[Eeglablist] Rank deficiency problem with ICA run on re-referenced data to the average of the temporal channels

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Fri May 7 10:47:54 PDT 2021


Dear Isil,

See also this article.
https://sccn.ucsd.edu/wiki/Makoto%27s_useful_EEGLAB_code#How_to_turn_on_the_automatic_protection_against_rank-deficient_ICA_in_pop_runica_.283.2F31.2F2021_added.29

I will post a related comment to the list.

Makoto

On Thu, May 6, 2021 at 6:32 PM Isil Bilgin via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> 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
> problem occurring?
>
> I would really appreciate any suggestions on this problem, please.
>
> Kind regards,
>
> Isil Bilgin
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