[Eeglablist] ICA running very slowly

Hiebel, Hannah (hannah.hiebel@uni-graz.at) hannah.hiebel at uni-graz.at
Mon Jan 16 11:26:51 PST 2017

Dear all,

I am using ICA to clean my EEG data for eye-movement related artifacts. I've already done some testing in the past to see how certain pre-processing steps affect the quality of my decomposition (e.g. filter settings). In most cases, it took approximately 1-2 hours to run ICA for single subjects (62 channels: 59 EEG, 3 EOG channels).

Now that I run ICA on my final datasets it suddenly takes hours over hours to do only a few steps. It still works fine in some subjects but in others runica takes up to 50 hours. I observed that in some cases the weights blow up (learning rate is lowered many times); in others it starts right away without lowering the learning rate but every step takes ages.

I've done some troubleshooting to see if a specific pre-processing step causes this behavior but I cannot find a consistent pattern. It seems to me though that (at least in some cases) the high-pass filter played a role - can anyone explain how this is related? Could a high-pass filter potentially be too strict?

On the eeglablist I could only find discussions about rank deficiency (mostly due to using average reference) as a potential reason. I re-referenced to linked mastoids - does this also affect the rank? When I check with rank(EEG.data(:, :)) it returns 62 though, which is equal to the number of  channels. For some of the "bad" subjects I nonehteless tried without re-referencing - no improvement. Also, reducing dimensionality with pca ("pca, 61") didn't help.

Any advice would be very much appreciated!

Many thanks in advance,


Hannah Hiebel, Mag.rer.nat.
Cognitive Psychology & Neuroscience
Department of Psychology, University of Graz
Universitätsplatz 2, 8010 Graz, Austria
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