Added comments to jupyter notebook

notebooks/EEG pipeline demo.ipynb

@@ -9,7 +9,9 @@
  "\n",
  "It is important to note that cmp _does not_ include preprocessing, so it is expected that you have your data ready to be analyzed. \n",
  "\n",
- "For visualization purposes, we are using subject 1 from the \"VEPCON\" dataset, available at ..., however, note that non-defaced MRIs were used to create the inverse solutions, as defacing creates distortions. "
+ "For visualization purposes, we are using subject 1 from the \"VEPCON\" dataset, available at ..., however, note that non-defaced MRIs were used to create the inverse solutions, as defacing creates distortions. \n",
+ "\n",
+ "In order to use the connectivity capabilities of MNE, one has to add MNE_connectivity to the conda environment, see https://mne.tools/mne-connectivity/stable/install.html. "
  ]
  },
  {
@@ -436,7 +438,7 @@
  "with open(os.path.join(output_dir,'cmp-v3.0.2',participant_label,'eeg',participant_label+'_rtc_epo.pkl'),'rb') as f:\n",
  " rtc_epo = pickle.load(f)\n",
  " # for some reason, MNE writes label time courses as lists. convert to numpy array\n",
- " #rtc_epo['data'] = np.array(rtc_epo['data'])"
+ " rtc_epo['data'] = np.array(rtc_epo['data'])"
  ]
  },
  {
@@ -544,11 +546,21 @@
  "\n",
  "# Plot the graph using node colors from the FreeSurfer parcellation. We only\n",
  "# show the 300 strongest connections.\n",
+ "# plot will appear in separate window\n",
+ "%matplotlib qt \n",
  "mnec.viz.plot_connectivity_circle(con_res['wpli2_debiased'], label_names, n_lines=300,\n",
  " node_angles=node_angles, node_colors='r',\n",
  " title='All-to-All Connectivity left-Auditory '\n",
  " 'Condition (PLI)')"
  ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "390756d6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
  }
  ],
  "metadata": {