[Eeglablist] Clustering ICs across sessions but within subjects

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Thu Aug 10 11:07:49 PDT 2017

Hello Ghislain, a quick followup here:

Here below is the eegh output for a series of steps I used to get an
initial ICA cluster for the single-subject's four files you sent.
I loaded only the  4 .set files,
then in the Study info gui gave them a unique ID, named them all as the
same session, condition and group...
and then ran precomputation for IC info (just spectra and scalp), then IC
preclustering with defaults for just spectra and scalp info,
and then clustered with defaults, and it seemed to work. You can give it a
try with the code below if you point it to the correct folder for loading
the files. Alternatively you should be able replicate the steps on your own.
Just first these steps first to force eeglab to cluster the ICs from the
four files, and then go from there, changing things.

Although your have sparse-EEG(14 electrodes) and it seems like the
electrodes are ot necessarily fully correct in terms of their locations
(they seem all in midline of the head rather than spread out - you can fix
this in Edit channel locations, to spread them out correctly),
I do see what seems like an eyeblink IC, a lateral eye movement IC, and
perhaps a muscle IC or two.

*****eegh output  below for seemingly successful single-subject,
multi-condition, IC clustering. Note that you may compute more IC measures,
do pre-clustering with different settings, and do the IC clustering with
different settings. Note that I recommend at this stage doing clear visual
checks of IC component properties from the different files, as a sanity
check and to familiarize yourself more.

EEG = pop_loadset('filename',{'raw_I_p102_ME_left.set'
'raw_I_p102_ME_right.set' 'raw_I_p102_MI_left.set'
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 0,'study',0);
[STUDY ALLEEG] = std_editset( STUDY, ALLEEG,
'name','xxx','task','xxx','notes','xxx','commands',{{'index' 1 'subject'
'103' 'subject' '1'} {'index' 2 'subject' '2'} {'index' 3 'subject' '3'}
{'index' 4 'subject' '4'} {'index' 1 'session' 1} {'index' 2 'session' 1
'condition' 'left'} {'index' 3 'condition' 'left'} {'index' 4 'condition'
'left'} {'index' 1 'group' '1'} {'index' 2 'group' '1'} {'index' 3 'group'
'1'} {'index' 4 'group'
'1'}},'updatedat','on','savedat','on','rmclust','on' );
[STUDY ALLEEG] = std_checkset(STUDY, ALLEEG);
'fft' 'logtrials' 'off'});
[STUDY ALLEEG] = std_preclust(STUDY, ALLEEG, 1,{'spec' 'npca' 10 'norm' 1
'weight' 1 'freqrange' [3 25] },{'scalp' 'npca' 10 'norm' 1 'weight' 1
'abso' 1});
[STUDY] = pop_clust(STUDY, ALLEEG, 'algorithm','kmeans','clus_num',  14 );
STUDY = std_topoplot(STUDY,ALLEEG,'clusters',[2   3   4   5   6   7   8   9
 10  11  12  13  14  15]);
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