# on either 2D or 3D head models
# can be applied to Cohen’s D (C as done here) or
# statistical values (statscondCluster.F_obs or F_obs_plot)
# of inter-individual brain connectivity
# defining manually bad channel for viz test
epo1.info["bads"] = ["F8", "Fp2", "Cz", "O2"]
epo2.info["bads"] = ["F7", "O1"]
# Warning, threshold='auto' must be used carefully,
# it is calculated specifically for the dyad,
# and therefore does not allow comparability between different dyads.
# Visualization of inter-brain connectivity in 2D
viz.viz_2D_topomap_inter(epo1, epo2, C, threshold='auto', steps=10, lab=True)
# Visualization of inter-brain connectivity in 3D
viz.viz_3D_inter(epo1, epo2, C, threshold='auto', steps=10, lab=False)
# Visualization of intra-brain connectivity in 2D
viz.viz_2D_topomap_intra(epo1,
epo2,
C1=result_intra[0],
C2=result_intra[1],
threshold='auto',
steps=2,
lab=False)
# Visualization of intra-brain connectivity in 3D
viz.viz_3D_intra(epo1,
# turn off x ticklabels for all but the lower axes
for ax in ax1, ax2, ax3:
plt.setp(ax.get_xticklabels(), visible=False)
plt.show()
# Setting the average reference
cleaned_epochs_AR[0].set_eeg_reference('average')
cleaned_epochs_AR[1].set_eeg_reference('average')
# Saving cleaned epochs
#cleaned_epochs_AR[0].save('epochs_cleaned_pair003_a-epo.fif', overwrite = True)
#cleaned_epochs_AR[1].save('epochs_cleaned_pair003_b-epo.fif', overwrite = True)
'''
################# Remove from here
from collections import OrderedDict
#Defining frequency bands
freq_bands = {'Theta': [4, 7],
'Alpha-Low': [7.5, 11],
'Alpha-High': [11.5, 13],
'Beta': [13.5, 29.5],
'Gamma': [30, 48]}
freq_bands = OrderedDict(freq_bands)
sampling_rate = epo1.info['sfreq']
#Picking preproccessed epochs for each participant
preproc_S1 = cleaned_epochs_AR[0]