[ENH] Reject ICA components based on muscle artifact

@AdoNunes wrote this code + example https://mne.tools/dev/auto_examples/preprocessing/muscle_detection.html to detect muscle artifacts in epochs but I think it’s helpful to apply muscle classification to ICA components for EEG which I don’t think is documented. The thought is to use annotate_muscle_zscore on ICA components. This is really important for TMS-EEG but also pretty important for just EEG, depending on how relaxed the subject’s head and face is. Potentially we could use TMS-EEG data from OpenNeuro, there is one dataset. I think maybe not necessary though.

[agramfort]

MEG is not too artifacted my EMG. At least much less than EEG and for most common protocols

my 2c

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[alexrockhill]

I’ve looked at the sample data and the muscle components are non-existent in MEG as far as I can tell.

One complication is that, for the default topomap, the properties plot for MEG and EEG data is magnetometers and, since the muscle artifacts don’t show up pretty much at all on magnetometers and since the topography is the main confirmation, that makes it difficult to use. You can programmatically pass topomap_args=dict(ch_type='eeg') but it would be nice to add a keyboard shortcut to toggle the topomap type. I think pretty easy to do as well.

This is what typical muscle artifacts look like in my experience and in the paper cited above. They are very low ICA components with MEG so it would probably be better to split this out into a separate example with just EEG (although if I were analyzing simultaneous MEG and EEG, I would probably want to go through even these low components to remove them from the EEG or compute ICA separately by channel type).

Also, from what I understand the “spiky” nature of the ICA components is a giveaway and is the firing of a single motor unit for that muscle.