# In[]: Plotting
print("Ploting ERPs for Class: " + Classes +" in Condition: " + Cond )
print("with the information of Subjects: " + str(N_S_list ))
# Put all data
X_S._data=X_data
X_S.events=Y_data
X_averaged=X_S.average()
fig= plt.figure(figsize=(20,10))
axs= fig.add_axes([0.1,0.1,0.8,0.8])
# Plot Cues
if plot_cues_bool:
plt.plot([0,0],[-15,15],axes=axs,color='black')
plt.plot([0.5,0.5],[-15,15],axes=axs,color='black')
plt.plot([3,3],[-15,15],axes=axs,color='black')
# Plot ERPs
X_averaged.plot(spatial_colors = spatial_colors, picks = picks, ylim = ylim, axes = axs, xlim= [t_start , t_end])
title = "ERPs - Condition: " + Cond + " in Class" + Classes
axs.set_title(title, fontsize = fontsize)
# Save Figure
if save_bool:
Ensure_dir(save_dir)
fig.savefig(save_dir + prefix + '_' + Cond + '_' + Classes + '_' + channels + '_.png', transparent = True)
# Check and Correct event
events = Event_correction(N_S=N_S, N_E=N_B, events=events)
# replace the raw events with the new corrected events
rawdata.event = events
report['Recording_time'] = int(
np.round(rawdata.last_samp / rawdata.info['sfreq']))
# Cognitive Control
report['Ans_R'], report['Ans_W'] = Cognitive_control_check(events)
# In[] Save report
file_path = save_dir + Num_s + '/ses-0' + str(N_B)
Ensure_dir(file_path)
file_name = file_path + '/' + Num_s + '_ses-0' + str(
N_B) + '_report.pkl'
with open(file_name, 'wb') as output:
pickle.dump(report, output, pickle.HIGHEST_PROTOCOL)
# In[]:EXG
# the EXG Channels for saving
picks_eog = mne.pick_types(rawdata.info,
eeg=False,
stim=False,
include=[
'EXG1', 'EXG2', 'EXG3', 'EXG4', 'EXG5',
'EXG6', 'EXG7', 'EXG8'
])
epochsEOG = mne.Epochs(rawdata,