[Eeglablist] Bayesian approach to ICA?

[Bachman, Peter]

Excellent - thank you, Scott!
 
Peter

________________________________

From: eeglablist-bounces at sccn.ucsd.edu on behalf of Scott Makeig
Sent: Fri 8/7/2009 3:30 PM
To: Bachman, Peter
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Bayesian approach to ICA?


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 http://sccn.ucsd.edu/~jason

Scott


On Thu, Aug 6, 2009 at 4:00 PM, Bachman, Peter <bachman at psych.ucla.edu> wrote:


	Hi everyone,
	 
	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.
	 
	At the moment, I'm wondering whether anyone has every attempted to implement a Bayesian approach to ICA within EEGLAB specifically.  
	 
	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).  
	 
	Thanks in advance for your input!
	Peter
	 
	 
	 
	Peter Bachman, PhD
	Semel Institute
	UCLA Department of Psychiatry & Biobehavioral Sciences
	bachman at psych.ucla.edu

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-- 
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, http://sccn.ucsd.edu/~scott

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