[Eeglablist] ICA decomposition with 128 versus 64 channels?

[Philip Michael Zeman]

Arno's makes a very good point.  Increasing the number of degrees of freedom
available to the ICA algorithm allows for the ability to make the components
returned by ICA, topographically simpler.  In doing so, a localization
algorithm has a better chance of modeling the source as a simple arrangement
of dipoles.  This works to a point.  I've found in some cases (cognitive
experiments) that ICA does not simplify the topography enough to allow me to
do dipole localization.

I have found that if I want to reduce the complexity of components returned
by ICA, (so that I can model the source as a dipole), all I have to do is
remove channels that are adding to the complexity of my topography before
doing ICA on the data.  For example, if I want to see a VEP (left and right
calcarine ctx) and ventral temporal activity combined (assuming the VEP and
the ventral temporal activity are not separable by ICA), I can use electrode
placement that puts electrodes near the relevant areas (occipital, frontal,
central, and temporal), ensuring a uniform electrode grid.  If I want to
separate the VEP from the temporal activity, I just remove the ventral
temporal electrodes. (Bilaterally of course.  I want to maintain the
integrity of the grid.)


~Phil Zeman



----- Original Message ----- 
From: "Arnaud Delorme" <arno at salk.edu>
To: "Matthew Belmonte" <mkb30 at cam.ac.uk>
Cc: <eeglablist at sccn.ucsd.edu>
Sent: Friday, January 20, 2006 11:30 AM
Subject: Re: [Eeglablist] ICA decomposition with 128 versus 64 channels?


>
> >I've heard from one EEGLAB user that 128 channels don't confer much
advantage
> >over 64, since inputs must be spatially downsampled in order to be
processed
> >practically on typical computing hardware
> >
> Standard computers with 1Gb of RAM can process a standard 100 to 200Mb
> dataset with 128 channels (and run ICA). It is the lower limit though.
>
> >, and since the independent components
> >of interest (those from neural sources) don't become much cleaner with
128
> >inputs as compared to 64.
> >
> They do become cleaner. It is important to have as many channels as
> possible if you want to perform source localization. For instance, in
> EEGLAB, DIPFIT will return residual variance on the collection of all
> data channels. If you can model one component with a single dipole and
> the residual variance is 2%, it is much more convincing if you have 128
> channels than if you have 64. The reason is a single dipole (5 numbers)
> was able to explain a scalp map containing 128 numbers (in contrast to
> 64). Since ICA does not take into account the spatial localization of
> channels (and thus there is no reason for scalp maps to end up being
> modeled with a single equivalent dipoles), it is much more convincing to
> conclude than an independent component represent the activity from a
> single part of cortex when you have 128 channels than when you have 64.
>
> Also, testing (not published) I have done with 256 channels show that
> the (single) dipole localization for ICA component becomes less stable
> when using less than 70 channels (in term of spatial localization). I
> reduced gradually the number of channels (using standard montages) and
> then performed dipole localization. The exact localization did not
> change much between 256, 128, and 70, but broke down below 70, and was
> dramatically different with 32 channels (more than 1 cm off). This would
> have to be validated in a larger study though.
>
> Best,
>
> Arno
>
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