[Eeglablist] How to correctly break down AR runica() in case of huge sets.

[Mahesh Casiraghi]

Dear more experienced EEGLabbers and ICA experts,


supposing one has to work with quite large datsets (several channels, very
high sample rate, long record lengths) and would therefore be unable to load
in memory several gigs of data altogether:

A) Is it methodologically problematic to run independent ICAs on subgroups
of trials and then separately perform AR (blinks and scalp detected ECG
components rejection) on each of them?

B) Assuming it would not be, as I tend indeed to think, a so recommendable
way, is there a methodologically proof way to combine all the obtained - and
presumably heterogeneous - sphere, weights and weights(-1) matrices in 3
single Sph, W, and W(-1) matrices and then use these new to backproject
after component rejection?

C) More precisely, let's suppose we have 700 trials and we run 7 independent
ICAs each time on 100 of them.

a) I would proceed in picking-up separately (subjective criteria, adjust,
faster or whatever one may prefer) the to-be-rejected components,
independently from each subgroup of trials.
b) I would then remove subgroup by subgroup the respective w(-1) columns and
EEG.icaact rows according to the discarded components.
c) I would merge the obtained 7 EEG.icasphere, the 7 EEG.icaweights, and the
7 EEG.icawinv, in 3 single matrices of equal dimensions, averaging through
nanmean (given the fact we are likely to pick up a different amount of
components from each of the trial subgroups and we would need consistent
matrix dimensions).
d) I would finally independently backproject subgroup by subgroup using the
same averaged EEG.icawinv and EEG.icasphere and each time the EEG.icaact of
the current subgroup of trials.

According to my first speculations, following a->b->c->d we should come up
with something analogous to the output of a big global ICA.

Am I wrong?

D) Did someone among you already try to run something like that and is
perhaps willing to provide some feedbacks-impressions?


Cheers,

Mahesh




Mahesh M. Casiraghi
PhD candidate - Cognitive Sciences
Roberto Dell'Acqua Lab, University of Padova
Pierre Jolicoeur Lab, Univesité de Montréal
mahesh.casiraghi at umontreal.ca

I have the conviction that when Physiology will be far enough advanced, the
poet, the philosopher, and the physiologist will all understand each other.
Claude Bernard
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