[Eeglablist] ICA running very slowly

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Tue Jan 24 16:49:06 PST 2017

Dear Hannah,

Sorry for the trouble. Hmm that's a mysterious behavior.
In the case of informax, it does change the processing speed depending on
your data quality. So I would recommend you check your data with
trimOutlier() plugin to see if there is any outlier in multiple senses.

If you are sure it is not becaues of data quality, we would want to receive
the data in question to replicate the problem, if it does not matter to
you. Thank you for your cooperation and patience.


On Mon, Jan 16, 2017 at 11:26 AM, Hiebel, Hannah (hannah.hiebel at uni-graz.at)
<hannah.hiebel at uni-graz.at> wrote:

> 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
> Hannah Hiebel, Mag.rer.nat.
> Cognitive Psychology & Neuroscience
> Department of Psychology, University of Graz
> Universitätsplatz 2, 8010 Graz, Austria
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Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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