[Eeglablist] How does transferring ICA matrixes between same-subject data sets affect further processing?

[Tarik S Bel-Bahar]

Hi Duncan, some quick notes about your questions below, hope they are of
use. Best wishes, Tarik

Mobile brain imaging (EEG in not just "passive and sitting-quietly"
conditions) is still a developing field. You may benefit from contacting a
few researchers who work in this area, including Klaus Gramman and Daniel
Ferris, so as to garner their opinions. There are several other groups that
should be findable via google. As you're at the frontier of this area,
it's better to test a range of options and make the best informed decision,
as there are few consolidated guidelines for this kind of research. There's
a range of issues and options, just a few thoughts below for now.

If you don't give some subset of data to ICA, it's of course not
considering it. If you have several ICA decompositions for one person, then
there should definitely be similarities across the decompositions. Thus,
it's not clear that you would end up with, after subtractions, just, as you
said, "  remaining signal consisting of all the activity that lacked in the
passive conditions?". This expectation makes sense, but it's just not
published about enough to be sure.

And yes, generally, if you are trying to subtract ICs representing neck
muscle activity from a dataset that has very little (or a lot of) activity
like that, the basic assumptions about subtracting the ICs may not hold.

In your case, consider yourself first in an exploratory stage. You are
considering several important points, so that's good. The field needs to
know more about mobile brain imaging problems and how to deal with them.
Overall this is an excellent opportunity to make a contribution to real
problems in the field.

You should be able to run ICA on the non-passive conditions and get
interpretable ICs that reflect real brain sources. However, they will
likely be more mixed with artifact activity than the ICs you get from the
passive condition.

I would recommend running an ICA across all conditions per person, as well
as a (separate) ICA for each condition, and review the results for yourself
(from single-condition and all-condition ICAs). You may get more
"prototypical artifact ICs that are applicable across the conditions. If
you have "weak artifact" IC from one condition and apply it to a "receiving
set"
it should remove some of that artifact activity, but not necessarily most
of it, especially if the IC information from the "donor set" does not
well-characterize the artifact dynamics in the "receiving set".

Another option for future consideration, is to ask people to make a  range
of stereotyped movements with their bodies, faces, and arms, and use that
as a kind of glossary of artifacts for a specific person.

Another option is to compare several conditions, with each condition having
"more artifacts". For example, really slow walking, normal walking, and
fast walking.

Some of your issues like cable sway and so on from movement may be resolved
with wireless caps available from several manufacturers, though I would not
recommend dry systems nor "non-research" consumer systems.

You may also want to think about using ASR in eeglab to clean up the data
beforehand. See an example of the effects of ASR on EEG with active
movement here:
https://www.youtube.com/watch?v=qYC_3SUxE-M
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