[Eeglablist] ICA question

[Pomper, Ulrich]

Dear Baris/ list members,
Is it actually correct to run an ICA more than once on the same dataset? So I could run an ICA, remove all the artifacts I can find, then run another ICA on the same dataset which presumably would find artifactual components that weren't found by the first one? Wouldn't that be a very promising approach to thoroughly clean the data?
(sorry for the hijack..)
Cheers, Ulrich


On 02.02.2012 06:14, Baris Demiral wrote:

I think your suggestion that concatenating everybody and at least find
the weak artifactual IC activity of the clean (least affected)
subjects, and taking this IC out may lead to, again, some spurious
effect, such that the weights you find will not exactly be the IC
weights related to the artifacts of the clean subject.

Your hypothesis is a bit hard to follow. You assume that there are
artifacts which are not captured by ICA for those clean subjects. If
you are so sure of that do the following: After the first ICA run,
take the observed artifactual ICs. Then run ICA again. And if there
are no more artifacts, then you are wrong.

In parallel, find the dipoles of the ICs with dipfit plugin. If a
component is really an artifactual component, the dipole will be near
the scull or out of the brain (use BESA 4-layer, with no
co-registration, will give you good estimations).

If you run ICA separately for the two groups twice  (rejecting
artifacts after the first one), you will end up with same number of
ICs for the two groups.

Baris

On Wed, Feb 1, 2012 at 8:44 AM, Enrico Schulz <enrico.schulz at gmail.com><mailto:enrico.schulz at gmail.com> wrote:
> Dear EEGlab list,
>
> I have a problem with the ICA-based artefact reduction that is actually not
> just restricted to the EEGlab software.
>
> I'm struggling with a lot of high frequency- artefacts at frontal and
> inferior electrodes around the head exhibiting a much higher amplitude than
> the cortical gamma band activity I'm interested in. Although it is possible
> to remove the strongest artefacts, some muscle activity could not be removed
> in my data sets because some of the artefacts do not give rise to a separate
> component.
>
> In my naive view, in addition to the fact that there are still artefacts in
> the data set, this could lead to a bias for some subjects. In theory, if a
> strong artefact gives rise to an independent component and can, hence, be
> removed, the amount of artefacts in that data set is now lower than in a
> different data set, where that artefact is not strong enough for a distinct
> component.
>
> The problem is even more complicated if an experimental group (e.g. pain
> patients) has stronger muscle artefacts than a healthy control group.
>
> Sorry for the long introduction, but my actual question is, whether it is
> possible to concatenate all single subject files and doing the ICA for that
> big file.
> I'm aware that this approach has other disadvantages, e.g. it requires a
> similar topography for each artefact across all subjects and a fast
> machine.
>
> Any help/opinion is highly appreciated!
>
> Best regards,
> Enrico
>
>
>
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--
Ş. Barış Demiral, PhD.
Department of Psychiatry
Washington University
School of Medicine
660 S. Euclid Avenue
Box 8134
Saint Louis, MO 63110
Phone: +1 (314) 7477 1603

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