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<DIV dir=ltr><FONT color=#000000 size=2 face=Arial>Excellent - thank you, Scott!</FONT></DIV>
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<DIV dir=ltr><FONT size=2 face=Arial>Peter</FONT></DIV></DIV>
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<FONT size=2 face=Tahoma><B>From:</B> eeglablist-bounces@sccn.ucsd.edu on behalf of Scott Makeig<BR><B>Sent:</B> Fri 8/7/2009 3:30 PM<BR><B>To:</B> Bachman, Peter<BR><B>Cc:</B> eeglablist@sccn.ucsd.edu<BR><B>Subject:</B> Re: [Eeglablist] Bayesian approach to ICA?<BR></FONT><BR></DIV>
<DIV>Jason Palmer's AMICA adapts to both the spatial projections and the pdf's of the sources. Our tests find it to be the best algorithm for high-density EEG analysis by measures we hope to publish soon. See <A href="http://sccn.ucsd.edu/~jason">http://sccn.ucsd.edu/~jason</A><BR><BR>Scott<BR><BR>
<DIV class=gmail_quote>On Thu, Aug 6, 2009 at 4:00 PM, Bachman, Peter <SPAN dir=ltr><<A href="mailto:bachman@psych.ucla.edu">bachman@psych.ucla.edu</A>></SPAN> wrote:<BR>
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<DIV><FONT color=#000000 size=2 face=Arial>Hi everyone,</FONT></DIV>
<DIV><FONT size=2 face=Arial></FONT> </DIV>
<DIV><FONT size=2 face=Arial>First, I'd like to express my appreciation for the help I've received lately from members of this listserv. I've posted a number of questions recently, and have received very helpful answers in all cases.</FONT></DIV>
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<DIV><FONT size=2 face=Arial>At the moment, I'm wondering whether anyone has every attempted to implement a Bayesian approach to ICA within EEGLAB specifically. </FONT></DIV>
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<DIV><FONT size=2 face=Arial>I know there are several ICA algorithms written in to EEGLAB, but as far as I can tell, none of them allow for any kind of modelling of priors. Of course, one might argue that integrating prior information undermines 'blind' source separation, but it appears that there is precedent for taking a Bayesian approach to ICA within the digital signal processing field (e.g., Winther & Petersen, 2007, Digital Signal Processing). </FONT></DIV>
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<DIV><FONT size=2 face=Arial>Thanks in advance for your input!</FONT></DIV>
<DIV><FONT size=2 face=Arial>Peter</FONT></DIV>
<DIV><FONT size=2 face=Arial></FONT> </DIV>
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<DIV><FONT color=#000000 size=1 face=Arial>Peter Bachman, PhD</FONT></DIV>
<DIV><FONT size=1 face=Arial>Semel Institute</FONT></DIV>
<DIV><FONT size=1 face=Arial>UCLA Department of Psychiatry & Biobehavioral Sciences</FONT></DIV>
<DIV><FONT size=1 face=Arial><A href="mailto:bachman@psych.ucla.edu" target=_blank>bachman@psych.ucla.edu</A></FONT></DIV></DIV></DIV><BR>_______________________________________________<BR>Eeglablist page: <A href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target=_blank>http://sccn.ucsd.edu/eeglab/eeglabmail.html</A><BR>To unsubscribe, send an empty email to <A href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu">eeglablist-unsubscribe@sccn.ucsd.edu</A><BR>For digest mode, send an email with the subject "set digest mime" to <A href="mailto:eeglablist-request@sccn.ucsd.edu">eeglablist-request@sccn.ucsd.edu</A><BR></BLOCKQUOTE></DIV><BR><BR clear=all><BR>-- <BR>Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla CA 92093-0961, <A href="http://sccn.ucsd.edu/~scott">http://sccn.ucsd.edu/~scott</A><BR></DIV></BODY></HTML>