<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><head><meta http-equiv=Content-Type content="text/html; charset=us-ascii"><meta name=Generator content="Microsoft Word 14 (filtered medium)"><style><!--
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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><div><div><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>Hi Jia,<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>There is some ambiguity in the case of non-stationary environments. Generally for statistical estimation, more data means better estimates (of components, activations, etc.), so you want as much data as possible. However, if the data is non-stationary, then you have different data points generated by different statistical systems, and combining the data will generally degrade the estimation of either system. So you really want as much data as possible <i>generated from the statistical system of interest</i>.<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>EEG data is usually preprocessed with a high-pass filter (remove mean and low frequency drift) to increase the stationarity. We are essentially trying to remove unimportant sources of non-stationarity to sample on from a supposedly long-term stationary system of brain and other biological sources.<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>Another issue is the number of sources that are present. Basic ICA assumes that the number of sources is less than the number of sensors/channels. So if you record from longer periods of time, you are likely to have more “artifactual”, transient type sources show up, which will force ICA to compromise the independence of the estimated components.<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>If we assume that the same components are present at different times, and that we have filtered and removed artifacts sufficiently to make the data consist of fewer sources than sensors, then generally the more data the better.<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>We might also assume that the same components are present in different subjects. Again it will be important to try to preprocess out unimportant differences (sources of nonstationarity) and try to be sure that the number of sources is less than the number of data dimensions used.<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>Hope that’s helpful.<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'>Best,<br>Jason<o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='color:black'><o:p> </o:p></span></p><p class=MsoNormal style='margin-bottom:12.0pt'>---------- Forwarded message ----------<br>From: <b>jia gu</b> <<a href="mailto:jia.gu12345@gmail.com">jia.gu12345@gmail.com</a>><br>Date: Mon, Jun 6, 2011 at 9:50 PM<br>Subject: quick EEGLAB ICA question<br>To: <a href="mailto:eeglab@sccn.ucsd.edu">eeglab@sccn.ucsd.edu</a><br><br><br>To whom it might concern:<br><br>Thank you very much for providing us the EEGLAB! :)<br>I am trying to use ICA to clean some EEG signals, I read that the min<br># of sample points should be at least 25x channel number squared. But<br>there is no upper limit. I wonder does the performance of ICA<br>(infomax) get better with more training points? or does it start to<br>degrade after a certain optimal number of sample points, and if so<br>what is the best # of sample points?<br>thank you very much for your time and help<br>cheers<br><span style='color:#888888'>jia</span><o:p></o:p></p></div><p class=MsoNormal><br><br clear=all><br>-- <br>Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation & Adj. Prof. of Neurosciences, University of California San Diego, La Jolla CA 92093-0559, <a href="http://sccn.ucsd.edu/~scott" target="_blank">http://sccn.ucsd.edu/~scott</a><o:p></o:p></p></div></div></body></html>