<div dir="ltr">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.<br><br>I know your data is larger but just to say that the processor (and probably the RAM if is too small) matters a lot.<br><br>Good luck</div><div class="gmail_extra"><br><div class="gmail_quote">2017-01-16 20:26 GMT+01:00 Hiebel, Hannah (<a href="mailto:hannah.hiebel@uni-graz.at">hannah.hiebel@uni-graz.at</a>) <span dir="ltr"><<a href="mailto:hannah.hiebel@uni-graz.at" target="_blank">hannah.hiebel@uni-graz.at</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">Dear all,<br>
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<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">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).<br>
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<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">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.</span></p>
<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">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?<br>
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<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">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 <em><span style="font-family:"Verdana",sans-serif">rank(EEG.data(:, :))
</span></em>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.<br>
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<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">Any advice would be very much appreciated!<br>
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<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">Many thanks in advance,</span></p>
<p><span style="font-size:9.0pt;font-family:"Verdana",sans-serif">Hannah</span></p>
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<div class="m_-9147620952188609437PlainText">Hannah Hiebel, Mag.rer.nat.<br>
Cognitive Psychology & Neuroscience</div>
<div class="m_-9147620952188609437PlainText">Department of Psychology, University of Graz</div>
<div class="m_-9147620952188609437PlainText">Universitätsplatz 2, 8010 Graz, Austria </div>
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