[Eeglablist] Double Dipping with ICA

[Nicholas Dogris]

Hi Michael,

Wrong use of words...I am simply trying to understand if running ICA, then
removing a component, and then running ICA again is "ok" or can produce bad
data.  I feel that I have received good answers to that question and feel
comfortable proceeding with doing so.

Cheers,

Nick

On Mon, Nov 9, 2020 at 11:36 AM Dr. Michael Villanueva <
mvillanueva at alphathetacenter.com> wrote:

> Nick
> Your use of the word "distorted" in reference to ICA caught my eye.
> Independent Component Analysis is a linear transformation.  Thus, ICA no
> more "distorts" data than subtracting the number 3 from 12 distorts the
> number 12.
>
> Michael
>
> -----Original Message-----
> From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Scott
> Makeig
> Sent: Sunday, November 8, 2020 10:51 AM
> Cc: EEGLAB List <eeglablist at sccn.ucsd.edu>
> Subject: Re: [Eeglablist] Double Dipping with ICA
>
> Re the double-dipping example Makoto provides: Don't neglect to check,
> however, whether and to what extent IC ERPs do contribute to the ICs signal
> variance - this may be less than 1% (i.e., removing the IC ERP from every
> trial reduces the overall IC signal variance by ~1%). If you wish, you can
> remove the ERPs from every trial after clustering, and then test
> differences in remaining cluster variance-not-explained-by-the-ERPs. Or,
> you can normalize the ERPs *before* clustering based on equivalent dipole
> location and normed ERPs, then test differences in cluster
> variance/power/etc.
>
> Scott
>
> On Sun, Nov 8, 2020 at 1:39 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:
>
> > Dear Nick,
> >
> > Two things.
> >
> > 1. The second ICA you mentioned does not change the result. Just try
> > it and you see what happens.
> >
> > 2. Double dipping refers to a procedure that is known to inflate Type
> > I error. ICA uses mutual information, and you test amplitude/phase
> > metric--these two do not have apparent correlation. If you say,
> > cluster ICs by ERP waveforms, then perform ERP amplitude difference,
> > this is double dipping (why? because IC cluster's ERP variance is
> > minimized by clustering, but this variance information is being used
> > in the subsequent statistical
> > test)
> >
> > Given that,
> >
> > > Are there reasons not to do this or to do it?
> >
> > You may do so but it is meaningless because the second ICA in that
> > case does nothing. And this has nothing to do with the double dipping
> problem.
> >
> > Makoto
> >
> > On Sun, Nov 8, 2020 at 8:22 AM Nicholas Dogris <
> > drdogris at neurofieldneurotherapy.com> wrote:
> >
> > > Will my data be distorted if I run ICA, remove IC's such as eye
> > > blinks,
> > and
> > > then run ICA again on the same dataset?  Are there reasons not to do
> > > this or to do it?
> > >
> > > Cheers,
> > >
> > > Nick
> > > _______________________________________________
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>
>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for
> Computational Neuroscience, Institute for Neural Computation, University of
> California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott
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