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

[Anna Michélsen]

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