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

[Anna Michélsen]

Dear Dr Makeig,

Thank you very much for your assistance. I have tested to lower the threshold with a somewhat increased similarity between the components classified as artifactual between repeated runs. The distribution of eye and muscle classification probabilities were relatively similar between the runs, but they still differed, and the ICA decompositions seem to vary more in datasets referenced to FCz than those with the average reference.
These variations seem further to have downstream effects because when I calculate the mean alpha power (8-13 Hz) for each dataset (i.e., the same dataset being repeatedly run through ICA and ICLabel), the results vary. Thus, for average-referenced data, the processing pipeline is reproducible: repeated runs with the same dataset return similar results. For FCz-referenced data, however, the results vary between runs with the same settings.
Note that I have only repeated the runs a small number of times.

I am wondering what might be causing this problem because I would ideally like to use a hard threshold with ICLabel instead of removing components manually, both for reproducibility reasons and because I have a large dataset.

Regards,
Anna

________________________________
From: Scott Makeig <smakeig at gmail.com>
Sent: Tuesday, June 7, 2022 10:14:02 PM
To: Anna Michélsen
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Is single-electrode-reference such as FCz-reference problematic for ICA function (pop_runica)?


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<mailto: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


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--
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<http://sccn.ucsd.edu/%7Escott>