[Eeglablist] ICA analysis + interpolation + rank of EEG data

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Sun Apr 2 17:05:20 PDT 2017

​Hello Sonia, some notes below, best wishes.

Generally speaking one ICA should be enough if you clean your data
appropriately before running ICA. Not sure which tutorial you are referring
to. If it is the general eeglab tutorial or Makoto's pipeline (check it out
if you have not) remember those are recommendations not golden rules. Many
investigators make their own unique choices.
Channels should not be interpolated before any ICA.​
I might be wrong, but cleanline as far as I know is meant to denoise data,
not drop channels, so you may want to double check that you are running
cleanline and not PREP toolbox or clean_rawdata.
Regarding rank see Makoto's pipeline and past discussions and responses on
Last, if you have not had a chance to fully process eeglab tutorial data it
is highly recommended.

On Thu, Mar 30, 2017 at 6:42 AM, Dr. Sonia Baloni <sbaloni at cbcs.ac.in>

> Hi All,
>     I am new to EEGlab and trying to work with ICA analysis. I have been
> reading the eeglab list mails on ICA topic. I have gather few footnotes
>  and few question which I would like to ask:
> 1. Cleanline algorithm removes bad channels from the data. Posts in
> EEGlablist suggests that interpolation should be done after ICA.The
> tutorial suggests that two ICAs should be performed on the same data-set.
> So if we are performing two ICAs, we should interpolate these channels
> after second ICA?
> 2. I assume rank of the data (used in ICA analysis) would be equivalent to
> the number of channels in data after running clean line function. If there
> are for example 128 channels with which data was collected and 10 channels
> were removed with clean line function then the rank of the data would be
> 118. If we now want to run ICA on this data do we need to add “ ‘pca’,117 “
> in command line option - next to ‘extended’, 1 ? as suggested in  the
> tutotrial : https://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_
> Using_ICA.
> Thanks a lot.
> Best
> Sonia
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