[Eeglablist] Rank deficiency problem with ICA run on re-referenced data to the average of the temporal channels
bilginlab at gmail.com
Thu May 6 16:26:35 PDT 2021
Dear EEGLAB members,
I am running an ICA on my EEG data collected with 64 channels (63 + 1 ECG)
but I am getting a message that says "EEGLAB has detected that the rank of
your data matrix is lower the number of input data channels. This might be
because you are including a reference channel or because you are running a
second ICA decomposition. The proposed dimension for ICA is 62 (out of 63
channels). Rank computation may be inaccurate so you may edit this number
below. If you do not understand simply press OK." with a text box asking
for the input for the proposed rank that is written 62 on it currently. And
when I run the ICA with this default, I get 62 ICA components as expected.
I know from previous messages here
https://sccn.ucsd.edu/pipermail/eeglablist/2013/007062.html and Makoto's
explanation here https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline
there are several reasons that might cause this. In order to find out I run
ICA each time separately for each preprocessing steps and I found out
re-referencing the data to TP9 and TP10 channels (for the further N400
analysis as recommended in the literature) results in such rank deficiency.
So I was wondering would it be a problem in the future for the source
localisation of the data or ERP estimations and if does how can I avoid the
I would really appreciate any suggestions on this problem, please.
More information about the eeglablist