[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
> 2, Avenue de la Forêt de Haye Tel : +33.(0)3.83.59.57.09
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