[Eeglablist] A second ICA after removing components?

[Maximilien Chaumon]

Thanks Makoto,

I did not tweak the inside of the EEG structure, I just ran ICA a second
time on the same EEG structure after removing the components. Perhaps it
would be good to prohibit doing a second ICA if components have been removed
from the dataset.

If I remember correctly I removed three components from the first
decomposition, and found it funny that the second one then recovered
components with extremely similar topographies, as shown
here<http://oszilla.hgs.hu-berlin.de/public/2ICAs.png>
.

I did update the wiki based on the beginning of this discussion and added
the note you are referring to.

Max


2011/8/17 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>

> Dear Max and Ronald,
>
> ICA randomizes all time points for every step of convergence, so it
> does not matter whether the data is epoched or continuous.
>
> Let me confirm this again: as it is described in the last line of the
> wikipage, 'components should NOT be rejected before the second ICA',
> if you want to improve the quality in decomposition. It is unusual
> that you obtain 15 ICs even after discarding 3 ICs out of 15... I
> don't think what you did is the supported method because once the data
> rank is reduced (e.g. 15 -> 12 as a result of discarding 3 ICs) it
> never recovers. I guess that probably you copied (ALL)EEG.data to the
> other (ALL)EEG.data directly which does not have ICA weights. This is
> not the way to improve quality in decomposition, so do not bother to
> test this method.
>
> Makoto
>
> 2011/8/16 Maximilien Chaumon <maximilien.chaumon at gmail.com>:
> > Thanks Ronald,
> > I understand your point with the dimensions.
> > I thought ICA does not care about time, though? So concatenated, epoched
> or
> > not, the results should be the same, right?
> > Max
> >
> > 2011/8/16 Ronald Phlypo <Ronald.Phlypo at ugent.be>
> >>
> >> Dear Max,
> >>
> >> the problem lies in the maximally allowed number of components in both
> >> decompositions. The first decomposition may allow for as many sources as
> >> sensors (15 in your case). However, once 3 artefacts have been removed,
> your
> >> data dimension reduces to 12, which allows to estimate a maximum of 12
> >> sources only. To circumvent this problem, I suppose what the wiki means
> is
> >> to do local decompositions first (trial by trial or epoch by epoch) and
> then
> >> concatenating the trials/epochs again after their correction and before
> the
> >> second "joint" decomposition. Since in this case the artefact removal is
> >> nonlinear, it does not reduce the dimension of your concatenated data.
> >>
> >> Hope this helps,
> >>
> >> Ronald
> >>
> >> PS: you might also want to have a look at
> >> http://www.hindawi.com/journals/cin/2007/075079/cta/ where short time
> and
> >> long term windows are used jointly for artefact removal. The text refers
> to
> >> literature on the mean duration of electrophysiological processes to
> >> motivate this decision.
> >>
> >> Le 15/08/2011 18:09, Maximilien Chaumon a écrit :
> >>
> >> Hello all,
> >>
> >> I'm currently cleaning data before working with components.
> >>
> >> I cut my dataset into epochs
> >> reject epochs where signal is bad
> >> run an ICA
> >> find blink and muscle components, reject them
> >> run an ICA again, and look at my components.
> >>
> >> ... as I understood was suggested at the bottom of this page
> >> http://sccn.ucsd.edu/wiki/Chapter_01:_Rejecting_Artifacts
> >>
> >> Then the ICs look very nice but come in pairs of extremely similar
> >> topographies with different time courses, as shown on this picture.
> >> I am wondering what happened here. I can imagine that rejecting
> components
> >> before running the second ICA is what went wrong... But why did I read
> that
> >> on the wiki?
> >>
> >> Thanks a lot for any advice.
> >> Max
> >>
> >>
> >>
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>
>
> --
> Makoto Miyakoshi
> JSPS Postdoctral Fellow for Research Abroad
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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