[Eeglablist] ICA problem

Scott Makeig smakeig at gmail.com
Mon Feb 1 13:26:32 PST 2010


Joe is correct that ICA will not converge if the rank of the data matrix is
less than the number of channels. The runica/binica algorithms are supposed
to test the rank of the input data. If two channels are identical, or if
some subset of n channels are otherwise interdependent, then the rank will
be less than the number of channels and PCA reduction should be applied to
remove the redundancy and allow the ICA decomposition to converge.

Arno -- There was a problem with the Matlab rank() function on 64-bit
machines, I believe. Has this been solved and Is the auto rank detection ->
PCA option currently implemented in runica/binica?  Perhaps we could add a
'toy' rank() function pre-test (e.g. finding the rank of a small full-rank
matrix to detect if rank() is working...) ? If so, run the rank test; if
not, then warn the user or build a work-around rank function that will work
properly?

Scott

On Mon, Feb 1, 2010 at 6:56 AM, Joseph Dien <jdien07 at mac.com> wrote:

> When you say "so long", how long do you mean?  While ICA is not by its
> nature a fast procedure, certain datasets can take much longer than usual.
>  For example, I find that if two channels are perfectly correlated (1 or -1)
> then an ICA run will take much longer.  This can happen if the data is mean
> mastoid referenced and both channels are explicitly included in the data
> because they will have a perfect -1 correlation (see Dien, 1998 for
> reference issues).  It can also happen if a channel is shorted out during
> acquisition and the reference channel is explicitly included because then
> they will have a perfect correlation.  Also if two channels are shorted
> together during the data acquisition they will be perfectly correlated with
> each other.  My EP Toolkit (
> https://sourceforge.net/projects/erppcatoolkit/) has code for dealing with
> these situations so you might want to look into it.  It implements an
> automated artifact correction routine that relies on EEGlab's runICA code,
> among!
>  other things.
>
> Cheers!
>
> Joe
>
>
>
> On Jan 29, 2010, at 4:49 AM, peng wang wrote:
>
> > Hi there,
> >
> >    I am using ICA to remove blinks via EEGLab. My dataset has 122
> channels, and it takes so long to compute 122 components.
> >    (1) So I tried to use the option "ncomps" (say, 24) to reduce the
> number of components. However, an error message appears after computing:
> "Matrix dimensions must agree".
> >
> >    (2) Then I tried fastICA instead as following,
> >
> > ==================
> >  sz = size(EEG.data);
> >  nchans = sz(1);
> >  npts = sz(2);
> >  ntrials = sz(3);
> >  clear sz;
> >  nICs = 24;
> >  data = reshape(EEG.data,nchans,npts*ntrials);
> >  [ica,V,W] = fastica(data,'numOfIC',nICs,'approach','symm');
> >  EEG.icasphere = eye(nchans);
> >  EEG.icaact = single(reshape(ica,nICs,npts,ntrials));
> >  EEG.icawinv = V;
> >  EEG.icaweights = W;
> >  EEG = eeg_checkset( EEG );
> >  clear V W ica data;
> >
> >  EEG = pop_saveset( EEG, 'filename','test_raw_ica');
> > ==================
> >
> >    Everything seems fine. But when I reject the blink component via GUI
> of eeglab and load the data again, Something strange happens. It seems the
> amplitude of EEG.data become much smaller, about in  -1~1 range.  Thus I
> wonder whether there was some normalization behind, and how can I correct
> it? The problem would not repeat if I choose the number of components same
> as channels event in fast ICA (e.g. change to "nICs = nchans" in the above
> code).
> >
> >    Thank you for your help.
> >
> > best
> > Peng
> > _______________________________________________
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>
>
> --------------------------------------------------------------------------------
>
> Joseph Dien,
> Senior Research Scientist
> University of Maryland
> 7005 52nd Avenue
> College Park, MD 20742-0025
>
> E-mail: jdien07 at mac.com
> Phone: 301-226-8848
> Fax: 301-226-8811
> http://homepage.mac.com/jdien07/
>
>
>
>
>
>
>
>
>
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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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