection, final
>> >>> artifactdetection, timelock of the single subject data. However,
>> >>> when I wanna compute the grandaverage of the single subjects I get
>> >>> the following error message:
>> >>>
>> >>> computing average of avg over 19 subjects
>> >>> Warning: discarding electrode information because it cannot be
>> >>> averaged
>> >>> > In ft_timelockgrandaverage at 249
>> >>> the call to "ft_timelockgrandaverage" took 0 seconds and required
>> >>> the additional allocation of an estimated 3 MB
>> >>> computing average of avg over 2 subjects
>> >>> Warning: discarding electrode information because it cannot be
>> >>> averaged
>> >>> > In ft_timelockgrandaverage at 249
>> >>> the call to "ft_timelockgrandaverage" took 0 seconds and required
>> >>> the additional allocation of an estimated 0 MB
>> >>> the call to "ft_prepare_layout" took 0 seconds and required the
>> >>> additional allocation of an estimated 0 MB
>> >>> the call to "ft_topoplotER" took 0 seconds and required the
>> >>> additional allocation of an estimated 0 MB
>> >>>
>> >>> Furthermore in the plot of the significant clusters, the
>> >>> channelnames are mixed up (do not correspond to my net)
>> >>>
>> >>>
>> >>> I guess that there is a problem with the layout I use.
>> >>> Here the code that I use
>> >>>
>> >>> %For generating the layout (also in the single subjects):
>> >>> cfg = [];
>> >>>
>> >>> cfg.channel = obs_data.label;
>> >>>
>> >>> cfg.layout = 'GSN-HydroCel-129.sfp';
>> >>>
>> >>> cfg.feedback = 'yes';
>> >>>
>> >>> lay = ft_prepare_layout(cfg);
>> >>>
>> >>>
>> >>> cfg_neighb = [];
>> >>>
>> >>> cfg_neighb.feedback = 'yes';
>> >>>
>> >>> cfg_neighb.method = 'triangulation';
>> >>>
>> >>> cfg_neighb.layout = lay;
>> >>>
>> >>> neighbours = ft_prepare_neighbours(cfg_neighb, obs_data);
>> >>>
>> >>>
>> >>> obs_data.elec = ft_read_sens('GSN-HydroCel-129.sfp');
>> >>>
>> >>>
>> >>>
>> >>> %For computing the grand average
>> >>>
>> >>> cfg = [];
>> >>>
>> >>> cfg.channel = 'all';
>> >>>
>> >>> cfg.latency = 'all';
>> >>>
>> >>> cfg.parameter = 'avg';
>> >>>
>> >>> cfg.keepindividual = 'no'
>> >>>
>> >>> GA_90 = ft_timelockgrandaverage(cfg,all90{:});
>> >>>
>> >>> GA_180 = ft_timelockgrandaverage(cfg,all180{:});
>> >>>
>> >>> % "{:}" means to use data from all elements of the variable
>> >>>
>> >>>
>> >>> For plotting the significant clusters
>> >>>
>> >>> cfg = [];
>> >>>
>> >>> cfg.style = 'blank';
>> >>>
>> >>> cfg.layout = lay;
>> >>>
>> >>> cfg.channellabels = 'yes';
>> >>>
>> >>> cfg.highlight = 'labels';
>> >>>
>> >>> cfg.highlightchannel = find(stat.mask);
>> >>>
>> >>> cfg.comment = 'no';
>> >>>
>> >>> figure; ft_topoplotER(cfg, GA_90)
>> >>>
>> >>> title('Nonparametric: significant with cluster multiple comparison
>> >>> correction')
>> >>>
>> >>>
>> >>>
>> >>> Do you have ANY idea to this? I am really completely helpless....
>> >>>
>> >>> thanks and best
>> >>>
>> >>> Katrin
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>>
>> >>> _______________________________________________
>> >>> fieldtrip mailing list
>> >>> fieldtrip at donders.ru.nl
>> >>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >>
> ----------------------------------------------------------------------------
> ----
>> >>
>> >> Joseph Dien, PhD
>> >> Research Associate
>> >> Cognitive Neurology
>> >> The Johns Hopkins University School of Medicine
>> >>
>> >> Lab E-mail: jdien1 at jhmi.edu
>> >> Private E-mail: jdien07 at mac.com
>> >> Office Phone: 410-614-3115
>> >> Cell Phone: 202-297-8117
>> >> Fax: 410-955-0188
>> >> http://joedien.com
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> >
> ----------------------------------------------------------------------------
> ----
>> >
>> > Joseph Dien, PhD
>> > Research Associate
>> > Cognitive Neurology
>> > The Johns Hopkins University School of Medicine
>> >
>> > Lab E-mail: jdien1 at jhmi.edu
>> > Private E-mail: jdien07 at mac.com
>> > Office Phone: 410-614-3115
>> > Cell Phone: 202-297-8117
>> > Fax: 410-955-0188
>> > http://joedien.com
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140908/81922
> ad2/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 4
>> > Date: Tue, 9 Sep 2014 06:37:13 +0000
>> > From: "Caspar, Emilie" <e.caspar at ucl.ac.uk>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] ft_rejectvisual: removing trials marked as
>> > bad
>> > Message-ID: <3C365782-4B07-4EFE-A921-99489BDD5DA6 at live.ucl.ac.uk>
>> > Content-Type: text/plain; charset="iso-8859-1"
>> >
>> > Hi Ashley,
>> >
>> > Maybe the mistake is you did not integrate in your script the
>> > command to save the rejection. Normally, with the following command,
>> > you will see in the Matlab Window that artifacts are correctly
>> > rejected after the "quit" button.
>> >
>> > clean_data = ft_rejectvisual(cfg, interpoldata);
>> > cfg.artfctdef.reject = 'complete';
>> > cfg.artfctdef.feedback = 'yes';
>> > cleandata = ft_rejectartifact(cfg,clean_data);
>> >
>> > Emilie
>> >
>> >
>> >
>> >
>> >
>> > Le 3 sept. 2014 ? 11:58, Ashley Greene
>> > <agreene24 at gmail.com<mailto:agreene24 at gmail.com>> a ?crit :
>> >
>> > Hello,
>> >
>> > I have used the rejectvisual function in attempt to remove bad
>> > trials, but after marking them and pressing the quit button,
>> > although the command window states that the selected trials have
>> > been removed, there is no obvious change in the data; all of the
>> > trials are still intact. Have I overlooked something?
>> >
>> > Thanks,
>> >
>> > Ashley
>> > _______________________________________________
>> > fieldtrip mailing list
>> > fieldtrip at donders.ru.nl<mailto:fieldtrip at donders.ru.nl>
>> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/4c2d0
> 985/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 5
>> > Date: Tue, 09 Sep 2014 14:16:20 +0200
>> > From: Patricia Wollstadt <Patricia.Wollstadt at gmx.de>
>> > To: fieldtrip at science.ru.nl
>> > Subject: Re: [FieldTrip] TRENTOOL pipeline help
>> > Message-ID: <540EEF94.9080601 at gmx.de>
>> > Content-Type: text/plain; charset="windows-1252"
>> >
>> > Hello Max,
>> >
>> > I added a few comments to the questions regarding individual
>> > parameters
>> > below. To address the general problem of TRENTOOL telling you, that
>> > there are not enough sample points in your data: From what I can see
>> > in
>> > your script, you probably don't have enough data points in each time
>> > series to robustly estimate TE. You analyze 800 ms of data sampled
>> > at
>> > 300 Hz, which gives you 240 samples per time series. Can you maybe
>> > avoid
>> > downsampling to 300 Hz and downsample to 600 Hz instead? Or could
>> > you
>> > analyze a longer time window of interest?
>> > Note that you also 'lose' data to embedding and the interaction
>> > delay:
>> > The first point that can be used for TE estimation is at max.
>> > embedding
>> > length + max. interaction delay in samples. For example: max.
>> > embedding
>> > length = dim * tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the
>> > max
>> > interaction delay of 30 ms = 9 samples. In this example, you would
>> > be
>> > left with 240 - 29 samples for TE estimation per trial. There is
>> > also
>> > the possibility to estimate time resolved TE/TE for shorter time
>> > windows
>> > of interest (see section 4.4 in the manual); however, this method
>> > requires the use of a GPU for TE estimation.
>> >
>> > I would further recommend to use the new pipeline for group
>> > statistics
>> > described in the manual in section 4.5 (the function
>> > 'TEgroup_calculate'
>> > is deprecated). The new pipeline allows you to reconstruct the
>> > interaction delay and uses the following functions (see also
>> > comments in
>> > the script):
>> >
>> > TEgroup_prepare -> prepares all data sets (all subjects/all
>> > conditions) for group analysis (this means finding common embedding
>> > parameters such that estimates are not biased between groups)
>> > InteractionDelayReconstruction_calculate -> estimates TE for
>> > individual data sets and all assumed interaction delays u
>> > InteractionDelayReconstruction_analyze -> reconstructs the
>> > interaction delay by selecting the u that maximizes TE for each
>> > channel
>> > TEgroup_stats -> calculate group statistics using a permutation test
>> >
>> > I can send you an example script for group TE analysis using this
>> > pipeline to get you started. I hope this helps you to get the group
>> > analysis running. Just write again if you're having trouble setting
>> > up
>> > the pipeline or something is not clear about the parameters/my
>> > comments.
>> >
>> > Best,
>> > Patricia
>> >
>> >
>> >
>> >
>> > On 09/04/2014 08:30 PM, Max Cantor wrote:
>> >> Hi fieldtrippers,
>> >>
>> >> I know trentool is not produced by the Donders Institute, so I'm
>> >> not
>> >> 100% sure if it is appropriate to ask questions about it here, but
>> >> to
>> >> the best of my knowledge they do not have a mailing list and I saw
>> >> a
>> >> few trentool questions in the archives, so I'm going to assume it's
>> >> ok...
>> >>
>> >> In any case, below is my current pipeline (slightly modified for
>> >> comprehensibility):
>> >>
>> >> (notes in bold are comments/questions made in this email, not
>> >> present
>> >> in the pipeline. Sorry in advance for the long post! Any help would
>> >> be
>> >> greatly appreciated as I'm a bit over my head on this but I think
>> >> I'm
>> >> close!)
>> >>
>> >> *****
>> >>
>> >> % Prepare group TE data
>> >>
>> >> cfgP = [];
>> >> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> >> cfgP.TEcalctype = 'VW_ds';
>> >> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>> >>
>> >> *I'm trying to find the transfer entropy between the left and right
>> >> auditory cortices in my experiment. The input is virtual sensor
>> >> data
>> >> that was produced using SAM in fieldtrip on real MEG data. *
>> >>
>> >> % specify u to be scanned
>> >>
>> >> cfgP.predicttime_u = 30;
>> >> cfgP.toi = [-0.4 0.4];
>> >>
>> >> *For clarification, the predicttime_u is in seconds but the toi is
>> >> in
>> >> milliseconds. If I understand correctly, the predicttime_u must fit
>> >> within the toi, but beyond that are there any benefits to it being
>> >> earlier or later?* PW: The predictiontime_u is in milliseconds and
>> >> the
>> >> toi is in seconds. The prediction time is the assumed interaction
>> >> delay between your two sources and should fit within your toi. In
>> >> general it is preferable to use the method for interaction delay
>> >> reconstruction for TE estimation, because it allows you to
>> >> reconstruct
>> >> the actual delay between your source and target times series. A
>> >> non-optimal u/interaction delay may cause an underestimation of TE,
>> >> so
>> >> it is recommended to use the pipeline for interaction delay
>> >> reconstruction whenever estimating TE for unknown delays.
>> >> If you use the methods for interaction delay reconstruction
>> >> 'predicttime_u' is replaced by
>> >> cfgTEP.predicttimemin_u % minimum u to be scanned
>> >> cfgTEP.predicttimemax_u % maximum u to be scanned
>> >> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> >> A large range for u values to be scanned increases computing time a
>> >> lot, so it is best to limit the u range to values that are
>> >> physiologically plausible.
>> >>
>> >> % ACT (Autocorrelation Time) estimation and constraints
>> >>
>> >> cfgP.maxlag = 150;
>> >> cfgP.actthrvalue = 7.5;
>> >> cfgP.minnrtrials = 5;
>> >>
>> >> *My understanding is maxlag should be 1/2 the sampling rate, so
>> >> since
>> >> the data are downsampled to 300hz, it should be 150. I know that
>> >> the
>> >> sample rate and filters are used to determine the actthrvalue, but
>> >> I
>> >> don't actually know the calculation. 7.5 was a rough guess just to
>> >> test the pipeline. I'm also uncertain of what minnrtrials should
>> >> be.*
>> >> PW: You can set the actthrvalue based on the filtering you did
>> >> prior
>> >> to TE analysis. If you for example highpass filtered at 10 Hz, you
>> >> shouldn't find an ACT higher than 30 samples, because you filtered
>> >> out
>> >> any components of the signal slower than 10 Hz/30 samples (given
>> >> your
>> >> sampling frequency of 300 Hz). So in this scenario the actthrvalue
>> >> would be 30.
>> >> A good value for cfgP.minnrtrials is 12 (a minimum number of trials
>> >> is
>> >> needed to realize the permutation test for estimated TE values).
>> >>
>> >> % Optimization
>> >>
>> >> cfgP.optimizemethod = 'ragwitz';
>> >> cfgP.ragdim = 4:8;
>> >> cfgP.ragtaurange = [0.2 0.4];
>> >> cfgP.ragtausteps = 15;
>> >> cfgP.repPred = 100;
>> >> *
>> >> *
>> >> *I am completely at a loss for this. I've done some reading into
>> >> transfer entropy, mutual information, etc., cited in trentool, but
>> >> I'm
>> >> yet to understand how exactly this optimization works and what the
>> >> configuration should be, given my data and experimental
>> >> intentions.*
>> >> PW: The Ragwitz criterion tries to find optimal embedding
>> >> parameters
>> >> dim and tau for the data. To do that, the method iteratively takes
>> >> all
>> >> possible combinations of dim and tau values that are provided in
>> >> cfgP.ragdim and cfgP.ragtaurange/.ragtausteps and tests how well
>> >> these
>> >> combinations embed the data. To test an embedding, the method
>> >> builds
>> >> the embedding vectors from the data; it then tests for each point
>> >> how
>> >> well the next point in time can be predicted from the reference
>> >> point's nearest neighbours. So for each embedded point, the method
>> >> searches for the nearest neighbours and calculates the average of
>> >> those nearest neighbours. The difference between the
>> >> averaged/predicted point and the actual next point is the error of
>> >> the
>> >> local predictor. The Ragwitz criterion will then return the
>> >> parameter
>> >> combination for which this error over all points is minimal.
>> >> The parameters set the following: 'ragdim' are dimensions to be
>> >> tested
>> >> by the method (I would reccomend to start with 2:10), 'ragtaurange'
>> >> together with 'ragtausteps' specifies the tau values to be tested
>> >> (TRENTOOL will build a vector from 0.2 to 0.4 in 15 steps). Note,
>> >> that
>> >> the values here are factors that are later multiplied with the ACT
>> >> to
>> >> obtain the actual tau. 'repPred' is the number of points that will
>> >> be
>> >> used for the local prediction, i.e. the Ragwitz criterion will test
>> >> the local prediction and calculate the error for the first 100
>> >> points
>> >> in your time series. The two parameters 'flagNei' ans 'sizeNei'
>> >> below
>> >> specify the type of neighbour search conducted by the Ragwitz
>> >> criterion: 'flagNei' tells the method to either conduct a kNN or
>> >> range
>> >> search; 'sizeNei' specifies the number of neighbours or the radius
>> >> to
>> >> be searched by a range search.
>> >>
>> >>
>> >> % Kernel-based TE estimation
>> >>
>> >> cfgP.flagNei = 'Mass';
>> >> cfgP.sizeNei = 4; % Default
>> >>
>> >> cfgP.ensemblemethod = 'no';
>> >> cfgP.outputpath = *OUTPUT PATH*;
>> >>
>> >> if ~exist(*Path for TEprepare data object*)
>> >> load VSdat;
>> >> TE_Wrd = {};
>> >> for i = 1:nConds
>> >> for j = 1:Nsub
>> >> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> >> end
>> >> end
>> >> clear VSdat;
>> >> save('TE_Wrd', 'TE_Wrd');
>> >> end
>> >> *
>> >> *
>> >> *The configuration and virtual sensor data, organized in a 3 x 15
>> >> cell
>> >> of structures (condition by subject) are the input. The TEprepare
>> >> substructure is added to each individual condition x subject .mat
>> >> files' data structure which are stored on disk independently.*
>> >>
>> >> % Use object_to_mat_conversion.m to replace individual condition x
>> >> subject virtual sensor data
>> >> % .mat files with their TE_Wrd equivalent
>> >> *
>> >> *
>> >> *I'm using a separate script to make some manipulations to the
>> >> objects
>> >> from disk; this will all eventually be integrated into the main
>> >> pipeline*.*TRENTOOL seems to handle data output very differently
>> >> from
>> >> fieldtrip and I've had trouble thinking through the most logical
>> >> way
>> >> to handle the data so it's a bit haphazard right now.*
>> >>
>> >> load cond080sub01.mat
>> >>
>> >> cfgG = [];
>> >> cfgG.dim = cond080sub01.TEprepare.optdim;
>> >> cfgG.tau = cond080sub01.TEprepare.opttau;
>> >>
>> >> if isfield(cond080sub01, 'TEprepare')
>> >> TEgroup_prepare(cfgG, fileCell);
>> >> else
>> >> error('Need to run TEprepare before TEgroup_prepare');
>> >> end
>> >> *
>> >> *
>> >> *For clarification, fileCell is a cell with the name of each
>> >> condition
>> >> x subject .mat file, which as I said before is collectively the
>> >> same
>> >> as the 3 x 15 VSdat structure (condition x subject).*
>> >>
>> >> % Replace .mat files with '_for_TEgroup_calculate' version in
>> >> % object_to_mat_conversion.m
>> >>
>> >> % TE Group Calculate
>> >>
>> >> load cond080sub01.mat
>> >> if isfield(cond080sub01, 'TEgroupprepare')
>> >> for i = 1:length(fileCell)
>> >> TEgroup_calculate(fileCell{i});
>> >> end
>> >> else
>> >> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> >> end
>> >> *
>> >> *
>> >> *At this step I get the following error:
>> >>
>> >> Error using transferentropy (line 337)
>> >> \nTRENTOOL ERROR: not enough data points left after embedding
>> >>
>> >> Error in TEgroup_calculate (line 133)
>> >> [TEresult] = transferentropy(cfg,data);*
>> >>
>> >> % TE Group Stats
>> >>
>> >> cfgGSTAT = [];
>> >> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> >> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>> >>
>> >> cfgGSTAT.uvar = 1;
>> >> cfgGSTAT.ivar = 2;
>> >> cfgGSTAT.fileidout = 'test_groupstats';
>> >>
>> >> TEgroup_stats(cfgGSTAT, fileCell);
>> >>
>> >> *Given the error above, I am yet to get to this step, but it does
>> >> not
>> >> seem fundamentally different from normal fieldtrip stats.*
>> >>
>> >> *****
>> >>
>> >> In case my notes were not clear or you skipped to the bottom, *my
>> >> primary concern is whether the error I'm getting in
>> >> TEgroup_calculate
>> >> is a pipeline issue* (I noticed the example pipeline in trentool,
>> >> the
>> >> manual, and published methods articles all seem to have slightly or
>> >> significantly different pipeline compositions), *or if the error
>> >> is*
>> >> due to ACT, ragwitz optimization, or some other faulty
>> >> parameterization *on my part due to a lack of understanding of how
>> >> transfer entropy works on a more theoretical/mathematical level*.
>> >> If
>> >> the latter is the case, is there any relatively straightforward way
>> >> to
>> >> conceptualize this, or is this something where I'm just going to
>> >> have
>> >> to keep reading and rereading until it eventually makes sense? I've
>> >> already done quite a bit of that and it hasn't pierced my thick
>> >> skull
>> >> yet but I'm sure it will eventually!
>> >>
>> >> Thank you so much,
>> >>
>> >> Max Cantor
>> >>
>> >>
>> >> --
>> >> Max Cantor
>> >> Lab Manager
>> >> Computational Neurolinguistics Lab
>> >> University of Michigan
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >
>> > --
>> > ------------------------------------------------------------
>> >
>> > Patricia Wollstadt, PhD Student
>> >
>> > MEG Unit, Brain Imaging Center
>> >
>> > Goethe University, Frankfurt, Germany
>> >
>> > Heinrich Hoffmann Strasse 10, Haus 93 B
>> >
>> > D - 60528 Frankfurt am Main
>> >
>> > -------------- next part --------------
>> > An HTML attachment was scrubbed...
>> > URL:
>> >
> <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20140909/ce6b2
> 97f/attachment-0001.html>
>> >
>> > ------------------------------
>> >
>> > Message: 6
>> > Date: Tue, 9 Sep 2014 15:21:20 +0200
>> > From: Holger Krause <Holger.Krause at med.uni-duesseldorf.de>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: [FieldTrip] Latest FT version on ftp-server is from three
>> > days ago
>> > Message-ID:
>> > <201409091521.22589.Holger.Krause at med.uni-duesseldorf.de>
>> > Content-Type: text/plain; charset="us-ascii"
>> >
>> > Dear developers,
>> >
>> > Could you please check the process for creation/publication of
>> > zip-files on
>> > the FTP server? The latest file I can find there is
>> > fieldtrip-20140906.zip.
>> >
>> > Thanks in advance,
>> >
>> > Holger
>> >
>> >
>> > ------------------------------
>> >
>> > Message: 7
>> > Date: Tue, 9 Sep 2014 09:19:55 -0400
>> > From: Max Cantor <mcantor at umich.edu>
>> > To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>> > Subject: Re: [FieldTrip] TRENTOOL pipeline help
>> > Message-ID:
>> > <CAFTjRaUXzwca7rR3sm8iPLLyUxqHO9kkm++=zAU4LUW=r7NRBw at mail.gmail.com>
>> > Content-Type: text/plain; charset="utf-8"
>> >
>> > This is immensely helpful, thank you. I was very confused about why
>> > some
>> > versions of the pipeline I saw were using group calculate and others
>> > were
>> > using interaction delay reconstruction and what that meant, and I
>> > think I
>> > have a more clear idea of what the different steps of the pipeline
>> > are
>> > doing. There are still a few things I'm a bit confused about though
>> > in
>> > terms of the pipeline. For instance, whether or not I need to do
>> > TEprepare
>> > before group prepare, and if I need to do graph analysis (which I'm
>> > not
>> > sure I fully understand but also haven't looked deeply into) before
>> > group
>> > stats.
>> >
>> > If you don't mind me taking you up on your offer, I think seeing
>> > your
>> > example script might help clarify some of these issues.
>> >
>> > Thank you!
>> >
>> > On Tue, Sep 9, 2014 at 8:16 AM, Patricia Wollstadt <
>> > Patricia.Wollstadt at gmx.de> wrote:
>> >
>> >> Hello Max,
>> >>
>> >> I added a few comments to the questions regarding individual
>> >> parameters
>> >> below. To address the general problem of TRENTOOL telling you, that
>> >> there
>> >> are not enough sample points in your data: From what I can see in
>> >> your
>> >> script, you probably don't have enough data points in each time
>> >> series to
>> >> robustly estimate TE. You analyze 800 ms of data sampled at 300 Hz,
>> >> which
>> >> gives you 240 samples per time series. Can you maybe avoid
>> >> downsampling to
>> >> 300 Hz and downsample to 600 Hz instead? Or could you analyze a
>> >> longer time
>> >> window of interest?
>> >> Note that you also 'lose' data to embedding and the interaction
>> >> delay: The
>> >> first point that can be used for TE estimation is at max. embedding
>> >> length
>> >> + max. interaction delay in samples. For example: max. embedding
>> >> length =
>> >> dim * tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the max
>> >> interaction
>> >> delay of 30 ms = 9 samples. In this example, you would be left with
>> >> 240 -
>> >> 29 samples for TE estimation per trial. There is also the
>> >> possibility to
>> >> estimate time resolved TE/TE for shorter time windows of interest
>> >> (see
>> >> section 4.4 in the manual); however, this method requires the use
>> >> of a GPU
>> >> for TE estimation.
>> >>
>> >> I would further recommend to use the new pipeline for group
>> >> statistics
>> >> described in the manual in section 4.5 (the function
>> >> 'TEgroup_calculate' is
>> >> deprecated). The new pipeline allows you to reconstruct the
>> >> interaction
>> >> delay and uses the following functions (see also comments in the
>> >> script):
>> >>
>> >> TEgroup_prepare -> prepares all data sets (all subjects/all
>> >> conditions)
>> >> for group analysis (this means finding common embedding parameters
>> >> such
>> >> that estimates are not biased between groups)
>> >> InteractionDelayReconstruction_calculate -> estimates TE for
>> >> individual data sets and all assumed interaction delays u
>> >> InteractionDelayReconstruction_analyze -> reconstructs the
>> >> interaction delay by selecting the u that maximizes TE for each
>> >> channel
>> >> TEgroup_stats -> calculate group statistics using a permutation
>> >> test
>> >>
>> >> I can send you an example script for group TE analysis using this
>> >> pipeline
>> >> to get you started. I hope this helps you to get the group analysis
>> >> running. Just write again if you're having trouble setting up the
>> >> pipeline
>> >> or something is not clear about the parameters/my comments.
>> >>
>> >> Best,
>> >> Patricia
>> >>
>> >>
>> >>
>> >>
>> >> On 09/04/2014 08:30 PM, Max Cantor wrote:
>> >>
>> >> Hi fieldtrippers,
>> >>
>> >> I know trentool is not produced by the Donders Institute, so I'm
>> >> not 100%
>> >> sure if it is appropriate to ask questions about it here, but to
>> >> the best
>> >> of my knowledge they do not have a mailing list and I saw a few
>> >> trentool
>> >> questions in the archives, so I'm going to assume it's ok...
>> >>
>> >> In any case, below is my current pipeline (slightly modified for
>> >> comprehensibility):
>> >>
>> >> (notes in bold are comments/questions made in this email, not
>> >> present in
>> >> the pipeline. Sorry in advance for the long post! Any help would be
>> >> greatly
>> >> appreciated as I'm a bit over my head on this but I think I'm
>> >> close!)
>> >>
>> >> *****
>> >>
>> >> % Prepare group TE data
>> >>
>> >> cfgP = [];
>> >> cfgP.Path2TSTOOL = *TSTOOLPATH*
>> >> cfgP.TEcalctype = 'VW_ds';
>> >> cfgP.channel = {'ctfdip_LAC' 'ctfdip_RAC'};
>> >>
>> >> *I'm trying to find the transfer entropy between the left and right
>> >> auditory cortices in my experiment. The input is virtual sensor
>> >> data that
>> >> was produced using SAM in fieldtrip on real MEG data. *
>> >>
>> >> % specify u to be scanned
>> >>
>> >> cfgP.predicttime_u = 30;
>> >> cfgP.toi = [-0.4 0.4];
>> >>
>> >> *For clarification, the predicttime_u is in seconds but the toi
>> >> is in
>> >> milliseconds. If I understand correctly, the predicttime_u must fit
>> >> within
>> >> the toi, but beyond that are there any benefits to it being earlier
>> >> or
>> >> later?* PW: The predictiontime_u is in milliseconds and the toi is
>> >> in
>> >> seconds. The prediction time is the assumed interaction delay
>> >> between your
>> >> two sources and should fit within your toi. In general it is
>> >> preferable to
>> >> use the method for interaction delay reconstruction for TE
>> >> estimation,
>> >> because it allows you to reconstruct the actual delay between your
>> >> source
>> >> and target times series. A non-optimal u/interaction delay may
>> >> cause an
>> >> underestimation of TE, so it is recommended to use the pipeline for
>> >> interaction delay reconstruction whenever estimating TE for unknown
>> >> delays.
>> >> If you use the methods for interaction delay reconstruction
>> >> 'predicttime_u' is replaced by
>> >> cfgTEP.predicttimemin_u % minimum u to be scanned
>> >> cfgTEP.predicttimemax_u % maximum u to be scanned
>> >> cfgTEP.predicttimestepsize % time steps between u to be scanned
>> >> A large range for u values to be scanned increases computing time a
>> >> lot,
>> >> so it is best to limit the u range to values that are
>> >> physiologically
>> >> plausible.
>> >>
>> >>
>> >> % ACT (Autocorrelation Time) estimation and constraints
>> >>
>> >> cfgP.maxlag = 150;
>> >> cfgP.actthrvalue = 7.5;
>> >> cfgP.minnrtrials = 5;
>> >>
>> >> *My understanding is maxlag should be 1/2 the sampling rate, so
>> >> since
>> >> the data are downsampled to 300hz, it should be 150. I know that
>> >> the sample
>> >> rate and filters are used to determine the actthrvalue, but I don't
>> >> actually know the calculation. 7.5 was a rough guess just to test
>> >> the
>> >> pipeline. I'm also uncertain of what minnrtrials should be.* PW:
>> >> You can
>> >> set the actthrvalue based on the filtering you did prior to TE
>> >> analysis. If
>> >> you for example highpass filtered at 10 Hz, you shouldn't find an
>> >> ACT
>> >> higher than 30 samples, because you filtered out any components of
>> >> the
>> >> signal slower than 10 Hz/30 samples (given your sampling frequency
>> >> of 300
>> >> Hz). So in this scenario the actthrvalue would be 30.
>> >> A good value for cfgP.minnrtrials is 12 (a minimum number of trials
>> >> is
>> >> needed to realize the permutation test for estimated TE values).
>> >>
>> >>
>> >> % Optimization
>> >>
>> >> cfgP.optimizemethod = 'ragwitz';
>> >> cfgP.ragdim = 4:8;
>> >> cfgP.ragtaurange = [0.2 0.4];
>> >> cfgP.ragtausteps = 15;
>> >> cfgP.repPred = 100;
>> >>
>> >> *I am completely at a loss for this. I've done some reading into
>> >> transfer entropy, mutual information, etc., cited in trentool, but
>> >> I'm yet
>> >> to understand how exactly this optimization works and what the
>> >> configuration should be, given my data and experimental
>> >> intentions.* PW:
>> >> The Ragwitz criterion tries to find optimal embedding parameters
>> >> dim and
>> >> tau for the data. To do that, the method iteratively takes all
>> >> possible
>> >> combinations of dim and tau values that are provided in cfgP.ragdim
>> >> and
>> >> cfgP.ragtaurange/.ragtausteps and tests how well these combinations
>> >> embed
>> >> the data. To test an embedding, the method builds the embedding
>> >> vectors
>> >> from the data; it then tests for each point how well the next point
>> >> in time
>> >> can be predicted from the reference point's nearest neighbours. So
>> >> for each
>> >> embedded point, the method searches for the nearest neighbours and
>> >> calculates the average of those nearest neighbours. The difference
>> >> between
>> >> the averaged/predicted point and the actual next point is the error
>> >> of the
>> >> local predictor. The Ragwitz criterion will then return the
>> >> parameter
>> >> combination for which this error over all points is minimal.
>> >> The parameters set the following: 'ragdim' are dimensions to be
>> >> tested by
>> >> the method (I would reccomend to start with 2:10), 'ragtaurange'
>> >> together
>> >> with 'ragtausteps' specifies the tau values to be tested (TRENTOOL
>> >> will
>> >> build a vector from 0.2 to 0.4 in 15 steps). Note, that the values
>> >> here are
>> >> factors that are later multiplied with the ACT to obtain the actual
>> >> tau.
>> >> 'repPred' is the number of points that will be used for the local
>> >> prediction, i.e. the Ragwitz criterion will test the local
>> >> prediction and
>> >> calculate the error for the first 100 points in your time series.
>> >> The two
>> >> parameters 'flagNei' ans 'sizeNei' below specify the type of
>> >> neighbour
>> >> search conducted by the Ragwitz criterion: 'flagNei' tells the
>> >> method to
>> >> either conduct a kNN or range search; 'sizeNei' specifies the
>> >> number of
>> >> neighbours or the radius to be searched by a range search.
>> >>
>> >>
>> >> % Kernel-based TE estimation
>> >>
>> >> cfgP.flagNei = 'Mass';
>> >> cfgP.sizeNei = 4; % Default
>> >>
>> >> cfgP.ensemblemethod = 'no';
>> >> cfgP.outputpath = *OUTPUT PATH*;
>> >>
>> >> if ~exist(*Path for TEprepare data object*)
>> >> load VSdat;
>> >> TE_Wrd = {};
>> >> for i = 1:nConds
>> >> for j = 1:Nsub
>> >> TE_Wrd{i}{j} = TEprepare(cfgP, VSdat{i}{j});
>> >> end
>> >> end
>> >> clear VSdat;
>> >> save('TE_Wrd', 'TE_Wrd');
>> >> end
>> >>
>> >> *The configuration and virtual sensor data, organized in a 3 x 15
>> >> cell
>> >> of structures (condition by subject) are the input. The TEprepare
>> >> substructure is added to each individual condition x subject .mat
>> >> files'
>> >> data structure which are stored on disk independently.*
>> >>
>> >> % Use object_to_mat_conversion.m to replace individual condition x
>> >> subject
>> >> virtual sensor data
>> >> % .mat files with their TE_Wrd equivalent
>> >>
>> >> *I'm using a separate script to make some manipulations to the
>> >> objects
>> >> from disk; this will all eventually be integrated into the main
>> >> pipeline*.*
>> >> TRENTOOL seems to handle data output very differently from
>> >> fieldtrip and
>> >> I've had trouble thinking through the most logical way to handle
>> >> the data
>> >> so it's a bit haphazard right now.*
>> >>
>> >> load cond080sub01.mat
>> >>
>> >> cfgG = [];
>> >> cfgG.dim = cond080sub01.TEprepare.optdim;
>> >> cfgG.tau = cond080sub01.TEprepare.opttau;
>> >>
>> >> if isfield(cond080sub01, 'TEprepare')
>> >> TEgroup_prepare(cfgG, fileCell);
>> >> else
>> >> error('Need to run TEprepare before TEgroup_prepare');
>> >> end
>> >>
>> >> *For clarification, fileCell is a cell with the name of each
>> >> condition x
>> >> subject .mat file, which as I said before is collectively the same
>> >> as the 3
>> >> x 15 VSdat structure (condition x subject).*
>> >>
>> >> % Replace .mat files with '_for_TEgroup_calculate' version in
>> >> % object_to_mat_conversion.m
>> >>
>> >> % TE Group Calculate
>> >>
>> >> load cond080sub01.mat
>> >> if isfield(cond080sub01, 'TEgroupprepare')
>> >> for i = 1:length(fileCell)
>> >> TEgroup_calculate(fileCell{i});
>> >> end
>> >> else
>> >> error('Need to run TEgroup_prepare before TEgroup_calculate');
>> >> end
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> *At this step I get the following error: Error using
>> >> transferentropy (line
>> >> 337) \nTRENTOOL ERROR: not enough data points left after embedding
>> >> Error in
>> >> TEgroup_calculate (line 133) [TEresult] =
>> >> transferentropy(cfg,data);*
>> >>
>> >> % TE Group Stats
>> >>
>> >> cfgGSTAT = [];
>> >> cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];
>> >> cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];
>> >>
>> >> cfgGSTAT.uvar = 1;
>> >> cfgGSTAT.ivar = 2;
>> >> cfgGSTAT.fileidout = 'test_groupstats';
>> >>
>> >> TEgroup_stats(cfgGSTAT, fileCell);
>> >>
>> >> *Given the error above, I am yet to get to this step, but it does
>> >> not
>> >> seem fundamentally different from normal fieldtrip stats.*
>> >>
>> >> *****
>> >>
>> >> In case my notes were not clear or you skipped to the bottom, *my
>> >> primary concern is whether the error I'm getting in
>> >> TEgroup_calculate is a
>> >> pipeline issue* (I noticed the example pipeline in trentool, the
>> >> manual,
>> >> and published methods articles all seem to have slightly or
>> >> significantly
>> >> different pipeline compositions), *or if the error is* due to ACT,
>> >> ragwitz optimization, or some other faulty parameterization *on my
>> >> part
>> >> due to a lack of understanding of how transfer entropy works on a
>> >> more
>> >> theoretical/mathematical level*. If the latter is the case, is
>> >> there any
>> >> relatively straightforward way to conceptualize this, or is this
>> >> something
>> >> where I'm just going to have to keep reading and rereading until it
>> >> eventually makes sense? I've already done quite a bit of that and
>> >> it hasn't
>> >> pierced my thick skull yet but I'm sure it will eventually!
>> >>
>> >> Thank you so much,
>> >>
>> >> Max Cantor
>> >>
>> >>
>> >> --
>> >> Max Cantor
>> >> Lab Manager
>> >> Computational Neurolinguistics Lab
>> >> University of Michigan
>> >>
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing
>> >>
> listfieldtrip at donders.ru.nlhttp://mailman.science.ru.nl/mailman/listinfo/fie
> ldtrip
>> >>
>> >>
>> >> --
>> >> ------------------------------------------------------------
>> >>
>> >> Patricia Wollstadt, PhD Student
>> >>
>> >> MEG Unit, Brain Imaging Center Goethe University, Frankfurt,
>> >> Germany
>> >>
>> >> Heinrich Hoffmann Strasse 10, Haus 93 B D - 60528 Frankfurt am
>> >> Main
>> >>
>> >> _______________________________________________
>> >> fieldtrip mailing list
>> >> fieldtrip at donders.ru.nl
>> >> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>> >>
>> >
>> >
>> >
>> > --
>> > Max Cantor
>> > Lab Manager
>> > Computational Neurolinguistics Lab
>> > University of Michigan
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>> > End of fieldtrip Digest, Vol 46, Issue 3
>> > ****************************************
>> >
>>
>>
>>
>> Barbara Schorr, MSc
>> Clinical and Biological Psychology
>> University of Ulm
>> Albert-Einstein-Allee 47
>> 89069 Ulm
>>
>>
>>
>> _______________________________________________
>> fieldtrip mailing list
>> fieldtrip at donders.ru.nl
>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> --
> Jim Herring, MSc.
> Neuronal Oscillations Group
> Centre for Cognitive Neuroimaging
> Donders Institute for Brain, Cognition and Behaviour
> Radboud University Nijmegen
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
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>
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>
> End of fieldtrip Digest, Vol 46, Issue 5
> ****************************************
>
Barbara Schorr, MSc
Clinical and Biological Psychology
University of Ulm
Albert-Einstein-Allee 47
89069 Ulm
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