[Eeglablist] Questions on ICA/DIPFIT implementation

[Helen L. Wieffering]

Hi everyone,

I am a relatively new user of EEGlab and have a few questions regarding ICA and DIPFIT. I would greatly appreciate any available help -- thanks in advance.

First, our data was taken as subjects completed two separate tasks. In the interest of making detailed comparisons, we have sorted the data into 16 separate files according to condition. However, some of these conditions contain overlap among trials: e.g. Correct_Task1, Correct_Task_2 will overlap with Correct_All_Tasks. Is it fine to feed all 16 conditions into ICA? Or is it more advisable to use only mutually exclusive conditions?

Second, I've had significant trouble saving the datasets after computing ICA. Here's the code we used:

        % concatenate each subject's condition datasets and run ICA
        [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, CURRENTSET, 'retrieve', cleandata, 'study', 0);
        EEG = eeg_checkset(EEG);
        EEG = pop_runica(EEG, 'icatype', 'runica', 'concatcond', 'on', 'options', {'extended', 1});
        [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, CURRENTSET, 'savenew', ICAfile, 'gui', 'off');
        EEG = eeg_checkset(EEG);

After which, Matlab outputs a message reading:
"Your memory options for saving datasets does not correspond to the format of the datasets on disk (ignoring memory options). Saving to matlab.mat"

This seems to occur consistently, no matter which memory options I choose. How can I make sure that ICA components are saved to the corresponding data file?
So far, the only solution has been to build a study out of all 16 files and then run ICA on the study ... however, this seems to apply identical ICA components to each file, which doesn't seem right either. Any ideas on what the problem might be?

Third, our EEG data consists of 128 channels. We are considering reducing that number so that we don't end up with 128 ICs --- but is there any consensus on an optimal number of channels/components to aim for? Any recommended reduction method?

And finally, at what point is it best to run DIPFIT? All the files consist of trials from the same subject and session - so then is it redundant to compute DIPFIT separately for each condition?

Again, thanks for any and all help. The ultimate goal is to run our data through connectivity analysis in SIFT, but of course we first need to iron out these details in the pre-processing stage.

Best,

Helen Wieffering
Bowdoin College
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