ICA and line noise can be unstable?

I have been trying to remove the line noise of some ECoG data using ICA (I am trying to avoid bandstop filter here).

However, the results I get are pretty unstable, and I am not quite sure why. That is, fitting the ICA several times of the same data can give radically different results (very good or very bad).

• I think I checked that this was not a copy/memory problem.
• Setting the ICA(random_state) stabilizes the results (but is arbitrary, since different random_state give very different results).

Here is a summary of what I do [complete notebook here]:

• sources = 1/F random source signal
• mix these source signals together with a line noise (raw = dot(cov, vstack(sources, linenoise))
• filtered_raw = raw.copy().filter(55, 65) to filter around line noise (because of this ref)
• ICA().fit(filtered_raw)`
• compute PSD of the data in component space dot(ica.components, original_raw)
• find the component carying the line noise: bad
• remove it from the data: ica.apply(original_raw, exclude=[bad])
• check the PSD of this cleaned data

This works ~ well for low sampling frequency with or without decim>1, but gets unstable for high sampling frequency when we use a decim>1.

This could be due to aliasing, so I tried to shuffle the data just prior to fitting the ICA

times = np.arange(raw.n_times)
np.random.shuffle(times)
raw._data = raw._data[:, times]

the results are a bit better, but still pretty unstable. In the notebook, you can see that it works 50% of the time

Any idea what I do wrong?

[mmagnuski]

And just by the way: do you think implementing eeglab’s cleanline in mne would be a worthwhile investment?

[kingjr]

I’m closing it for now. Thank you all for your comments and help.