<div><div><img src="cid:1848cdbfdda924041311" style="max-width: 100%;"></div><br></div><div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, 18 Nov 2022 at 15:15, <<a href="mailto:fieldtrip-request@science.ru.nl">fieldtrip-request@science.ru.nl</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Send fieldtrip mailing list submissions to<br>
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Today's Topics:<br>
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1. Re: Help with ft_sourcestatistics (Schoffelen, J.M. (Jan Mathijs))<br>
2. Re: Help needed for timelock analysis<br>
(Francisco Javier Quirant Agulló)<br>
<br>
<br>
----------------------------------------------------------------------<br>
<br>
Message: 1<br>
Date: Fri, 18 Nov 2022 14:09:28 +0000<br>
From: "Schoffelen, J.M. (Jan Mathijs)"<br>
<<a href="mailto:janmathijs.schoffelen@donders.ru.nl" target="_blank">janmathijs.schoffelen@donders.ru.nl</a>><br>
To: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>><br>
Subject: Re: [FieldTrip] Help with ft_sourcestatistics<br>
Message-ID: <<a href="mailto:F2D7B73C-A0E7-4529-87AD-510992F431B0@donders.ru.nl" target="_blank">F2D7B73C-A0E7-4529-87AD-510992F431B0@donders.ru.nl</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Hi Francisco,<br>
<br>
The fieldname ‘avg’, in the source reconstructed data has a historical reason, but is a bit unlucky and misleading. Indeed it refers to the fact that the numeric data in the sub-structure reflects an average (across a bunch observations). In your case it is the signal power in the baseline and in the post stimulus respectively.<br>
<br>
Now, if you want to run a permutation test, you need a sufficient amount of subjects (which in this case serve as the ‘observations’). This is what Stephan refers to in his earlier reply in this thread. Given that you used only 1 subject worth of data, the code throws an error (and correctly so), because there is not much to permute. To do a meaningful statistical inference you need at least results from 15 or so (but ideally more) subjects.<br>
<br>
If, on the other hand, you want to perform the statistics within the subject (i.e. using the individual trials as units-of-observation for the permutation) you need to set up your analysis in a slightly different way.<br>
<br>
Best wishes,<br>
<br>
Jan-Mathijs<br>
<br>
<br>
On 18 Nov 2022, at 14:20, FRANCISCO JAVIER GÓMEZ CAMPOS via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>> wrote:<br>
<br>
<br>
Dear Stephan,<br>
<br>
Thank you for your response, but in this case our source analysis data does not contain the average across subjects. If you refer to the ‘avg’ structure, it is one of the outputs coming from performing the source analysis by contrasting the pre and post stimulus interval. During the process, I calculate an inverse filter calculated from both intervals together and then applied separately to each interval, from this process I obtain the output ‘avg’, I do this for each subject and each condition.<br>
<br>
You can see it in more detail at this link <a href="https://www.fieldtriptoolbox.org/tutorial/beamformer/" rel="noreferrer" target="_blank">https://www.fieldtriptoolbox.org/tutorial/beamformer/</a><<a href="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/tutorial/beamformer/__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1IE_2mg0g$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/tutorial/beamformer/__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1IE_2mg0g$</a>> in the section ‘Source Analysis: Contrasting activity to another interval’.<br>
<br>
Our source data are distributed as follows, for each subject I have the mean power of each voxel for each condition independently, perhaps best understood with the following image: <<a href="https://urldefense.com/v3/__https://gyazo.com/00af9e5ca9647bec71d561c4197eefc5__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1KfbWELHw$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://gyazo.com/00af9e5ca9647bec71d561c4197eefc5__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1KfbWELHw$</a>> <a href="https://gyazo.com/39bb9c919f469d95f984a68b6dca6e18" rel="noreferrer" target="_blank">https://gyazo.com/39bb9c919f469d95f984a68b6dca6e18</a><<a href="https://urldefense.com/v3/__https://gyazo.com/39bb9c919f469d95f984a68b6dca6e18__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1JEfrkLwg$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://gyazo.com/39bb9c919f469d95f984a68b6dca6e18__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1JEfrkLwg$</a>><br>
<br>
[<a href="https://lh6.googleusercontent.com/fLsaCgJYAhYIaQRGNxzNEdwzQN7n10QBbjS-oOeTbhvP68qQ3rBW8dnFa37-Vg2pzaNbY2NiVPCW4YCNLwwed-IpPQfXo-RzL1Sh9ac7LgqEkClX4kIr79C_BAfOvGhmtIMeW-wgY5VYJ31TSbyQpcllTBTnD13S5roabFDKvjtghYDMV7sr-2Kz1Hanbg" rel="noreferrer" target="_blank">https://lh6.googleusercontent.com/fLsaCgJYAhYIaQRGNxzNEdwzQN7n10QBbjS-oOeTbhvP68qQ3rBW8dnFa37-Vg2pzaNbY2NiVPCW4YCNLwwed-IpPQfXo-RzL1Sh9ac7LgqEkClX4kIr79C_BAfOvGhmtIMeW-wgY5VYJ31TSbyQpcllTBTnD13S5roabFDKvjtghYDMV7sr-2Kz1Hanbg</a>]<br>
We suspect that the problem could stem from the fact that our data structure differs from the one described in the tutorial for ft_sourcestatistics in that we do not provide sources from each trail, but the average source of each condition instead. So, our question is whether with this data structure we can perform a nonparametric randomization test, by creating the null hypothesis distribution shuffling voxels between conditions and then obtain an 'average' null distribution across subjects. I am not sure if this makes sense. Another way could be to shuffle conditions across subjects and obtain a null hypothesis distribution from this shuffle.<br>
<br>
<br>
Thank you again,<br>
<br>
Best,<br>
<br>
Francisco Javier Gómez<br>
<br>
Instituto de Biomedicina de Sevilla(IBIS)<br>
<br>
<br>
________________________________<br>
De: STEPHAN MORATTI <<a href="mailto:smoratti@psi.ucm.es" target="_blank">smoratti@psi.ucm.es</a><mailto:<a href="mailto:smoratti@psi.ucm.es" target="_blank">smoratti@psi.ucm.es</a>>><br>
Enviado: jueves, 17 de noviembre de 2022 14:44<br>
Para: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>><br>
Cc: FRANCISCO JAVIER GÓMEZ CAMPOS <<a href="mailto:fjgomez-ibis@us.es" target="_blank">fjgomez-ibis@us.es</a><mailto:<a href="mailto:fjgomez-ibis@us.es" target="_blank">fjgomez-ibis@us.es</a>>><br>
Asunto: Re: [FieldTrip] Help with ft_sourcestatistics<br>
<br>
Dear Francisco,<br>
<br>
It seems that in your source data you probably have the average across subjects, but the individual source data for each subject is missing. You may store them in individual cells and then submit these cells to the source statistics.<br>
<br>
Best,<br>
<br>
Stephan<br>
<br>
<br>
El jue, 17 nov 2022 a las 11:32, FRANCISCO JAVIER GÓMEZ CAMPOS via fieldtrip (<<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>>) escribió:<br>
Dear community,<br>
<br>
I am having problems using the ft_sourcestatistics function. I would like to use this function<br>
to conduct a within-subject analysis to establish which areas show differences between two<br>
experimental conditions. I mainly have doubts about two aspects, the first one is about how to<br>
attach the source analysis data, and the second one is about the design of the design matrix.<br>
<br>
Regarding the data coming from the source analysis, I have the sources separated by<br>
conditions (obtained by following this tutorial <a href="https://www.fieldtriptoolbox.org/tutorial/beamformer/" rel="noreferrer" target="_blank">https://www.fieldtriptoolbox.org/tutorial/beamformer/</a><<a href="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/tutorial/beamformer/__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1IE_2mg0g$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/tutorial/beamformer/__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1IE_2mg0g$</a>>):<br>
<br>
Source analysis data for each condition:<br>
freq: 4.8302<br>
cfg: [1×1 struct]<br>
dim: [85 110 132]<br>
inside: [1234200×1 logical]<br>
pos: [1234200×3 double]<br>
method: 'average'<br>
avg: [1×1 struct]<br>
<br>
avg:<br>
pow: [1234200×1 double]<br>
noise: [1234200×1 double]<br>
filter: {1234200×1 cell}<br>
label: {61×1 cell}<br>
filterdimord: '{pos}_ori_chan'<br>
<br>
The code I use is the following (based on this code in this tutorial<br>
<a href="https://www.fieldtriptoolbox.org/example/source_statistics/" rel="noreferrer" target="_blank">https://www.fieldtriptoolbox.org/example/source_statistics/</a><<a href="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/example/source_statistics/__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1IM6xnJLg$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/example/source_statistics/__;!!HJOPV4FYYWzcc1jazlU!6N5sAUOLFHX5VmnV9FuI390JcfBKDqKW5I94Eju8n6bs515XFjjjZfbDgj9-ZJOis1KRANuRDSZRpVED-QyvuI2uvBEic1IM6xnJLg$</a>> ) :<br>
<br>
design=[1 2]; %for 2 conditions<br>
% run sourcestatistics using cluster based correction %<br>
cfg = [];<br>
cfg.dim = sourceanalysis_condition1(1).dim;<br>
<br>
cfg.method = 'montecarlo';<br>
cfg.statistic = 'ft_statfun_depsamplesT';<br>
cfg.parameter = 'pow';<br>
cfg.correctm = 'cluster';<br>
cfg.numrandomization = 'all';<br>
cfg.alpha = 0.05; % note that this only implies single-sided testing<br>
cfg.tail = 0;<br>
cfg.design(1,:) = [1:length(find(design==1)) 1:length(find(design==2))];<br>
cfg.design(2,:) = design;<br>
cfg.uvar = 1; % row of design matrix that contains unit variable (in this case: trials)<br>
cfg.ivar = 2; % row of design matrix that contains independent variable (the conditions)<br>
stat = ft_sourcestatistics(cfg, sourceanalysis_condition1, sourceanalysis_condition2)<br>
<br>
When I run this code, I get the following error:<br>
Error using ft_statfun_depsamplesT (line 83)<br>
The data must contain at least two units of observation (trials or subjects).<br>
<br>
I understand that my design matrix and/or the structure of sourceanalysis_condition are<br>
wrong, I have tried different combinations and I cannot find the solution. I hope you can<br>
guide me in this regard so that I can adapt it to the conditions of my study.<br>
<br>
Thanks,<br>
Francisco Javier Gómez,<br>
Instituto de Biomedicina de Sevilla (IBIS)<br>
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<br>
--<br>
<br>
Stephan Moratti, PhD<br>
Profesor de Psicología Experimental<br>
Departamento de Psicología Experimental<br>
Facultad de Psicología<br>
Center for Cognitive and Computational Neuroscience<br>
Universidad Complutense de Madrid<br>
<a href="mailto:smoratti@ucm.es" target="_blank">smoratti@ucm.es</a><mailto:<a href="mailto:smoratti@ucm.es" target="_blank">smoratti@ucm.es</a>><br>
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------------------------------<br>
<br>
Message: 2<br>
Date: Fri, 18 Nov 2022 14:46:13 +0000<br>
From: Francisco Javier Quirant Agulló <<a href="mailto:FJQUIAGU@upv.edu.es" target="_blank">FJQUIAGU@upv.edu.es</a>><br>
To: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>><br>
Subject: Re: [FieldTrip] Help needed for timelock analysis<br>
Message-ID:<br>
<<a href="mailto:DB6P190MB0055EF7B329F4780D5C4191FF9099@DB6P190MB0055.EURP190.PROD.OUTLOOK.COM" target="_blank">DB6P190MB0055EF7B329F4780D5C4191FF9099@DB6P190MB0055.EURP190.PROD.OUTLOOK.COM</a>><br>
<br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Hi Jan,<br>
<br>
Thank you very much for your patience and efforts. You are very much right, I have gotten those results some minutes before your email and was collecting information for calling Fixed on the mailing list. Truly i must have passed on something trivial because for the past week i was getting something more like this:<br>
<br>
[cid:c47d9644-6773-45ba-b28c-2f924dfc1937]<br>
<br>
This screenshot is from checking the sent code yesterday.<br>
<br>
Answering your side note, i was trying to separate the single trial in to multiple because of a coment on the ft_timelockanalysis function. While trying to pass the variables directly it gave an error but when i called a file didn't say anything so i kept it that way. Now that it works properly on single trial this piece will be erased.<br>
<br>
Thanks a lot again for your time and help,<br>
Greetings,<br>
Javier.<br>
________________________________<br>
De: fieldtrip <<a href="mailto:fieldtrip-bounces@science.ru.nl" target="_blank">fieldtrip-bounces@science.ru.nl</a>> en nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>><br>
Enviado: viernes, 18 de noviembre de 2022 14:27<br>
Para: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>><br>
Cc: Schoffelen, J.M. (Jan Mathijs) <<a href="mailto:janmathijs.schoffelen@donders.ru.nl" target="_blank">janmathijs.schoffelen@donders.ru.nl</a>><br>
Asunto: Re: [FieldTrip] Help needed for timelock analysis<br>
<br>
Hi Javier,<br>
<br>
Thanks for the update, and sorry to continue nagging about this, but it is - despite your (and certainly also my) best efforts - still not clear what your problem is. I felt lucky, so I downloaded the data, and performed the following (inspired by the code that you sent along):<br>
<br>
[cid:57062A47-E1C6-468B-8B4D-D779231F63C0]<br>
<br>
The call to ft_topoplotER yielded three figures, which clearly show different topographies. Note that what is visualized is the (average of the) time course, not the covariance. So the interim conclusion is that the data is different in the 3 different ‘conditions’, at least in their average (which is according to the code that you provided)<br>
<br>
[cid:2A726A6B-E907-494C-A646-39A8D1D20FA9]<br>
<br>
<br>
Next, I decided to compute the covariance per condition, and check whether they are equal. This does not seem to be the case, because I get:<br>
<br>
[cid:771BB67D-CDD4-4865-A6D0-42FFB9CDD0A6]<br>
<br>
Visualization of the covariance matrices confirms this<br>
<br>
[cid:A811EEE6-5E6D-461C-ABEF-0FCB776E8917]<br>
<br>
<br>
In summary, I don’t think that there is something wrong with ft_timelockanalysis, at least not in the sense that the 'covariance distributions’ are the same. What makes you (still) think that they are the same?<br>
<br>
<br>
<br>
Also - as a side note - I don’t understand why you first create a data structure in fieldtrip style, save it as a mat-file, and then call ft_preprocessing on it. What do you think that this step achieves?<br>
<br>
Best wishes,<br>
Jan-Mathijs<br>
<br>
<br>
<br>
<br>
On 18 Nov 2022, at 11:44, Francisco Javier Quirant Agulló via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>> wrote:<br>
<br>
Hi Jan-Mathijs,<br>
<br>
<br>
<br>
First thank you again for your patience and the suggested links. I apologize as trying to be concise I’ve fallen way short. Lets try this again with more organized information. Also apologies for any typos as English is my second language.<br>
<br>
<br>
<br>
Goal: As stated in my initial mail the objective is to characterize source estimation methods through metrics. Some of this metrics are derived from a Resolution matrix that is obtained with the inverse kernel and the leadfield matrix (R = KL). Resolution matrices for all source points get averaged in a so called empirical Resolution matrix from where the metrics get extracted.<br>
<br>
<br>
<br>
Steps: To obtain this matrix all source model points have to “activate”. This is achieved by dividing the brain volume in patches (following Desikan-Killiani’s atlas) that activate in different trials or sessions. With Brainstorm a step signal of 1000 samples and fs= 500Hz gets generated and forced upon those patches, so for every source point within them this is the source signal. There are 68 patches that activate in different trials or sessions, from each of these a EEG (64 electrode cap) gets simulated using a BEM model calculated with Brainstorm default/tutorial files. Some amount of noise is added, random noise in the source signals distribution and a combination of pink+white noise as sensor noise when obtaining the EEG signals. At the end of the day there are 68 different source activation maps with their associated EEG level signals. With this EEG signals different estimations (dSPM, eLORETA, sLORETA…) will be performed and Resolution matrices obtained for each of them.<br>
<br>
<br>
<br>
>From here the EEG files get exported to matlab to calculate eLORETA using FieldTrip. eLORETA needs as input the data covariance calculated through ft_timelockanalysis to get the results in the time domain, this is at least what I understand after reading the function information and the tutorials. When calling the function using the code from the first mail and independently of the EEG signals used I get the same covariance distribution and hence, the exact same source map after applying eLORETA. The step signal is the same, true, but the activation region within the brain is different and so they are the EEG level signals.<br>
<br>
<br>
<br>
Why EEG signals derived from different brain activation regions get the same covariance distribution when calling ft_timelockanalysis?<br>
<br>
<br>
<br>
Files and aditional information:<br>
[<a href="https://res-geo.cdn.office.net/assets/mail/file-icon/png/generic_16x16.png%5Dfiles.mat" rel="noreferrer" target="_blank">https://res-geo.cdn.office.net/assets/mail/file-icon/png/generic_16x16.png]files.mat</a><<a href="https://urldefense.com/v3/__https://upvedues-my.sharepoint.com/:u:/g/personal/fjquiagu_upv_edu_es/EdO01tlTC69Bi6p42iMb63EB9xV8o9BWTZhZ8uWE50KHNg?e=TWKEsU__;!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYUsxhiHaw$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://upvedues-my.sharepoint.com/:u:/g/personal/fjquiagu_upv_edu_es/EdO01tlTC69Bi6p42iMb63EB9xV8o9BWTZhZ8uWE50KHNg?e=TWKEsU__;!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYUsxhiHaw$</a>> contains the matlab variables where<br>
- data: is merely a template struc for the code.<br>
- eeg_01/02/03: these are 3 different EEG level signals for the activation of 3 different brain regions.<br>
- vol: headmodel employed for all inverse problem related calculations.<br>
- elec: sensor information for EEG channels.<br>
- sourcemodel_and_leadfield: kind of self explanatory, merely a crappy demo for quick calculations.<br>
<br>
In [<a href="https://res-geo.cdn.office.net/assets/mail/file-icon/png/generic_16x16.png" rel="noreferrer" target="_blank">https://res-geo.cdn.office.net/assets/mail/file-icon/png/generic_16x16.png</a>] code_example.m<<a href="https://urldefense.com/v3/__https://upvedues-my.sharepoint.com/:u:/g/personal/fjquiagu_upv_edu_es/EdYcnPUx2r5Bl-EAfx-0pBsB0vW0X2Vg-FHzKwD5g8OuBQ?e=oPU19A__;!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYXjSqDPUQ$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://upvedues-my.sharepoint.com/:u:/g/personal/fjquiagu_upv_edu_es/EdYcnPUx2r5Bl-EAfx-0pBsB0vW0X2Vg-FHzKwD5g8OuBQ?e=oPU19A__;!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYXjSqDPUQ$</a>> there is a demo of what i am doing in matlab using the previous variables.<br>
<br>
<br>
I hope everything is more clear now. Thank you again for your time and patience.<br>
<br>
Greetings,<br>
Javier<br>
<br>
<br>
<br>
De: fieldtrip <<a href="mailto:fieldtrip-bounces@science.ru.nl" target="_blank">fieldtrip-bounces@science.ru.nl</a><mailto:<a href="mailto:fieldtrip-bounces@science.ru.nl" target="_blank">fieldtrip-bounces@science.ru.nl</a>>> En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip<br>
Enviado el: viernes, 18 de noviembre de 2022 7:50<br>
Para: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>><br>
CC: Schoffelen, J.M. (Jan Mathijs) <<a href="mailto:janmathijs.schoffelen@donders.ru.nl" target="_blank">janmathijs.schoffelen@donders.ru.nl</a><mailto:<a href="mailto:janmathijs.schoffelen@donders.ru.nl" target="_blank">janmathijs.schoffelen@donders.ru.nl</a>>><br>
Asunto: Re: [FieldTrip] Help needed for timelock analysis<br>
<br>
<br>
<br>
Hi Javier,<br>
<br>
<br>
<br>
Sorry to repeat my self, but you are still not clear.<br>
<br>
<br>
<br>
You ask ‘why is it giving me always the same result despite being completely different EEG signals’.<br>
<br>
<br>
<br>
I don’t understand what ‘it’ is, and I see nowhere in your messages what you mean with ‘completely different EEG signals’. It’s all still Spanish to me.<br>
<br>
<br>
<br>
Different channels? Different simulations? Different step functions? Different noise? How did you do the simulations? How do the simulated signals look? What forward model was used in the simulation? Did you simulate enough variability in the sources’ time courses in order to have a chance to observe spatiotemporal variability at the scalp level? What did you expect from the call to ft_timelockanalysis?<br>
<br>
<br>
<br>
Also, please refer to <a href="http://www.catb.org/~esr/faqs/smart-questions.html" rel="noreferrer" target="_blank">http://www.catb.org/~esr/faqs/smart-questions.html</a><<a href="https://urldefense.com/v3/__http://www.catb.org/*esr/faqs/smart-questions.html__;fg!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYXL7d1FAw$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__http://www.catb.org/*esr/faqs/smart-questions.html__;fg!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYXL7d1FAw$</a>> and <a href="https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/" rel="noreferrer" target="_blank">https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/</a><<a href="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/__;!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYUHTvfexA$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/__;!!HJOPV4FYYWzcc1jazlU!9BfdnfX8eW7Tqf2Fa3O6aptGt8B_Auuj6EUkkl5xZdJJRsgsnBYCK7PD8As-zMvgdOk4WJkw1pfWqDzzztCp2BupPB99kYUHTvfexA$</a>><br>
<br>
<br>
<br>
Good luck,<br>
<br>
<br>
<br>
Jan-Mathijs<br>
<br>
<br>
<br>
<br>
On 17 Nov 2022, at 18:04, Francisco Javier Quirant Agulló via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>> wrote:<br>
<br>
<br>
<br>
Hi Jan,<br>
<br>
<br>
<br>
Sorry for not being clear enough. The question is, how should I apply the timelock function properly? Or, why is it giving me always the same result despite being completely different EEG signals?<br>
<br>
<br>
<br>
Thanks and greetings,<br>
Javier<br>
<br>
<br>
<br>
De: fieldtrip <<a href="mailto:fieldtrip-bounces@science.ru.nl" target="_blank">fieldtrip-bounces@science.ru.nl</a><mailto:<a href="mailto:fieldtrip-bounces@science.ru.nl" target="_blank">fieldtrip-bounces@science.ru.nl</a>>> En nombre de Schoffelen, J.M. (JanMathijs) via fieldtrip<br>
Enviado el: jueves, 17 de noviembre de 2022 17:19<br>
Para: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>><br>
CC: Schoffelen, J.M. (Jan Mathijs) <<a href="mailto:janmathijs.schoffelen@donders.ru.nl" target="_blank">janmathijs.schoffelen@donders.ru.nl</a><mailto:<a href="mailto:janmathijs.schoffelen@donders.ru.nl" target="_blank">janmathijs.schoffelen@donders.ru.nl</a>>><br>
Asunto: Re: [FieldTrip] Help needed for timelock analysis<br>
<br>
<br>
<br>
Hi Javier,<br>
<br>
<br>
<br>
What is your question? I don’t manage to distill it from your e-mail.<br>
<br>
<br>
<br>
Best wishes,<br>
<br>
<br>
<br>
Jan-Mathijs<br>
<br>
<br>
<br>
<br>
<br>
On 17 Nov 2022, at 15:42, Francisco Javier Quirant Agulló via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><mailto:<a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a>>> wrote:<br>
<br>
<br>
<br>
Hello everyone,<br>
<br>
<br>
<br>
My name is Javier, I am a student of Biomedical engineering and I am working currently on my final master's project. The project aims to compare various inverse algorithms with some metrics and characterize them.<br>
<br>
<br>
<br>
Right now what I am doing is generating a step signal at source level in 68 different brain sections and obtaining simulated EEG signals with fs = 500 Hz & 2s length (1000 samples) and some noise added. The initial steps are performed using BrainStorm and EEGs are exported to matlab to apply eLORETA and sLORETA with Fieldtrip.<br>
<br>
<br>
<br>
For this I am trying to calculate the time domain information for the signals withft_timelockanalysis and use the results in ft_sourceanalysis. I realized that the source results were identical for the different EEGs (each one for a particular brain section), while the EEGs seem right the output of the ft_timelockanalysis is the same for all signals. Checking the function information I saw a note in line 45 that says: “% FIXME if input is one raw trial, the covariance is not computed correctly”. Tried dividing the EEG in 4 trials but no difference whatsoever. Spent some time also checking results within the function but haven’t made any progress.<br>
<br>
<br>
<br>
I think I may be using the data with an incorrect format or a wrong cfg, any help would be much apreciated. Follows the code.<br>
-----------------------------------<br>
data.trial{1} = real(eeg_01.F);% No idea why but EEGs from BrainStorm convert to complex in matlab<br>
data.time{1} = eeg_01.Time;<br>
<br>
<br>
<br>
save('temp.mat','data');cfg = []; % This section only when I divide in trials (obviously)<br>
cfg.trialdef.triallength = 0.5;<br>
cfg.datafile = 'temp.mat';<br>
cfg.trialdef.eventtype = 'none';<br>
cfg= ft_definetrial(cfg);<br>
data = ft_preprocessing(cfg);<br>
delete 'temp.mat'<br>
<br>
<br>
<br>
cfg = [];<br>
cfg.covariance = 'yes';<br>
cfg.keeptrials = 'yes'; % I comment these two lines for single trial<br>
cfg.trials = 'all'<br>
data_cov = ft_timelockanalysis(cfg, data);<br>
<br>
<br>
<br>
cfg.layout = 'EEG1010.lay';<br>
ft_topoplotER(cfg,data_cov); % This to plot the result, I get the same for all EEG files.<br>
<br>
<br>
<br>
-----------------------------------<br>
Here you have the mat file with all variables and 3 of the EEGs already loaded: [<a href="https://res-geo.cdn.office.net/assets/mail/file-icon/png/generic_16x16.png" rel="noreferrer" target="_blank">https://res-geo.cdn.office.net/assets/mail/file-icon/png/generic_16x16.png</a>] files.mat<<a href="https://urldefense.com/v3/__https:/upvedues-my.sharepoint.com/:u:/g/personal/fjquiagu_upv_edu_es/EUPygCCic5NEiZak0leUSHsBSJAOKw9aJ_-ae7oibQIhNQ?e=TPWOkC__;!!HJOPV4FYYWzcc1jazlU!7ZVGIVpHujMhbuqj07PjD4_tJOJIGEhEa9EXa1hIOo6IqWXpztkw-y9kXyF9UpcjBTlphhjSrdqGapjwDYPr0saXmetMS6Y5taMH_w$" rel="noreferrer" target="_blank">https://urldefense.com/v3/__https:/upvedues-my.sharepoint.com/:u:/g/personal/fjquiagu_upv_edu_es/EUPygCCic5NEiZak0leUSHsBSJAOKw9aJ_-ae7oibQIhNQ?e=TPWOkC__;!!HJOPV4FYYWzcc1jazlU!7ZVGIVpHujMhbuqj07PjD4_tJOJIGEhEa9EXa1hIOo6IqWXpztkw-y9kXyF9UpcjBTlphhjSrdqGapjwDYPr0saXmetMS6Y5taMH_w$</a>><br>
<br>
<br>
<br>
<br>
<br>
<br>
I hope everything is clear enought.<br>
<br>
<br>
<br>
Greetings,<br>
Javier<br>
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End of fieldtrip Digest, Vol 144, Issue 14<br>
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</blockquote></div></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature">Edit Lelle Gyori<br></div>