[Eeglablist] Best Approach for Running ICA on EEG Data for Classification(ME & MI) (Pragati Dode)

[Pragati Dode]

Thank you Yamil and Jason for replying. I wanted to choose only one option
due to computational and time limitations. The information you both
provided is really helpful in making my decision.

On Wed, Mar 19, 2025 at 3:31 PM Jason Palmer via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi Pragati and others,
>
> Keeping the data together is a sound approach, but I think this is really a
> research issue, dependent on the application. For example, we can easily
> imagine a context where the brain sources differ with condition,
> particularly in the topographic map / dipole location. In this case, the
> unmixing matrices will differ with condition. If a classifier is trained on
> specific source activity, you may get better results using the condition
> specific source versus the unified sources (and a multiclass classifier).
> On
> the other hand, if the condition differences involve differences in source
> activity from a spatially fixed brain source, then you would be better off
> doing ICA on the combined data as this will give a better estimate of the
> spatially fixed source. As we generally don't know what the differences are
> a priori, it's probably best to try both, with cross-validation, and asses
> the performance.
>
> Additionally, I have found at least with ECoG data that improved
> classification can be achieved by doing band-pass filtering prior to ICA,
> using band specific source power as features.
>
> Best,
> Jason
>
> -----Original Message-----
> From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Yamil
> Vidal
> via eeglablist
> Sent: Monday, March 17, 2025 6:49 AM
> To: EEGLAB List <eeglablist at sccn.ucsd.edu>
> Subject: Re: [Eeglablist] Best Approach for Running ICA on EEG Data for
> Classification(ME & MI) (Pragati Dode)
>
> Dear Pragati,
> Undoubtedly, you need to keep all your data together for running ICA, and
> actually for all of your preprocessing. You should only divide the data in
> conditions as a last step for the purpose of running the classification.
> This is because if you first divide your data in conditions and then do
> further preprocessing, be this ICA cleaning or anything else, you are
> likely
> to introduce differences in the data across conditions which your
> classifier
> might later pick.
> In this way, you might find differences between conditions, even if there
> are no real differences.
> One way to think of it is that you are likely going to use ICA to remove
> blinks, cardiac and muscle artifacts, which should not differ across
> conditions and therefore you don't need to divide the data beforehand.
> Another reason not to divide the data is that ICA benefits from having
> plenty of data.
> I hope this is helpful.
> Best,
> Yamil
>
> Dr. Yamil Vidal
> Predictive Brain Lab
> Donders Centre for Cognitive Neuroimaging Nijmegen the Netherlands
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