<p><span style="padding: 3px 10px; border-radius: 5px; color: #ffffff; font-weight: bold; display: inline-block; background-color: #ff0000;"> External Email - Use Caution </span></p><p></p><div dir="ltr">It's tough to know where the error is occurring without the traceback, but I suspect it comes from:<div><br></div><div> label_ts = mne.extract_label_time_course(<br> stcs, labels, inv['src'], return_generator=True)<br></div><div><br></div><div>You can add `allow_missing=True`, see documentation <a href="https://mne.tools/stable/generated/mne.extract_label_time_course.html">here</a>.</div><div><br></div><div>Eric</div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, May 1, 2020 at 11:42 AM Andrade Rey René <<a href="mailto:rene.andrade@edu.uah.es">rene.andrade@edu.uah.es</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"> External Email - Use Caution <br>
<br>
Dear experts:<br>
I have the issue that I put in the subject of this email. The error occurs when trying to execute this line. <br>
<br>
<br>
<br>
corr = mne.connectivity.envelope_correlation(label_ts, verbose=True)<br>
<br>
<br>
<br>
Below is the whole script. <br>
<br>
Sincerely, <br>
Andrade. <br>
<br>
<br>
import mne<br>
import os<br>
import numpy as np<br>
import matplotlib.pyplot as plt<br>
from mne.minimum_norm import make_inverse_operator, apply_inverse<br>
mne.viz.set_3d_backend('pyvista')<br>
trans='fsaverage'<br>
subject='fsaverage'<br>
src='/usr/local/freesurfer/subjects/fsaverage/fsaverage-oct6-src.fif'<br>
raw = mne.io.read_raw_bdf('/home/andraderenew/Downloads/meditation_arnaud_delorme/sub-001_ses-01_eeg_sub-001_ses-01_task-meditation_eeg.bdf', preload=True)<br>
events = mne.find_events(raw, stim_channel=None)<br>
epochs = mne.Epochs(raw, events, preload=True)<br>
evoked = epochs.average()<br>
mne.set_eeg_reference(evoked, ref_channels='average', copy=True, projection=True, ch_type='auto', verbose=None)<br>
<br>
cov = mne.read_cov('/usr/local/freesurfer/subjects/fsaverage/data_eeg_meditation_subj1_sess1/fsaverage-5120-5120-5120-cov.fif')<br>
<br>
fwd_sol = mne.read_forward_solution('/usr/local/freesurfer/subjects/fsaverage/data_eeg_meditation_subj1_sess1/fsaverage-5120-5120-5120-fwd.fif')<br>
<br>
inv = mne.minimum_norm.read_inverse_operator('/usr/local/freesurfer/subjects/fsaverage/data_eeg_meditation_subj1_sess1/fsaverage-5120-5120-5120-inv.fif')<br>
<br>
subjects_dir = '/usr/local/freesurfer/subjects/'<br>
<br>
<br>
<br>
snr = 3.<br>
lambda2 = 1. / snr ** 2<br>
<br>
surfer_kwargs = dict(<br>
hemi='lh', subjects_dir=subjects_dir,<br>
clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',<br>
initial_time=0.09, time_unit='s', size=(800, 800),<br>
smoothing_steps=5)<br>
<br>
<br>
surfer_kwargs['clim'].update(kind='percent', lims=[99, 99.9, 99.99])<br>
for mi, method in enumerate(['MNE', 'dSPM', 'sLORETA', 'eLORETA']):<br>
stc = apply_inverse(evoked, inv, lambda2,<br>
method=method, pick_ori=None,<br>
verbose=True)<br>
peak_vertex, peak_time = stc.get_peak(hemi='lh', vert_as_index=True,<br>
time_as_index=True)<br>
<br>
peak_vertex_surf = stc.lh_vertno[peak_vertex]<br>
<br>
peak_value = stc.lh_data[peak_vertex, peak_time]<br>
brain = stc.plot(figure=mi, **surfer_kwargs)<br>
brain.add_foci(peak_vertex_surf, coords_as_verts=True, hemi='lh', color='blue')<br>
brain.add_text(0.1, 0.9, method, 'title', font_size=20)<br>
print(stc)<br>
# lh_coordinates = src[0]['rr'][stc.lh_vertno]<br>
# lh_data = stc.lh_data<br>
# input()<br>
<br>
<br>
<br>
labels = mne.read_labels_from_annot(subject, 'aparc',<br>
subjects_dir=subjects_dir)<br>
epochs.apply_hilbert() # faster to apply in sensor space<br>
stcs = mne.minimum_norm.apply_inverse_epochs(epochs, inv, lambda2=1. / 9., pick_ori='normal',<br>
return_generator=True)<br>
label_ts = mne.extract_label_time_course(<br>
stcs, labels, inv['src'], return_generator=True)<br>
corr = mne.connectivity.envelope_correlation(label_ts, verbose=True)<br>
<br>
# let's plot this matrix<br>
fig, ax = plt.subplots(figsize=(4, 4))<br>
ax.imshow(corr, cmap='viridis', clim=np.percentile(corr, [5, 95]))<br>
fig.tight_layout()<br>
input()<br>
<br>
threshold_prop = 0.15 # percentage of strongest edges to keep in the graph<br>
degree = mne.connectivity.degree(corr, threshold_prop=threshold_prop)<br>
stc = mne.labels_to_stc(labels, degree)<br>
stc = stc.in_label(mne.Label(inv['src'][0]['vertno'], hemi='lh') +<br>
mne.Label(inv['src'][1]['vertno'], hemi='rh'))<br>
brain = stc.plot(<br>
clim=dict(kind='percent', lims=[75, 85, 95]), colormap='gnuplot',<br>
subjects_dir=subjects_dir, views='dorsal', hemi='both',<br>
smoothing_steps=25, time_label='Beta band')<br>
<br>
del stc<br>
<br>
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</blockquote></div>