<p><span style="padding: 3px 10px; border-radius: 5px; color: #ffffff; font-weight: bold; display: inline-block; background-color: #ff0000;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;External Email - Use Caution&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span></p><p></p><div dir="ltr">It&#39;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[&#39;src&#39;], 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 &lt;<a href="mailto:rene.andrade@edu.uah.es">rene.andrade@edu.uah.es</a>&gt; 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(&#39;pyvista&#39;)<br>
trans=&#39;fsaverage&#39;<br>
subject=&#39;fsaverage&#39;<br>
src=&#39;/usr/local/freesurfer/subjects/fsaverage/fsaverage-oct6-src.fif&#39;<br>
raw = mne.io.read_raw_bdf(&#39;/home/andraderenew/Downloads/meditation_arnaud_delorme/sub-001_ses-01_eeg_sub-001_ses-01_task-meditation_eeg.bdf&#39;, 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=&#39;average&#39;, copy=True, projection=True, ch_type=&#39;auto&#39;, verbose=None)<br>
<br>
cov = mne.read_cov(&#39;/usr/local/freesurfer/subjects/fsaverage/data_eeg_meditation_subj1_sess1/fsaverage-5120-5120-5120-cov.fif&#39;)<br>
<br>
fwd_sol = mne.read_forward_solution(&#39;/usr/local/freesurfer/subjects/fsaverage/data_eeg_meditation_subj1_sess1/fsaverage-5120-5120-5120-fwd.fif&#39;)<br>
<br>
inv = mne.minimum_norm.read_inverse_operator(&#39;/usr/local/freesurfer/subjects/fsaverage/data_eeg_meditation_subj1_sess1/fsaverage-5120-5120-5120-inv.fif&#39;)<br>
<br>
subjects_dir = &#39;/usr/local/freesurfer/subjects/&#39;<br>
<br>
<br>
<br>
snr = 3.<br>
lambda2 = 1. / snr ** 2<br>
<br>
surfer_kwargs = dict(<br>
    hemi=&#39;lh&#39;, subjects_dir=subjects_dir,<br>
    clim=dict(kind=&#39;value&#39;, lims=[8, 12, 15]), views=&#39;lateral&#39;,<br>
    initial_time=0.09, time_unit=&#39;s&#39;, size=(800, 800),<br>
    smoothing_steps=5)<br>
<br>
<br>
surfer_kwargs[&#39;clim&#39;].update(kind=&#39;percent&#39;, lims=[99, 99.9, 99.99])<br>
for mi, method in enumerate([&#39;MNE&#39;, &#39;dSPM&#39;, &#39;sLORETA&#39;, &#39;eLORETA&#39;]):<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=&#39;lh&#39;, 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=&#39;lh&#39;, color=&#39;blue&#39;)<br>
    brain.add_text(0.1, 0.9, method, &#39;title&#39;, font_size=20)<br>
    print(stc)<br>
 #  lh_coordinates = src[0][&#39;rr&#39;][stc.lh_vertno]<br>
 #  lh_data = stc.lh_data<br>
 #   input()<br>
<br>
<br>
<br>
    labels = mne.read_labels_from_annot(subject, &#39;aparc&#39;,<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=&#39;normal&#39;,<br>
                                return_generator=True)<br>
    label_ts = mne.extract_label_time_course(<br>
        stcs, labels, inv[&#39;src&#39;], return_generator=True)<br>
    corr = mne.connectivity.envelope_correlation(label_ts, verbose=True)<br>
<br>
    # let&#39;s plot this matrix<br>
    fig, ax = plt.subplots(figsize=(4, 4))<br>
    ax.imshow(corr, cmap=&#39;viridis&#39;, 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[&#39;src&#39;][0][&#39;vertno&#39;], hemi=&#39;lh&#39;) +<br>
                       mne.Label(inv[&#39;src&#39;][1][&#39;vertno&#39;], hemi=&#39;rh&#39;))<br>
    brain = stc.plot(<br>
        clim=dict(kind=&#39;percent&#39;, lims=[75, 85, 95]), colormap=&#39;gnuplot&#39;,<br>
        subjects_dir=subjects_dir, views=&#39;dorsal&#39;, hemi=&#39;both&#39;,<br>
        smoothing_steps=25, time_label=&#39;Beta band&#39;)<br>
<br>
    del stc<br>
<br>
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</blockquote></div>