[Eeglablist] Dealing with bad channels in merged datasets

Erickson ericksonb.eng at gmail.com
Thu Apr 3 13:02:02 PDT 2014

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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