[Eeglablist] indipendent component analisys

[Makoto Miyakoshi]

Dear Agnieszka,

> with 4 eye-, 2 mastoids- and 5 scalp electrodes (Fp1,FP2,FZ,CZ,OZ).

So you have total of 11 channels. I'd run ICA. At least you can identify
eye blink and eye movement reliably.

Makoto

On Thu, Nov 19, 2015 at 12:24 AM, Agnieszka Zuberer <azuberer at googlemail.com
> wrote:

> 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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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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