[Eeglablist] Remove or retain low-mutual information components in ADJUST?

Erickson ericksonb.eng at gmail.com
Wed Jan 22 09:52:39 PST 2014


Marco,
Thanks for your response, that makes sense to me!
I've tested ADJUST with several sample datasets, and it appears to be
working well on our data.

Brian Erickson
Ph.D. Candidate
Applied Brain and Cognitive Sciences Program
Drexel University, College of Arts and Sciences, Department of Psychology


On Fri, Jan 17, 2014 at 4:39 AM, Marco Buiatti <marco.buiatti at gmail.com>wrote:

> Dear Brian,
>
> while ICs low in the list might represent just noise, they might also
> be associated with low-variance artifacts like transitory jumps in one
> specific electrode, and these are the ones that ADJUST typically
> captures at the bottom of the list.
>
> In any case, since they represent low portions of the overall
> variance, I do not think that if they are just noise, eliminating them
> would be a problem, at least for a standard ERP analysis.
>
> Do however check that ADJUST works properly on your data before
> blindly apply it.
>
> Best,
>
> Marco (ADJUST author).
>
> On 15 January 2014 23:43, Erickson <ericksonb.eng at gmail.com> wrote:
> > List,
> > I am creating a data processing pipeline using ADJUST to automatically
> > reject components. Before I let this monster loose on my data, I want to
> > make sure that I should trust ADJUST when it tells me that components
> low on
> > the list are artifacts. To explain:
> >
> > ICA components are returned in order of decreasing mutual information.
> Thus,
> > component 1 has the highest MI, 2 the next highest, etc. At the EEGLAB
> > conference this year, Scott made the point that components very low on
> the
> > list contain very low MI and may not actually represent sources, but
> noise
> > not otherwise accounted for. Yet ADJUST does not seem to have any cutoff
> -
> > it will happily label component 84 out of a dimensionality of 84 as an
> > artifact. If this component is not reliable, I certainly don't want to
> > eliminate it from my data as an artifact.
> >
> > Has anybody dealt with this issue? Perhaps by setting some cutoff (half
> the
> > dimensionality? or some measure of MI?) below which we no longer accept
> that
> > calling a component artifactual makes sense?
> >
> > Thank you,
> > Brian
> >
> > Brian Erickson
> > Ph.D. Candidate
> > Applied Brain and Cognitive Sciences Program
> > Drexel University, College of Arts and Sciences, Department of Psychology
> >
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>
>
> --
> Marco Buiatti, PhD
>
> CEA/DSV/I2BM / NeuroSpin
> INSERM U992 - Cognitive Neuroimaging Unit
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>
> ***********************************************
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