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Dear Keith,<br>
<br>
<br>
If it concerns slowly evolving potentials, or potentials with a slowly
decreasing autocorrelation function, second-order (statistics) methods
based on non-proportional autocorrelation functions might be more
appropriate than the non-gaussian (higher order statistics) versions of
ICA. <br>
<br>
Some references:<br>
<br>
<ul>
<li><b>SOBI:</b> Adel Belouchrani and Karim Abed-meraim and J.F.
Cardoso and E. Moulines, 'A Blind Source Separation Technique Using
Second Order Statistics', IEEE Trans on Signal Processing, pp434--444
(45), 1997</li>
<li><b>TDSEP: </b>A. Ziehe & K-R Müller, 'TDSEP - an efficient
algorithm for blind separation using time structure', Proc. of the 8th
Int'l Conf on Artificial Neural Networks, pp675-680, 1998</li>
<li><b>AMUSE: </b>Lang Tong and Ruey-Wen Liu and Soon, Victor C. and
Yih-Fang Huang, 'Indeterminacy and Identifiability of Blind
Identification', IEEE Trans. on Circuits and Systems, pp499-509 (38)
1991</li>
<li><b>but also</b> L. Molgedey and Schuster, H.G., 'Separation of a
Mixture of Independent Signals using Time Delayed Correlations',
Physical Review Letters, pp3634-3637, (72), 1994</li>
</ul>
<br>
SOBI is included in the EEGLab package in an implementation of
Belouchrani and Cichocki [sobi.m] and might be worth the try either for
separating the signal of interest, either for a non-parametric slow
varying potential wave subtraction as a preprocessing step.<br>
<br>
<br>
Good luck!<br>
<br>
<br>
Ronald<br>
<br>
<br>
Joseph Dien a écrit :
<blockquote cite="mid:91508648-2B83-48CA-A02F-BE107A996096@mac.com"
type="cite">
<pre wrap="">You might try doing a PCA in the temporal domain instead of an ICA in
the spatial domain, if the time course is sufficiently consistent.
Cheers!
Joe
On Sep 8, 2009, at 1:01 PM, Keith Yoder wrote:
</pre>
<blockquote type="cite">
<pre wrap="">Hi all,
Our group has EEG time-series data collected with a BioSemi
ActiveTwo 128-electrode cap. Our paradigm includes a Go-NoGo, with
an alerting stimulus (S1) preceding the presentation of the Go-NoGo
stimulus (S2). S1 and S2 are separated by 3 seconds. When we
examine ERPs from single electrodes, we observe a typical contingent
negative variation (CNV). However, when we run ICA (runica), none
of the components isolate the CNV. We've tried highpass filtering
at 0.5hz (obviously to high), 0.01hz and without any highpass
filtering, all without success. Has anyone else run into a similar
problem? Does anyone have any additional advice for isolating a CNV
with ICA?
Thanks,
Keith Yoder
--
Research Assistant
Belmonte Autism Lab
Cornell University
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<pre wrap=""><!---->
--------------------------------------------------------------------------------
Joseph Dien,
Senior Research Scientist
Center for Advanced Study of Language
University of Maryland
7005 52nd Avenue
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Phone: 301-226-8848
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