[Eeglablist] ICs with identical topographies

Maximilien Chaumon maximilien.chaumon at gmail.com
Wed Sep 7 10:26:05 PDT 2011


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 <http://oszilla.hgs.hu-berlin.de/public/2P3like_components.png>(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<http://oszilla.hgs.hu-berlin.de/public/2P3like_components.png>.
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<http://oszilla.hgs.hu-berlin.de/public/2P3spectopo.png>.
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<http://oszilla.hgs.hu-berlin.de/public/Similar_ICs.PNG>
>> .
>> 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 <http://oszilla.hgs.hu-berlin.de/public/Similar_ICs.mat>
>> .
>>
>> Best,
>> Max
>>
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