got stuck in mne.preprocessing.find_bad_channels_maxwell()

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1

Hi.
I ran the code at the bottom and got an error code.
I’m totally new to eeg data analysis, so I really have no idea where the error came from.
I could only guess there is something wrong with data itself, like wrong data format or wrong data info.

Can anyone help out to fix it or just guess what the problem is?
I’d appreciate it so much.

auto_noisy_chs, auto_flat_chs, auto_scores = mne.preprocessing.find_bad_channels_maxwell(
    raw, return_scores=True, verbose=True)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[36], line 1
----> 1 auto_noisy_chs, auto_flat_chs, auto_scores = mne.preprocessing.find_bad_channels_maxwell(
      2     raw_prep, return_scores=True, verbose=True)

File :10, in find_bad_channels_maxwell(raw, limit, duration, min_count, return_scores, origin, int_order, ext_order, calibration, cross_talk, coord_frame, regularize, ignore_ref, bad_condition, head_pos, mag_scale, skip_by_annotation, h_freq, extended_proj, verbose)

File...\mne\preprocessing\maxwell.py:2237, in find_bad_channels_maxwell(***failed resolving arguments***)
   2234 min_count = min(_ensure_int(min_count, 'min_count'), len(starts))
   2235 logger.info('Scanning for bad channels in %d interval%s (%0.1f sec) ...'
   2236             % (len(starts), _pl(starts), step / raw.info['sfreq']))
-> 2237 params = _prep_maxwell_filter(
   2238     raw, skip_by_annotation=[],  # already accounted for
   2239     origin=origin, int_order=int_order, ext_order=ext_order,
   2240     calibration=calibration, cross_talk=cross_talk,
   2241     coord_frame=coord_frame, regularize=regularize,
   2242     ignore_ref=ignore_ref, bad_condition=bad_condition, head_pos=head_pos,
   2243     mag_scale=mag_scale, extended_proj=extended_proj)
   2244 del origin, int_order, ext_order, calibration, cross_talk, coord_frame
   2245 del regularize, ignore_ref, bad_condition, head_pos, mag_scale

File :12, in _prep_maxwell_filter(raw, origin, int_order, ext_order, calibration, cross_talk, st_duration, st_correlation, coord_frame, destination, regularize, ignore_ref, bad_condition, head_pos, st_fixed, st_only, mag_scale, skip_by_annotation, extended_proj, reconstruct, verbose)

File ...\mne\preprocessing\maxwell.py:412, in _prep_maxwell_filter(raw, origin, int_order, ext_order, calibration, cross_talk, st_duration, st_correlation, coord_frame, destination, regularize, ignore_ref, bad_condition, head_pos, st_fixed, st_only, mag_scale, skip_by_annotation, extended_proj, reconstruct, verbose)
...
--> 493     raise ValueError('No channels match the selection.')
    494 n_unique = len(np.unique(np.arange(len(info['ch_names']))[sel]))
    495 if n_unique != len(sel):

ValueError: No channels match the selection.

image

2

This looks like an EEG recording. The maxfilter algorithm only works for MEG data. The algorithm first selects all the MEG channels, fails to find any as all channels are marked as EEG or EOG, and thus gives this unhelpful error message.

To automatically detect bad channels in EEG data, I recommend taking a look at the autoreject package, in particular the RANSAC algorithm it implements.

3 Likes
3

We should improve the error message :disguised_face:

1 Like
4

Thank you for the solutions. I was looking for not time consuming solutions to detect noisy or flat channels, and maxwell function was not the solution in my case. I should try RANSAC as you said. Thank you so much.

1 Like