[Eeglablist] Problems in run ICA and re-run ICA

[梅晓林]

Dear all,

There are prolbems of running speed that I have met when I am doing ICA
analysis, and I really need your help.

The data I have is in 66 channels (64 EEG channels and 2 EOG channels). The
preprocess procedure here only involves filtering (high pass, 0.1HZ),
rejecting bad epochs manually, and interpolating bad channels (method,
'sphereral').

I found that:
1, if the bad channels have been removed and interpolated, then the
following ICA on this data is rather time costing, it would take 1~2 hours
to start the learning rate, and 7 hours later, it only went 50 steps.

However, if I choose reference the data before ICA or doing ICA without
those interpolated channels, the speed of running ICA become normal, and
the data of one subject can be processed in one hour.

2, if I have removed one component (usually the eye blick), after that,
when I am trying to re-run ICA, the probelm comes again: the starting
learning rate is low, and it takes ages to run the data.

The weired thing is that the problem happened here exists in most of the
subjects (30of40), but for others, it is okay to re-run without any change.

I do have tried your other suggestions that set the 'icatype' to 'pca' in
the second run if some components have been removed, or define the 'ncomps'
as the number of the decreased dimension. The former change ('pca') can
re-speed the ICA but the latter won't (define ncomps).

I become more and more confused. As the problem mentioned above, my
question here is:

1, Can I run ICA if some channels are correlated to others? if there are
correlated channels, the ICA running seems doesn't work.

2, After removing one component in the first run, is it possible to ru-run
ICA without any additional parameter setting? I can understand that the
number of  data demension has been reduced after substracting components,
but I don't understand why it can work in some subjects and not work for
all.

Your reply is really of great value for this work. Looking forward to it.
Thanks a lot.

Best,

Melinna
Department of Psychology
Sun-Yat Sen University
China
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