I'm analyzing MEG data using python mne. To automatically detect bad epochs I want to use the autoreject package which is based on scikit-learn (http://autoreject.github.io/).
I have created my epochs and adapted the code from the example given on the autoreject page, so it looks like this:
n_interpolates = np.array([1, 4, 32])
consensus_percs = np.linspace(0, 1.0, 11)
picks = mne.pick_types(raw.info, meg='mag', stim=False, include=[], exclude=[])
ar = AutoReject(n_interpolates, consensus_percs, picks=picks,
thresh_method='random_search', random_state=42)
epochs_clean = ar.fit_transform(epochs)
However, if I run the last line to apply autoreject to my epochs, I get the following error message:
_setup_dots() missing 1 required positional argument: 'ch_type'
The full traceback is here:
> epochs_clean = ar.fit_transform(epochs) 105 coil definitions read Traceback (most recent call last):
File "<ipython-input-57-ae1b953bbd77>", line 1, in <module>
epochs_clean = ar.fit_transform(epochs)
File "/home/xxx/.local/lib/python3.8/site-packages/autoreject/autoreject.py", line 1110, in fit_transform
return self.fit(epochs).transform(epochs, return_log=return_log)
File "/home/xxx/.local/lib/python3.8/site-packages/autoreject/autoreject.py", line 960, in fit
self.dots = _compute_dots(this_info)
File "/home/xxx/.local/lib/python3.8/site-packages/autoreject/utils.py", line 426, in _compute_dots
int_rad, noise, lut_fun, n_fact = _setup_dots(mode, coils, 'meg')
TypeError: _setup_dots() missing 1 required positional argument: 'ch_type'