# Use 'MEG 2343' as seed
seed_ch = 'MEG 2343'
picks_ch_names = [raw.ch_names[i] for i in picks]
# Create seed-target indices for connectivity computation
seed = picks_ch_names.index(seed_ch)
targets = np.arange(len(picks))
indices = seed_target_indices(seed, targets)
# Define wavelet frequencies and number of cycles
cwt_freqs = np.arange(7, 30, 2)
cwt_n_cycles = cwt_freqs / 7.
# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info['sfreq'] # the sampling frequency
con, freqs, times, _, _ = spectral_connectivity(
epochs, indices=indices,
method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,
cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles, n_jobs=1)
# Mark the seed channel with a value of 1.0, so we can see it in the plot
con[np.where(indices[1] == seed)] = 1.0
# Show topography of connectivity from seed
title = 'WPLI2 - Visual - Seed %s' % seed_ch
layout = mne.find_layout(epochs.info, 'meg') # use full layout
tfr = AverageTFR(epochs.info, con, times, freqs, len(epochs))
tfr.plot_topo(fig_facecolor='w', font_color='k', border='k')
# Define wavelet frequencies and number of cycles
cwt_freqs = np.arange(7, 30, 2)
cwt_n_cycles = cwt_freqs / 7.
# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info['sfreq'] # the sampling frequency
con, freqs, times, _, _ = spectral_connectivity(epochs,
indices=indices,
method='wpli2_debiased',
mode='cwt_morlet',
sfreq=sfreq,
cwt_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles,
n_jobs=1)
# Mark the seed channel with a value of 1.0, so we can see it in the plot
con[np.where(indices[1] == seed)] = 1.0
# Show topography of connectivity from seed
title = 'WPLI2 - Visual - Seed %s' % seed_ch
layout = mne.find_layout(epochs.info, 'meg') # use full layout
tfr = AverageTFR(epochs.info, con, times, freqs, len(epochs))
tfr.plot_topo(fig_facecolor='w', font_color='k', border='k')
# %%
# References
# ----------
# .. footbibliography::
def wpli_analysis_time(subjects, log):
conds = ['lon', 'sho']
roi_cons = {}
roi = rois['f']
spatial_con = mne.channels.read_ch_connectivity('biosemi64')
for ix_s, s in enumerate(subjects):
print('subject {} of {}' .format(ix_s+1, len(subjects)))
for ix_c, c in enumerate(conds):
filename = op.join(study_path, 'results', 'wpli', 'over_time', '{}_{}_wpli.npz' .format(s, c))
dat = np.load(filename)
info = dat['info'].item()
# Create results matrices
if ix_s == 0:
roi_cons[c] = np.empty((len(subjects), dat['con'].shape[0], dat['con'].shape[-2], dat['con'].shape[-1]))
# Get ROI connectivity
# roi_ixs = [ix for ix, ch in enumerate(info['ch_names']) if ch in roi]
# roi_con = np.mean(dat['con'][roi_ixs, :, :, :], axis=0)
roi_ixs = 33
roi_con = dat['con'][roi_ixs, :, :, :]
roi_con[roi_ixs, :, :] = 1.0
roi_cons[c][ix_s, :, :, :] = roi_con.copy()
avg_con = [np.mean(roi_cons[c], axis=0) for c in conds]
for ix_c, c in enumerate(conds):
tfr = AverageTFR(info, avg_con[ix_c], dat['times'], dat['freqs'], len(subjects))
tfr.plot_topo(fig_facecolor='w', font_color='k', border='k', vmin=0, vmax=0.5, cmap='viridis', title=c)
s = 12
for c in conds:
tfr = AverageTFR(info, roi_cons[c][s, :, :, :], dat['times'], dat['freqs'], len(subjects))
tfr.plot_topo(fig_facecolor='w', font_color='k', border='k', vmin=0, vmax=1, cmap='viridis', title=c)
# Stats
test_con = [roi_cons[c][:, 19, :, :] for c in conds]
#threshold = None
threshold = dict(start=0, step=0.2)
T_obs, clusters, cluster_p_values, H0 = \
permutation_cluster_test([test_con[0], test_con[1]],
n_permutations=1000, threshold=threshold, tail=0)
times = dat['times']
times *= 1e3
freqs = dat['freqs']
fig, ax = plt.subplots(1)
T_obs_plot = np.nan * np.ones_like(T_obs)
for c, p_val in zip(clusters, cluster_p_values):
if p_val <= 0.05:
T_obs_plot[c] = T_obs[c]
ax.imshow(T_obs,
extent=[times[0], times[-1], freqs[0], freqs[-1]],
aspect='auto', origin='lower', cmap='gray')
ax.imshow(T_obs_plot,
extent=[times[0], times[-1], freqs[0], freqs[-1]],
aspect='auto', origin='lower', cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Frequency (Hz)')
plt.title('ROI Connectivity')