[Eeglablist] ICA question

[Tarik S Bel-Bahar]

Its an option, but hardly a final solution. Depends on your Erp vs. ica
needs. People have had success using ica time isolate and amplify
particularly erps.
On Feb 3, 2012 11:34 AM, "Pomper, Ulrich" <Ulrich.Pomper at charite.de> wrote:

>  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><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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