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<p>Dear Andreas,</p>
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<p class="MsoNormal">thanks a lot for your reply! It would be great if you could have a look; of course I can provide a dataset and the relevant pre-processing code. Maybe any of the specifications are not ideal, which you would probably see right away when
 looking at the script.</p>
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<p class="MsoNormal">There shouldn’t be any discontinuities without boundary markers unless I missed something. I am filtering the continuous data (before rejecting any segments) in case that’s somehow relevant.</p>
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<p class="MsoNormal">Indeed, I sometimes used a different Matlab version (2015b vs. 2014a) and already wondered if this might be somehow related. Do think that could be the source of the problem? I believe I re-ran the scripts for the affected subjects on one
 computer (Matlab 2015b) but I can’t tell for sure. Would you suggest re-running it again and check if it changes anything before making the data available?</p>
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Best,<br>
Hannah
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<div class="PlainText">Hannah Hiebel, Mag.rer.nat.<br>
Cognitive Psychology &amp; Neuroscience</div>
<div class="PlainText">Department of Psychology, University of Graz</div>
<div class="PlainText">Universitätsplatz 2, 8010 Graz, Austria<br>
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<div id="divRplyFwdMsg" dir="ltr"><font style="font-size:11pt" color="#000000" face="Calibri, sans-serif"><b>Von:</b> Andreas Widmann &lt;widmann@uni-leipzig.de&gt;<br>
<b>Gesendet:</b> Donnerstag, 19. Jänner 2017 21:53<br>
<b>An:</b> Hiebel, Hannah (hannah.hiebel@uni-graz.at)<br>
<b>Cc:</b> eeglablist@sccn.ucsd.edu<br>
<b>Betreff:</b> Re: [Eeglablist] ICA running very slowly</font>
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<div>Hi Hannah,
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<div class="">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?</div>
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<div class="">Best,</div>
<div class="">Andreas</div>
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<div class="">Am 19.01.2017 um 09:41 schrieb Hiebel, Hannah (<a href="mailto:hannah.hiebel@uni-graz.at" class="">hannah.hiebel@uni-graz.at</a>) &lt;<a href="mailto:hannah.hiebel@uni-graz.at" class="">hannah.hiebel@uni-graz.at</a>&gt;:</div>
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<span class="">Dear Alberto and Tarik,</span></div>
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<span class="">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.</span></div>
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<span class="">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 &gt;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.</span></div>
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<span class="">&nbsp;</span></p>
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<span class="">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 (&lt; 1h for the subjects where it took &gt; 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 &gt;20h in the “problematic” cases.<span class="Apple-converted-space">&nbsp;</span></span></div>
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<span class="">This gives me the impression that the higher cut-off frequency causes the problems (or maybe stopband edge and attenuation are more decisive?).</span></div>
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<span class="">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.</span></div>
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<span class="">I’d be very grateful if anyone could provide more insight!</span></div>
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<span class="">Best,<br class="">
Hannah</span><span class=""></span></div>
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<div class="PlainText">Hannah Hiebel, Mag.rer.nat.<br class="">
Cognitive Psychology &amp; Neuroscience</div>
<div class="PlainText">Department of Psychology, University of Graz</div>
<div class="PlainText">Universitätsplatz 2, 8010 Graz, Austria<br class="">
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<div id="divRplyFwdMsg" dir="ltr" class=""><font class="" style="font-size:11pt" face="Calibri, sans-serif"><b class="">Von:</b><span class="Apple-converted-space">&nbsp;</span>Alberto Sainz &lt;<a href="mailto:albertosainzc@gmail.com" class="">albertosainzc@gmail.com</a>&gt;<br class="">
<b class="">Gesendet:</b><span class="Apple-converted-space">&nbsp;</span>Mittwoch, 18. Jänner 2017 04:29<br class="">
<b class="">An:</b><span class="Apple-converted-space">&nbsp;</span>Hiebel, Hannah (<a href="mailto:hannah.hiebel@uni-graz.at" class="">hannah.hiebel@uni-graz.at</a>)<br class="">
<b class="">Cc:</b><span class="Apple-converted-space">&nbsp;</span><a href="mailto:eeglablist@sccn.ucsd.edu" class="">eeglablist@sccn.ucsd.edu</a><br class="">
<b class="">Betreff:</b><span class="Apple-converted-space">&nbsp;</span>Re: [Eeglablist] ICA running very slowly</font>
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<div dir="ltr" class="">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 class="">
<br class="">
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 class="">
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Good luck</div>
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<div class="gmail_quote">2017-01-16 20:26 GMT&#43;01:00 Hiebel, Hannah (<a href="mailto:hannah.hiebel@uni-graz.at" class="">hannah.hiebel@uni-graz.at</a>)<span dir="ltr" class="">&lt;<a href="mailto:hannah.hiebel@uni-graz.at" target="_blank" class="">hannah.hiebel@uni-graz.at</a>&gt;</span>:<br class="">
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<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; font-family:Verdana,sans-serif">Dear all,<br class="">
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<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; 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 class="">
</span></div>
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<span class="" style="font-size:9pt; font-family:Verdana,sans-serif"></span></div>
<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; 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></div>
<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; 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 class="">
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<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; 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<span class="Apple-converted-space">&nbsp;</span><em class=""><span class="" style="font-family:Verdana,sans-serif">rank(EEG.data(:, :))<span class="Apple-converted-space">&nbsp;</span></span></em>it
 returns 62 though, which is equal to the number of&nbsp; channels. For some of the “bad” subjects I nonehteless tried without re-referencing – no improvement. Also, reducing dimensionality with pca (&quot;pca, 61&quot;) didn’t help.<br class="">
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<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; font-family:Verdana,sans-serif">Any advice would be very much appreciated!<br class="">
</span></div>
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<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; font-family:Verdana,sans-serif">Many thanks in advance,</span></div>
<div class="" style="margin-top:0px; margin-bottom:0px"><span class="" style="font-size:9pt; font-family:Verdana,sans-serif">Hannah</span></div>
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<div class="m_-9147620952188609437PlainText">Hannah Hiebel, Mag.rer.nat.<br class="">
Cognitive Psychology &amp; 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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