[Eeglablist] Re. Using ICA with interpolated channels

Jaco Sitt jdsitt at gmail.com
Fri Oct 1 01:36:48 PDT 2010


Dear Arnaud,

Could you please give more details of the reduction of dimensionality
on the 256 channels recordings ?

I am running ICA on the whole 256 channels  to remove artifacts, but I
am concern regarding the quality of ICA in this case.

Kind regards,


-- 
Jacobo Diego Sitt, MD, PhD

Groupe Hospitalier Pitié-Salpétrière,
Service de Neurologie 1, & Centre du Cerveau et de la Moëlle
47-83 Bd de l'Hôpital, 75651 PARIS Cedex 13

INSERM U992CEA/Saclay, NeuroSpin Bât 145
CEA - Saclay, France
Point Courrier 156, 91191 Gif/Yvette Cedex



On Thu, Sep 30, 2010 at 8:02 PM, Arnaud Delorme <arno at ucsd.edu> wrote:
> Dear Grega and Robert,
> concerning interpolating channels before running ICA. Robert's analysis of
> pros and cons of interpolating channels before running ICA makes perfect
> sense. We personally at the Swartz center never interpolate data channels
> and rarely reduce the dimensionality of the data matrix before running ICA
> (unless we are using 256 channels and then we reduce the dimensionality to
> 150 before running ICA). As Robert pointed out both reducing the
> dimensionality using PCA and interpolating channels introduce no
> linearities. Spherical interpolation introduces non-linearities because a
> non-linear algorithm is used to interpolate channels. Pre-processing with
> PCA introduces non-linearities because some of the PCA components - the ones
> with the lowest eigenvalues - are discarded. Since PCA does not model the
> structure of the data (i.e. the brain sources), this introduces non
> linearity. It is hard enough to run ICA and get a clean decomposition for
> the purpose of analyzing brain source that it is better not to apply any
> procedure that would potentially introduce non-linearity. When running ICA
> for the purpose of removing artifacts, this is probably less critical.
> Just wanted to add my grain of salt,
> Cheers,
> Arno
> On Sep 29, 2010, at 4:36 AM, Robert Whelan wrote:
>
> Dear Grega,
>
> That is a great suggestion -- thank you. Jordi Costa Faidella emailed me
> directly with the same suggestion yesterday and we've already started coding
> up the approach that you describe -- should be done and tested in a day or
> two. We will also run our EEG data through the new approach and quantify the
> difference between interpolating channels vs. removing channels before ICA.
>
> Re. Joseph Dien's ERP PCA toolkit. At the time of writing our paper we
> wanted to pick a method from the literature for comparison (although the ERP
> PCA toolkit has been available for a while), and with the publication of
> Dien (2010) we will definitely compare the two approaches. Although I
> haven't used the Toolkit yet, I read the Dien (2010) paper recently and it
> looks great.
>
> Thanks again,
>
> Rob & Hugh
>
> On Tue, Sep 28, 2010 at 9:14 PM, Grega Repovs
> <grega.repovs at psy.ff.uni-lj.si> wrote:
>>
>> 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,
>> Grega Repovs
>>
>>
>> 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
>> Aškerčeva 2
>> SI-1000 Ljubljana
>> tel: +386 1 241 1175
>> email: grega.repovs at psy.ff.uni-lj.si
>
>
>
> --
> 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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