[Eeglablist] Artifact detection and ICA on data with multiple epoch types
Chadwick Boulay
boulay at brain.bio.keio.ac.jp
Wed May 8 18:50:09 PDT 2013
Hi Becky,
My previous answers were sent directly to you, not to the list. Since
the list is moderated and there are large time differences among you,
me, and the moderators it would be a while before you saw my answer, if
at all. It was a simple answer so I thought you'd appreciate getting it
sooner. The other readers are probably confused about what code your
reply is referring to. Sorry about that.
Thank you for noticing those fields I forgot to change.
As for component rejection, you can indicate which components to reject
using MERGED_SET without actually rejecting them. This will create a
field in MERGED_SET.reject called gcompreject. Then copy that field over
to your EEG1, EEG2, and EEG3 reject structs.
if ~isfield(EEG1,'reject') || ~isstruct(EEG1.reject)
EEG1.reject = struct('gcompreject',MERGED_SET.reject.gcompreject);
else
EEG1.reject.gcompreject = MERGED_SET.reject.gcompreject;
end
%Repeat for EEG2 and EEG3
%I also like to run a eeg_checkset anytime I change the EEG structure
directly though I can no longer remember exactly why or what it does.
EEG1= eeg_checkset(EEG1);
You should now be able to use the GUI or EEG = pop_subcomp(EEG, [], 0);
to subtract those marked components from your channel data.
Finally, while my original reply used EEG1, EEG2, EEG3 for simplicity,
it now seems like it would be better to put your EEG structs into a cell
array and loop over the array.
i.e.
EEGS{1} = EEG1;
EEGS{2} = EEG2;
EEGS{3} = EEG3;
for set_ix = 1:length(EEGS)
%Do you work on EEGS{set_ix}
end
Cheers,
Chad
On 5/9/2013 12:23 AM, Becky Prince wrote:
> Hi all,
>
> Thanks to everyone for the responses!
>
> I tried Chad's solution first, which involved manually concatenating
> the the 3 epoched data sets into the data field of an EEG structure,
> then running ICA on the concatenated set and copying the ICA results
> into the ICA fields in the epoched sets. This solution works but I
> ran into a minor problem that's worth mentioning for anyone else who
> wants to do this following the solution previously posted by Chad.
> When one of the data sets is copied into TEMP and all of the epoched
> data is added to TEMP.data, a few other fields in TEMP need to be
> changed (otherwise they retain the data from the copied epoched set,
> and the ICA command produces an error).
>
> TEMP.trials = 1;
> TEMP.pnts = size(TEMP.data,2);
> TEMP.xmin = 0;
> TEMP.xmax = (TEMP.pnts/TEMP.srate);
> TEMP.times = [];
>
> Mikolaj and Arno, thank you for the advice on converting the epochs of
> different lengths to continuous data. Mikolaj you said you thought
> there was an EEGLAB function to 'de-epoch' the data, so I'm guessing
> you were thinking of 'eeg_epoch2continuous' which Arno has also
> mentioned. In case anyone else is interested, I found that you can
> solve this problem with the eeg_epoch2continuous function in the
> following way:
>
> % epoched data set 1 = EEG1, epoched data set 2 = EEG2, epoched data
> set 3 = EEG3
> % convert epoched data to continuous in order to merge for ICA
> CEEG1 = eeg_epoch2continuous(EEG1);
> CEEG2 = eeg_epoch2continuous(EEG2);
> CEEG3 = eeg_epoch2continuous(EEG3);
> MERGED_SET = pop_mergeset(CEEG1, CEEG2);
> MERGED_SET = pop_mergeset(MERGED_SET, CEEG3); % pop_mergeset only
> works with 2 INEEG arguments
> % run ICA on the merged data
> MERGED_SET = pop_runica(MERGED_SET,'extended',1);
> % copy ICA results into EEG1, EEG2 and EEG3 (see earlier post by
> Chadwick Boulay)
>
> Mikolaj thank you for the link to the 'epoch_linearize' function as
> well. I haven't tried it yet but plan to as it looks very useful!
>
> My last question about this is how to do component rejection on these
> data. When I first tried this I first did component rejection on the
> MERGED_SET, then copied the ICA fields from the merged data set into
> the ICA fields for each epoched data set (EEG1, EEG2, EEG3). However
> I'm not sure if that actually worked, so I think what I might have to
> do is: 1) copy the ICA fields form the MERGED_SET into the epoched
> sets, 2) do component rejection on one of the sets, 3) run the
> component rejection command on all epoched data sets using the
> component numbers identified for rejection. Is that right?
>
> Best wishes and thanks again for the help!
> Becky
>
>
>
> On 7 May 2013 04:18, Arnaud Delorme <arno at ucsd.edu
> <mailto:arno at ucsd.edu>> wrote:
>
> The command line function eeg_epoch2continuous also allows to
> transform EEG epochs into a continuous dataset.
> Best,
>
> Arno
>
> On 5 May 2013, at 16:06, Miko?aj Magnuski wrote:
>
>> It seems I didn't send my last message to eeglablist, it can be
>> found at the end of this one.
>>
>> Anyway, Becky, I have found and uploaded the beforementioned function
>> to
>> https://sites.google.com/site/analizasygnalow/zasoby/nasze-narzedzia
>> the function name is epoch_linearize.m and it is called this way:
>> newEEG = epoch_linearize(EEG);
>>
>> it does not retain events, but you don't need these for ICA.
>> For linearizing multiple files you can use linearize_all_eeg
>> (also on the page) like this:
>> ALLEEG = linearize_all_eeg(files, folder);
>> 'files' is assumed to be a cell matrix of file names
>> (for example: {'subj01.set', 'subj02.set'}), while 'folder'
>> a string defining filepath (like: 'C:\DATA\Exp1_2012\').
>>
>> You would still have to save these files and later
>> join relevant ones. I had a script for this too
>> where you could specify with regular expressions
>> what files should be linearized and then concatenated,
>> but I didn't transform it into a more universal function,
>> I'll try to do this soon if you would find it useful.
>>
>> If you have any troubles using these functions -
>> let me know!
>>
>>
>> You can also transform all your subject subfiles (epochs of
>> different length) into continuous signals (instead of epochs
>> you can place 'boundary' events, although it is not necessary
>> for ICA) - such that you have one file per subject. Then you
>> would perform ICA and copy ica weights to your original
>> epoched files. I think there is a function in EEGlab to
>> 'de-epoch' the data (Arno, Makoto, am I correct?). Anyway - I
>> wrote such function some time ago, so I'll see if I can still
>> find it. The function did not retain events from the original
>> dataset, but you do not need this for ICA.
>> (there is yet another option - you could epoch the data into
>> small chunks of 1-s long or so, reject epochs containing
>> artifacts or signal of no interest, then perform ICA and copy
>> ica weights to your normally epoched data. I too have a
>> function for this, which allows for several useful options
>> like automatic rejection of windows (these small epochs) that
>> are too far from cerain events or event sequences - so that
>> you wouldn't have to manually click and reject windows
>> ocurring in breaks or during a task you are not interested
>> in. Unfortunatelly the function help is still in polish - but
>> the basic options are easy to explain in one short mail -
>> that is if you think you will find such function useful)
>>
>> 30 kwi 2013 19:24, "Becky Prince" <becky.prince at york.ac.uk
>> <mailto:becky.prince at york.ac.uk>> napisa?(a):
>>
>> Dear EEGLAB users,
>>
>> I have a question about the best artifact detection and
>> ICA procedures for data sets with multiple epoch types of
>> different lengths.
>>
>> Each trial in my EEG task is made up of 3 non-overlapping
>> epochs of interest: 1) 4 seconds, 2) either 1.5 or 2.5
>> seconds, 3) 2.5 seconds. For my previous ERP experiments
>> I've used the following data processing procedure: epoch
>> the data, reject bad epochs, run ICA on remaining epochs,
>> reject ICA components, analyze data in channel space.
>> For my current experiment (with 3 epochs per trial), I
>> haven't been able to run ICA on all of the epoched data
>> at once because, as soon as I epoch the data, it is split
>> into a separate data set. I'm fairly sure that these 3
>> separate epoched data sets cannot be merged/appended for
>> a single ICA run because the epochs are of differing lengths.
>>
>> I've come up with 2 different protocols for processing
>> the individual data sets. I'm not sure which is better
>> or if I'm overlooking other options.
>>
>> Solution 1: artifact rejection and ICA on continuous data
>> 1) reject artifacts in continuous data
>> 2) run ICA on continuous data
>> 3) reject ICA components
>> 4) epoch the data (creates 3 separate data sets)
>> 5) analyze data in channel space
>>
>> Solution 2: artifact rejection and ICA on epoched data
>> 1) epoch the data (creates 3 separate data sets)
>> Then for each of the 3 epoched data sets:
>> 2) reject epochs
>> 3) run ICA on remaining epochs
>> 4) reject ICA components
>> 5) analyze data in channel space
>>
>> My problem with solution 1 is that it is much more time
>> consuming to detect the artifactual sections of
>> continuous data than it is to detect and reject bad
>> epochs. Also, ICA seems to run more slowly in this case
>> (even after taking into account the differences in the
>> size of the continuous and epoched data sets). My
>> problem with solution 2 is that it doesn't seem right to
>> run ICA separately for each epoched data set for the same
>> subject/session. Because I am doing within-subject
>> comparisons among the 3 trial segments, I'm concerned
>> about producing artificial differences among these epochs
>> as a result of differences in the ICA decomposition and
>> component rejection.
>>
>> Sorry for the long question. Can anyone tell me what
>> they would recommend? Any suggestions and/or criticisms
>> of my current processing steps would be very helpful!
>>
>> Many thanks,
>> Becky
>> PhD Candidate
>> Department of Psychology
>> University of York
>>
>>
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