[Eeglablist] indipendent component analisys

[Agnieszka Zuberer]

I would like to add a further question to the pre-conditions of the ICA.

We currently do measurements with 4 eye-, 2 mastoids- and 5 scalp
electrodes (Fp1,FP2,FZ,CZ,OZ).
My question is if an ICA , from a theoretical point of view, is not
suitable with such few electrodes or if there are some a
posteriori check-up criteria after performing an ICA on the data, if the
ICA is not performing well.
We tried a gratton (regression based) correction but it was not working at
all (in many cases over-correcting). Would a kurtosis based artifact
correction be suitable in this case?

Thank you in advance for your response.

Agnieszka

2015-11-18 20:00 GMT+01:00 Scott Makeig <smakeig at ucsd.edu>:

> Dorian -
>
> Finding the same number of sources as channels makes ICA into a linear
> change of basis problem -- this makes the math simpler and reduces the
> number of assumptions involved in applying the analysis.
>
> We know, of course, that strictly speaking, small-scale potential
> variations in cortex alone are vastly more variegated than the number of
> scalp channels -- but most of these variations will be cancelled out
> through common volume conductance and summation at the scalp electrodes
> (i.e., through destructive phase interference, positive-going and
> negative-going potentials at any time point tending to cancel each other in
> their summation at each scalp electrode channel).
>
> EEG signals are thus dominated by (i.e,. chiefly sum) larger signals
> arising from locally synchronous 'patches' of cortical activity whose
> signals, summed across the disparate patch source activity, thus act as the
> effective (brain) sources of scalp EEG signals. In practice, ICA is of
> interest for brain EEG data analysis because it separates out signals from
> these patches (Delorme et al., 2012 PLoS ONE; Akalin Acar et al,
> Neuroimage, 2015).
>
> Scott Makeig
>
> On Tue, Nov 10, 2015 at 2:11 AM, Dorian Grelli <dorian.grelli at gmail.com>
> wrote:
>
>> Hi guys,
>> I've another questions about ICA. This is more theretical. I am wondering
>> why, after running ICA, we get as many indipendent components as we have
>> channells. I studied a bit of ICA theory in the tutorials ("for dummies"
>> and "not for dummies" that I found on the Internet) but, unfortunately, my
>> background is quite far from math and matrixes and it's difficult for me to
>> digest every detail. I think I get a bit of the theory but the point above
>> is still unclear. Could you help me?
>>
>> Cheers,
>>
>> Dorian
>>
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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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-- 
Agnieszka Zuberer
Möhrlistr. 92
8006 Zürich

Tel.: +41 76 29 51 321
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