[Eeglablist] Is single-electrode-reference such as FCz-reference problematic for ICA function (pop_runica)?

[Scott Makeig]

Anna -

Making or missing a 90% hard threshold in ICLabel can make the variability
seem stronger than it is.  Did you look at the actual distribution of e.g.
Eye component likelihood values for 'candidate' Eye components?

Scott

On Mon, Jun 6, 2022 at 1:03 PM Anna Michélsen via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear EEGlablist,
>
> By chance I noticed that the results of the ICA decomposition and the
> subsequent classification with ICLabel varies considerably when run
> repeatedly on the same dataset (e.g., from 5 to 11 components classified as
> eye and/or muscle artifacts with 90% probability for the exact same
> dataset). This is only the case when the data is FCz-referenced (i.e., the
> online reference is kept unchanged). If the data is instead re-referenced
> to average and then repeatedly run through ICA, the resulting decomposition
> and classification appears more stable.
>
> Before sending the data to ICA it has been:
> 1) high-passed filtered (1Hz) with pop_eegfiltnew
> 2) processed with pop_cleanline (default settings)
> 3) processed with pop_clean_rawdata (default settings)
> 4) removed channels have been interpolated with pop_interp (‘spherical’)
> 5) after these four steps the data is saved with the online reference FCz
> as one dataset and re-referenced to average (FCz added back) and saved as a
> second dataset.
>
> These two datasets are then repeatedly run through ICA using the following
> code:
> pop_runica(EEG, 'extended', 1, 'interrupt', 'on', 'pca', channel rank)
> pop_iclabel(EEG, 'default')
> pop_icflag(EEG, [NaN NaN;0.9 1;0.9 1;NaN NaN;NaN NaN;NaN NaN;NaN NaN])
>
> I expected there to be some variation in decomposition (and possibly minor
> changes in which components were classified as artifactual) between
> repeated runs due to ICA starting with a random weight matrix, but not that
> the results of the classification would differ to this extent. Am I missing
> something obvious? Does the PCA before ICA add to the instability of the
> results? I have repeated this procedure for several different datasets and
> some appear more stable than others. Could this simply be a question of
> data quality leading to less stable ICA decompositions? Any form of
> assistance is appreciated.
>
> Regards,
> Anna
>
>
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to
> eeglablist-unsubscribe at sccn.ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to
> eeglablist-request at sccn.ucsd.edu
>


-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott