MNE 1.8.0 / Windows 11
Hello all, I am trying to extract the timecourses from volume and surface regions of a mixed source space using mne.extract_label_time_course. Passing the function an empty list of labels, as well as the mixed source estimate and mixed source space:
mne.extract_label_time_course(mixed_stc, src=mixed_src, labels=[])
Returns four timecourses. My mixed source space contains two surface source spaces (lh/rh cortex) and four volume source spaces (lh/rh cerebellar cortex/WM). Interestingly, when specifying one label:
surf_labels = ['superiorfrontal-rh']
adult_surf_labels = mne.read_labels_from_annot(subject='fsaverage', parc='aparc', subjects_dir=subjects_dir)
label = [label for label in surf_labels if label.name in surf_labels]
mne.extract_label_time_course(mixed_stc, src=mixed_src, labels=label)
Five timecourses are returned, and if I specify a second label then there are 6 time courses and so on.
When I do specify one label, the first timecourse that is returned is new, while the other four are the exact same timecourses that get returned without specifying any labels.
If anyone could help clarify how this is connected to my mixed source space, or recommend the best practice for extacting timecourses from surface/volume regions (I have used LCMV beamformer for source-localization) that would be much appreciated!