[Eeglablist] Bayesian approach to ICA?

[Scott Makeig]

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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