[Eeglablist] ICA "adds" noise?

Matt Craddock matt.craddock at uni-leipzig.de
Wed Jan 23 03:44:41 PST 2013

On 18/01/2013 20:39, Joseph Dien wrote:
> Another thought occurs to me.  I have indeed noticed a tendency for
> increased noise to show up in my own ICA-based artifact correction
> routine in the EP Toolkit (Tim Curran first reported it to me).  I've
>  never worked out why.  I ended up implementing a trial-by-trial
> workaround wherein the eyeblink factors are removed from a given
> trial only when it reduces the overall variance of the trial.  In
> other words, when the benefit outweighs the cost.  The increased
> noise that I see is small enough that it gets averaged out for ERPs
> so has not been an issue.  Could be an issue for frequency-based
> measures though.  I need to look into this further.  Anyway, what
> you're reporting seems more severe than anything I've observed so
> perhaps something different.

Hi Joe, Kristina, and all,

I'm mostly dealing with frequency analysis; the noise does indeed pose
some problems for frequency-based measures, since it translates into
noise in the gamma band range (>40Hz, mostly). This issue has been
reported previously on this list:


and the conclusion then was that it was down to reduced rank:


Hence why I jumped on that as an explanation when I saw Kristina's
original post. My situation turns out to be a little different from
hers, in that I use average reference rather than linked mastoids, and
don't keep a reference channel in the data, so it didn't seem to be 
caused by the duplicate data issue Makoto identified (although sometimes 
it may have been - see later; but wouldn't that also be a rank 
reduction?). In my case I've found doing PCA first (reducing number of 
components to the rank, so usually only to numChannels-1) makes this 
problem go away, but given that everybody said avoid doing that first, I 
also had a closer look at the datasets where I'd had this problem and 
found in some cases that there were *very* high correlations between 
some channels (.99 in one case!). Removing one of those channels before 
running ICA (and *not* doing PCA) also fixed the problem. I didn't see 
any major differences in the components between PCAing first and 
removing the channels, though of course that's not to say there aren't 
any that would emerge if looking at them more systematically!

> Average reference doesn't reduce the rank.  Basically all it does is
> to virtually move the reference site.  In the original
> vertex-referenced data, there is informational ambiguity as to
> whether recorded voltage fluctuations are due to activity at the
> reference site or at the recording site (unavoidable since voltages
> are by their nature relative and so require a reference site).  When
> one algebraically rereferences the data to a different single
> reference site (including the virtual reference site of average
> reference) there is no increase in informational ambiguity.  Mean
> mastoid reference does increase increase informational ambiguity
> because it introduces a new ambiguity of whether reference site
> activity is occurring at the left or right mastoid.  In essence, this
> is because a subset of the total set of electrodes has been singled
> out and mixed together.  This increased ambiguity reduces the rank by
> one.

Hmm, but this page says that average reference does reduce rank, and 
that's been what people have said on this list for quite a while.

Happy to be corrected, but on the whole I'm left a little puzzled - the 
default behaviour of EEGlab's GUI is to suggest PCA reduction if your 
data is reduced rank. Given the consensus seems to be to avoid PCA, does 
that need to change, or at least to suggest people be very cautious 
about using it and try to find alternative ways of conditioning their 
data, be that removing channels or whatever?


Dr. Matt Craddock

Post-doctoral researcher,
Institute of Psychology,
University of Leipzig,
Seeburgstr. 14-20,
04103 Leipzig, Germany
Phone: +49 341 973 95 44

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