[Eeglablist] Re. Using ICA with interpolated channels
grega.repovs at psy.ff.uni-lj.si
Tue Sep 28 13:14:31 PDT 2010
Dear Rob & Hugh,
Since there seem to be arguments against using the problematic channels both before as well as after interpolation, why not run ICA without those channels. So the procedure would be:
1/ Identify and remove bad channels
2/ Perform ICA on good channels only
3/ Remove bad ICA components
4/ Reconstruct good channels
5/ Interpolate bad channels
This way neither noise nor non-linearities would affect the ICA solution and bad channels can still be interpolated based on cleaned data.
I also have one other question with regards to FASTER. In your paper you compared it to SCADS. I was wondering, why did you not compare it to ERP PCA Toolkit by Joseph Dien, which also performs fully automated data preprocessing and employs algorithms similar to FASTER. I myself would be quite interested in that comparison.
All the best,
On Sep 28, 2010, at 12:04 PM, Robert Whelan wrote:
> Jordi Costa Faidella wrote "Is it correct to perform an ICA on a dataset in which some of the channels have been interpolated?"
> This is an interesting question and we considered both orders (each order has some advantages and disadvantages) for the FASTER method. Ultimately, we decided to run interpolation first followed ICA. Here was our rationale:
> As the EEGLAB manual recommends – “ICA works best when given a large amount of basically similar and mostly clean data.” (see p.59). Therefore, an ICA on a dataset in which some channels are noisy (perhaps with a lot of non-stereotypic data due to a problem with the electrode) may decrease the quality of the ICA (i.e., dissimilar activations are mixed into the ICs).
> On the other hand, interpolating before ICA raises a couple of issues 1) it reduces the dimensionality of the data and 2) introduces some non-linearity into the data (if the interpolation method was not linear), which is detrimental to the ICA solution. We dealt with Issue 1in FASTER by restricting the maximum number of ICs to correspond with the reduced rank of the data after interpolation.
> The choice then was between reducing the quality of the ICA by introducing noisy channels or reducing the quality of the ICA by the non-linearity introduced due to spherical interpolation. Although ICA assumes linearity, there is almost certainly some non-linearity in the signals recorded at the scalp, and the non-linearity introduced by spherical interpolation is likely only a small contributer to the overall non-linearity. In any case, based on pilot testing we found that when the ICA was done with noisy channels included (i.e., not interpolated out) the resulting components were less useful than when the data were cleaner (i.e., the channels were interpolated). As an aside, testing algorithms on real data proved much more informative than testing on the simulated data, perhaps due to the inclusion of non-stereotypic artefacts in the real data.
> That said, we are certainly open to persuasion on this issue and/or suggestions about how to quantify which order is better. Also, might there be situations in which one order is superior to the other, perhaps depending on the maximum number of ICs that can be generated?
> If there is demand, we can also configure FASTER so that the user can select the order of the processing steps. Email me directly robert.whelan at tcd.ie or whelanrob at gmail.com if this is something that people might want or with any other suggestions.
> Best Regards,
> Rob & Hugh
> Robert Whelan, PhD
> Senior Research Scientist
> Trinity Centre for Bioengineering
> Trinity College Dublin
> Department of Neurology
> St. Vincent's University Hospital
> Elm Park, Dublin 4
> webpage: http://www.mee.tcd.ie/~neuraleng/People/Robert
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Asist. Prof. Grega Repovš, Ph.D.
Department of Psychology
University of Ljubljana
tel: +386 1 241 1175
email: grega.repovs at psy.ff.uni-lj.si
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