[Eeglablist] ICA question

[Scott Makeig]

John -

The (Delorme et al., 2012) PLoS One paper will be online
here<http://dx.plos.org/10.1371/journal.pone.0030135> --
any day now, I believe; a pre-press ms. is available
here<http://sccn.ucsd.edu/~scott/pdf/Delorme_EEG_ICs_are_dipolar_lo.pdf>.
A
mathematical exposition of Amica is
here<http://sccn.ucsd.edu/~jason/amica_a.pdf>,
and with related mathematical results on Jason's home
page<http://sccn.ucsd.edu/~jason/>
.

Scott Makeig


On Fri, Feb 3, 2012 at 5:03 PM, John Fredy <jfochoaster at gmail.com> wrote:

> Hello Scott
>
> Can you send me please the complete references of Palmer and Delorme?
>
> Thanks in advance
>
> John Ochoa
> Universidad de Antioquia
>
> On Thu, Feb 2, 2012 at 10:01 AM, Scott Makeig <smakeig at gmail.com> wrote:
> > Enrico -
> >
> > A best solution would be to record from more electrodes at frontal and
> > inferior electrodes around the head -- since (muscle) source density is
> > higher here, electrode density should best be higher here as well (as
> > non-intuitive as that may sound). This is particularly relevant for a
> gamma
> > band study (see Onton & Makeig, 2009 for an example with 256 electrodes
> over
> > the whole scalp).
> >
> > Also, you should investigate using Amica (Palmer, 2007) with
> unlikely-data
> > rejection set on, and/or possibly using two or more models competing for
> the
> > data (though the latter option is still difficult to interpret). Jason
> > Palmer is about to release binaries for PC, Mac, and linux that take
> > advantage of multiple cores and processors when possible. A study in
> press
> > (Delorme et al., PLoS One, 2/3/12-) shows Amica to be the best algorithm
> for
> > blind source decomposition of EEG data from at least two angles...
> >
> > Multi-subject ICA sacrifices much of the specificity and accuracy of ICA,
> > and also suffers more from undercompleteness (e.g., more
> > distinct-if-overlapping independent source projections than channels)
> than
> > single-subject data....
> >
> > Scott Makeig
> >
> > On Wed, Feb 1, 2012 at 6:44 AM, Enrico Schulz <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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> >
> >
> >
> >
> > --
> > Scott Makeig, Research Scientist and Director, Swartz Center for
> > Computational Neuroscience, Institute for Neural Computation; Prof. of
> > Neurosciences (Adj.), University of California San Diego, La Jolla CA
> > 92093-0559, http://sccn.ucsd.edu/~scott
> >
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>



-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation; Prof. of
Neurosciences (Adj.), University of California San Diego, La Jolla CA
92093-0559, http://sccn.ucsd.edu/~scott
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