[Eeglablist] isolating a CNV with ICA

Keith Yoder kjyoder at gmail.com
Fri Sep 11 07:31:18 PDT 2009


Thanks for all of the advice!  We're trying several methods (lowpass
filtering the data, running ICA on an averaged dataset, etc.).  Some of our
problem seems to be coming from a lot of 'high'-frequency (i.e. >5hz)
"noise" in our data (almost certainly because the latency between S1 and S2
is a constant 3 seconds).  I'll post back to this list if we make any
headway.
Thanks again,
Keith

On Fri, Sep 11, 2009 at 3:56 AM, Ronald Phlypo <ronald.phlypo at ugent.be>wrote:

>  Dear Keith,
>
>
> 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.
>
> Some references:
>
>
>    - *SOBI:* 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
>    - *TDSEP: *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
>    - *AMUSE: *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
>    - *but also* L. Molgedey and Schuster, H.G., 'Separation of a Mixture
>    of Independent Signals using Time Delayed Correlations', Physical Review
>    Letters, pp3634-3637, (72), 1994
>
>
> 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.
>
>
> Good luck!
>
>
> Ronald
>
>
> Joseph Dien a écrit :
>
> 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:
>
>
>
>  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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>  --------------------------------------------------------------------------------
>
> Joseph Dien,
> Senior Research Scientist
> Center for Advanced Study of Language
> University of Maryland
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>
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