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

[Makoto Miyakoshi]

Dear Melinna,

Very interesting point. I was also surprised that my wiki page did not
explain it. I added an explanation to it. I believe it answers your
questions. If not let me know.
https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#Rejecting_ICs_to_improve_the_next_ICA.3F_Or_what_is_rank_and_linear_algebra_.2807.2F16.2F2018_updated.29

Makoto

On Sun, Jul 15, 2018 at 9:10 PM 梅晓林 <melinna.sysu at gmail.com> wrote:

> 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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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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