[Eeglablist] Dealing with bad channels in merged datasets

[Makoto Miyakoshi]

Dear Erickson,

> However, since the setup is identical between sessions, we are merging
the datasets from each subject's 4 sessions into a large dataset,

Sorry to point this out, but you can't do this because electrode cap
applications were different for each of 4 recordings, right?

Even a re-gelling can move a channel and create 'another' IC which shows
'blocked' ERPimages...

Run ICA separately for each of 4 recording sessions. If you don't have
enough datapoints, you can run pca to reduce dimensions. For infomax, after
'extended', 1, continue 'pca', 20 for example.

Makoto

2014-04-03 13:02 GMT-07:00 Erickson <ericksonb.eng at gmail.com>:

> List,
> I am creating a data pipeline to process resting state eeg with ADJUST,
> and I've run into a conceptual problem with bad channels.
>
> Our study involved the collection of resting state data across several
> days. Individually these resting state files are not long enough to meet
> the data requirements of ICA (datapoints/channels^2 > 30 or 40). However,
> since the setup is identical between sessions, we are merging the datasets
> from each subject's 4 sessions into a large dataset, running ICA on this
> dataset, and then applying the ICA weights back to the 4 individual
> datasets. We have no reason to believe that the EEG signature of a blink
> would be any different between sessions, nor is the cognitive task
> different (resting state) so this merge seems to be a nice way to take care
> of the problem.
>
> However, my issue is that if there is a bad channel in one of these 4
> datasets, and I remove it, the dimensionality of the datasets is different
> and they can't be merged, much less used for ICA. Normally I would
> interpolate to get those channels back, but it's not correct to interpolate
> before ICA.
>
> Currently, my solution is just to accept the loss of data. If a channel is
> bad in any of the 4 original datasets, I have to remove it from all 4
> original datasets. Then I can merge them and run ICA on the merged file.
> Then I apply those ICA weights to the 4 original datasets individually and
> run ADJUST. However, I'm obviously throwing away a lot of data here so I
> would like to know if there is a better way.
>
> Can anyone suggest an option I am not thinking of to solve this issue?
>
> Thanks for your time! - Brian
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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