[Eeglablist] ICA running very slowly

[Andreas Widmann]

Hi Hannah,

I would like to try to replicate this behavior. Could you please make available one of the affected datasets and the relevant parts of the code used for pre-processing and ICA, e.g. via the bugtracker or Dropbox? Are there possibly data discontinuities without boundary markers? Did you keep MATLAB version constant?

Best,
Andreas

> Am 19.01.2017 um 09:41 schrieb Hiebel, Hannah (hannah.hiebel at uni-graz.at) <hannah.hiebel at uni-graz.at>:
> 
> Dear Alberto and Tarik,
>  
> thank you very much for your suggestions. I work on a computer with i7 3.60 GHz processor, 8 GB RAM or notebook with i7 2.5 GHz and 8GB Ram – this should be okay.
> Gladly, the ICA eventually finds a solution and the IC maps look good. However, the question for me is still why does the ICA become >10 times slower after changing the pre-processing routine. I’ve continued testing and indeed the high-pass filter seems to be responsible for the differences.
>  
> In my recent routine I used the eeglab windowed sinc FIR filter with 1 Hz cut-off frequency, 1 Hz transition bandwidth, 0.001 passband ripple, Kaiser window. When I change the filter (settings) while keeping all other steps the same, I see huge differences in ICA runtime in some subjects. That is, when using a 0.1 Hz Butterworth filter instead, ICA is running fast again (< 1h for the subjects where it took > 30h before). With the eeglab basic FIR filter with 1 Hz passband edge and default settings defined by the internal heuristic (resulting in 0.5 Hz cut-off, 1 Hz trans. bandwidth) it’s also running much faster in most subjects but already takes >20h in the “problematic” cases. 
>  
> This gives me the impression that the higher cut-off frequency causes the problems (or maybe stopband edge and attenuation are more decisive?).
> That's very surprising as I would not have expected the filter to have such an impact and a higher cut-off is normally recommended.
>  
> I’d be very grateful if anyone could provide more insight!
>  
> Best,
> Hannah
> 
> 
> Hannah Hiebel, Mag.rer.nat.
> Cognitive Psychology & Neuroscience
> Department of Psychology, University of Graz
> Universitätsplatz 2, 8010 Graz, Austria
> 
> Von: Alberto Sainz <albertosainzc at gmail.com>
> Gesendet: Mittwoch, 18. Jänner 2017 04:29
> An: Hiebel, Hannah (hannah.hiebel at uni-graz.at)
> Cc: eeglablist at sccn.ucsd.edu
> Betreff: Re: [Eeglablist] ICA running very slowly
>  
> I would suggest to try in a different computer. I have been applying ICA in a 14 electrode 30min continuous EEG recording (around 40mb) in two different computers. 2Ghz dual core computer took 1h. 2.2Ghz i7 takes around 5 minutes.
> 
> I know your data is larger but just to say that the processor (and probably the RAM if is too small) matters a lot.
> 
> Good luck
> 
> 2017-01-16 20:26 GMT+01:00 Hiebel, Hannah (hannah.hiebel at uni-graz.at <mailto:hannah.hiebel at uni-graz.at>)<hannah.hiebel at uni-graz.at <mailto:hannah.hiebel at uni-graz.at>>:
> 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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