[Eeglablist] re referencing to average after ICA

[Tarik S Bel-Bahar]

Thanks for your question Raquel, and great idea to get a better idea of the
mechanics and assumptions. Hands-on playing with different options and
rerunning ICAs can be quite useful. What I meant is that usually
researchers, as far as I understand, usually apply the ICA decomposition to
files (from processing steps before ICA) that are very similar to the files
that went "into" ICA. But there are various ways the files could be the
"same"....

In other words, the usual thing is to apply the ICA weights to a file that
has at least
***A. the *same* channels as the file(s) that went into ICA
***B. the *same* subject and same recording session as the file(s) that
went into ICA
I think this is due the basic expectations of ICA and the data structures
in eeglab.  Also, each person/session of course has their own decomposition.


FURTHER
***C. Usually,the file which is getting the ICA weights will be the *same *as
the file(s) that went into the ICA (e.g, in terms of filtering or
bandpassing or re-referencing). Relatedly, I don't believe it's appropriate
to apply ICA weights from 1hz-highpass files to unfiltered files, but I
might be wrong.


HOWEVER
***D. The file which is getting the ICA weights does not need to have the *same
*exact time points as the file(s) that went into the ICA, and it can be
epoched or continuous. As long as it has the correct features matching the
file that went into ICA. So it's in terms of time points to apply the ICA
weights to that you have the most freedom, relative to other feature of the
data.
Thus.... one can re-apply to the continuous or near-continous epoched data,
and then do artifact rejection with ICA info, re-do epoching after doing
ICA cleaning, and other similar strategies. Some of the strategies are
laidout in the eeglab tutorials and articles.


ps. when things are setup wrong with ICA, the solutions will look weird,
the eeg data will look weird, and/or eeglab will break. If you get a chance
to, please consider sharing some examples of your next tests, or some
summary of your understanding that could benefit new users later on.
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