[Eeglablist] ICs with identical topographies

Maximilien Chaumon maximilien.chaumon at gmail.com
Mon Sep 12 05:30:37 PDT 2011


Thank you Randu,

So, should I do my rereferencing step like this?
I first compute the average M of my data across sensors, then rereference
the data using an average reference, then add the M data as a new sensor. Is
this correct?

    Av = mean(EEG.data);

    EEG = pop_reref( EEG, []);

    EEG.data(end+1,:) = Av(:);
    EEG.nbchan = size(EEG.data,1);
    EEG.chanlocs(end+1).label = 'AveRef';

I don't really understand how adding another channel that is the average of
all others will increase the rank of the data. And the result of
svd(reshape(EEG.data,EEG.nbchan,EEG.trials*EEG.pnts)) does not change
(except adding one point at the end, very close to zero).

Sorry to bug you with this, but I just want to be sure I'm doing it right.

Thanks,
Max





2011/9/12 Radu Ranta <radu.ranta at ensem.inpl-nancy.fr>

> Hi Max,
>
> As a complement of information to what Scott was saying, re-referencing
> to the average without keeping the reference (i.e. the average) makes
> the rank of your data even smaller (basically, the sum of all your
> channels except the reference will be 0, that is you can always write
> one of them as -(sum of all others)). You might want to take a look at
> this paper, which suggests a which is the "best" re-referencing before
> ICA: "EEG montage analysis in the Blind Source Separation
> framework" (Salido-Ruiz et al, Biomedical Signal Processing and Control
> 6(1), 2011).
>
> Radu
>
> Le jeudi 08 septembre 2011 à 15:02 +0200, Maximilien Chaumon a écrit :
> > I think I found at least part of the solution to my problem:
> > I keep the reference channel (in my case an average of all electrodes)
> > in the dataset. This seems to reduce the rank of the data (the svd
> > vector drops close to zero on the last value). I think I get an
> > intuition of why it does so, but how am I supposed to do?
> > Overall, I've checked, the subjects on which I removed one or more bad
> > channels (after rereferencing) are fine. Their ICA looks nice, and the
> > svd values do not drop close to zero on the last value.
> > The problem is for those for whom I did not remove any bad channel.
> > Their ICA shows the symptoms shown on my last email, below.
> >
> > So my questions end up being:
> > Do I have to reference my data to one electrode and not include it in
> > the ICA? and if so, why would anyone use an average reference before
> > ICA? what is recommended? I can't find anything about referencing the
> > data in the tutorial.
> >
> > Thanks,
> > Max
> >
> > 2011/9/7 Maximilien Chaumon <maximilien.chaumon at gmail.com>
> >         Hi eeglabbers,
> >
> >         I am still having issues with ICA returning extremely similar
> >         (but not identical) topographies. The gui (although I run a
> >         version updated a few days ago) does not popup any suggestion
> >         to reduce the rank. I only get this warning (Warning: fixing
> >         rank computation inconsistency (68 vs 69) most likely because
> >         running under Linux 64-bit MatlabAttempting to convert data
> >         matrix to double precision for more accurate ICA results.) I
> >         still get 69 components in the end.
> >
> >         Here's some more info:
> >         the rank of the data is 68. The svd drops abruptly close to
> >         zero at the last value. I have 69 electrodes (64 heancap +
> >         3EOG+2mastoids). I guess there's a gel bridge somewhere.
> >         Although correlations between all electrodes don't reach .95.
> >         When I let the ICA run with default options, I still get these
> >         two components (always 'P3 like' components, this was
> >         reproduced in other subjects). Their frequency profiles are
> >         too good to be true, with low noise and a peak at 10Hz,
> >         another one around 20Hz, see the figure. I would leave them
> >         alone if they were not spoiling all my data: As I remove the
> >         components, when I click this 'singles' button, which shows me
> >         the trial by trial time course. I get what is shown on the
> >         right of the figure. High frequency bursts appearing every now
> >         and then, usually at times where there is high variability
> >         across channels.
> >         Removing both components resolves the issue, but I loose a
> >         rather important part of the data.
> >         Here is the spectopo at 60Hz. There is a strong artefact here.
> >         The two components show a high power at all frequencies.
> >         How could frequencies that do not exist in the input be
> >         created by the ICA? I filter my data, before ICA below 45Hz.
> >
> >         I tried running fastica, asking for 68 components, no such
> >         artifact appears but the decomposition looks much less nice,
> >         at least with the parameters I've used.
> >
> >         So in the end, my question is:
> >         How can I run an ICA without trouble if the rank of the data
> >         is not equal to the number of electrodes? How can I identify
> >         potentially gel bridged electrodes?
> >
> >         Many thanks,
> >         Max
> >
> >
> >
> >
> >         2011/8/27 Arnaud Delorme <arno at ucsd.edu>
> >                 Regarding the matrix rank, we have recently realized
> >                 that the rank function (and other rank function we had
> >                 programmed) are not fully reliable which is probably
> >                 with Max observes the component he observes. The
> >                 runica function should automatically decrease the rank
> >                 of the input data matrix. However, sometimes it does
> >                 not use the correct rank. We have modified the runica
> >                 GUI so that if the matrix is not full rank, it now
> >                 pops up a new window suggesting to the user a rank
> >                 reduced value. This value may be adjusted by the user
> >                 based on prior knowledge. For instance, if you have
> >                 removed 5 components from the data, you would reduce
> >                 the rank by 5 (and overwrite the rank that is
> >                 automatically detected if it is not correct).
> >
> >
> >                 Arno
> >
> >
> >                 On Aug 23, 2011, at 10:11 PM, John J.B. Allen wrote:
> >
> >                 > Max
> >                 >
> >                 >
> >                 > I have observed that when the data are not full
> >                 > rank.   You can test the rank of your data by
> >                 > reshaping your epoched data to a 2D matrix, and
> >                 > running the rank command, like this:
> >                 >
> >                 >
> >                 > rank(reshape(EEG.data,EEG.nbchan,EEG.trials*EEG.pnts))
> >                 >
> >                 >
> >                 > When I did this, your rank is 63, but you have 69
> >                 > channels, indicating that some channels are linearly
> >                 > dependent on others.  I think this is the source of
> >                 > your problem, and if you remove those channels
> >                 > before running ICA, you should no longer see this
> >                 > issue.
> >                 >
> >                 >
> >                 > Best
> >                 >
> >                 >
> >                 > John
> >                 >
> >                 >
> >                 >
> >                 >
> >                 >
> >                 >
> >                 > On Tue, Aug 23, 2011 at 07:24, Maximilien Chaumon
> >                 > <maximilien.chaumon at gmail.com> wrote:
> >                 >         Hi eeglabbers,
> >                 >
> >                 >         I sometimes get ICs with extremely similar
> >                 >         topographies and time courses, like on this
> >                 >         slide.
> >                 >         I know that ICA returns independent
> >                 >         components.
> >                 >         Does that not mean that they should not look
> >                 >         the same?
> >                 >         I know the components are independent in a
> >                 >         statistical sense, which is not the same as
> >                 >         uncorrelated, but still. I'm a bit
> >                 >         surprised. What do these two components mean
> >                 >         if they cancel one another? well, do they?
> >                 >
> >                 >         Sorry if my question is naive, but what is
> >                 >         happening?
> >                 >
> >                 >         The data is here.
> >                 >
> >                 >         Best,
> >                 >         Max
> >                 >
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> --
> ______________________________________________________________________
> E-mail : Radu.Ranta at ensem.inpl-nancy.fr
>
> Radu RANTA
> Nancy Université/INPL - ENSEM - CRAN
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