def calc_source_band_induced_power(epochs, inverse_operator, label, bands,
n_cycles):
# Compute a source estimate per frequency band
return source_band_induced_power(epochs,
inverse_operator,
bands,
label=label,
n_cycles=n_cycles,
use_fft=False,
n_jobs=1)
# picks MEG gradiometers
picks = fiff.pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=False, include=include, exclude='bads')
# Load condition 1
event_id = 1
events = events[:10] # take 10 events to keep the computation time low
# Use linear detrend to reduce any edge artifacts
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
preload=True, detrend=1)
# Compute a source estimate per frequency band
bands = dict(alpha=[9, 11], beta=[18, 22])
stcs = source_band_induced_power(epochs, inverse_operator, bands, n_cycles=2,
use_fft=False, n_jobs=1)
for b, stc in stcs.iteritems():
stc.save('induced_power_%s' % b)
###############################################################################
# plot mean power
import matplotlib.pyplot as plt
plt.plot(stcs['alpha'].times, stcs['alpha'].data.mean(axis=0), label='Alpha')
plt.plot(stcs['beta'].times, stcs['beta'].data.mean(axis=0), label='Beta')
plt.xlabel('Time (ms)')
plt.ylabel('Power')
plt.legend()
plt.title('Mean source induced power')
plt.show()
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6),
preload=True,
detrend=1)
# Compute a source estimate per frequency band
bands = dict(alpha=[9, 11], beta=[18, 22])
stcs = source_band_induced_power(epochs,
inverse_operator,
bands,
n_cycles=2,
use_fft=False,
n_jobs=1)
for b, stc in stcs.iteritems():
stc.save('induced_power_%s' % b)
###############################################################################
# plot mean power
import matplotlib.pyplot as plt
plt.plot(stcs['alpha'].times, stcs['alpha'].data.mean(axis=0), label='Alpha')
plt.plot(stcs['beta'].times, stcs['beta'].data.mean(axis=0), label='Beta')
plt.xlabel('Time (ms)')
plt.ylabel('Power')
plt.legend()
plt.title('Mean source induced power')
plt.colorbar()
plt.show()
bands = dict(alpha=[8, 12])
BP_list = []
for j, label in enumerate([labels[9], labels[10], labels[9]+labels[10]]):
for cond in epochs.event_id.keys():
stcs = source_band_induced_power(epochs[cond],
inverse_operator,
bands=bands,
label=label,
lambda2=lambda2,
method="dSPM",
baseline=(None, 0),
baseline_mode='zscore',
pca=True)
if len(label.name.split()) > 2:
l_name = label.name.split()[0][5:][:-3] + "_lh_rh"
else:
l_name = label.name[5:][:-3] + "_" + label.name[-2:]
BP_list.append("BP_%s_%s" % (cond, l_name))
exec("BP_%s_%s = stcs['alpha']" % (cond, l_name))
lambda2,
method='sLORETA',
pick_ori="normal",
return_generator=True)
stcs_slow_mt = apply_inverse_epochs(slow_epo_isi,
inverse_operator,
lambda2,
method='sLORETA',
pick_ori="normal",
return_generator=True)
# Compute a source estimate for 10-17 Hz frequency band
stcs_slow_v1_induced = source_band_induced_power(slow_epo_isi,
inverse_operator,
dict(alpha_beta=[10, 17]),
method='sLORETA',
n_cycles=2,
n_jobs=1,
use_fft=True,
label=v1_label)
stcs_slow_mt_induced = source_band_induced_power(fast_epo_isi,
inverse_operator,
dict(alpha_beta=[10, 17]),
method='sLORETA',
n_cycles=2,
n_jobs=1,
use_fft=True,
label=mt_label)
# plt.plot(stcs_slow_v1_induced['alpha_beta'].vertices[1], stcs_slow_v1_induced['alpha_beta'].data.mean(1), label='slow')
# plt.plot(stcs_fast_v1_induced['alpha_beta'].vertices[1], stcs_fast_v1_induced['alpha_beta'].data.mean(1), label='fast')
subjects_dir=subjects_dir,
freesurfer=True)
mt_label_part = mt_label.split(parts=5,
subject='Case' + subject,
subjects_dir=subjects_dir,
freesurfer=True)
psi_slow_v1_mt_all = np.zeros([len(v1_label_part), 1])
slow_v1_pow = []
for label_num_v1 in np.arange(len(v1_label_part)):
stcs_slow_v1_induced = source_band_induced_power(
slow_epo_isi,
inverse_operator,
dict(alpha_beta=[8, 17]),
method='sLORETA',
n_cycles=2,
label=v1_label_part[label_num_v1])
#sort array of vertices in descending order according power
pow_idx_v1 = stcs_slow_v1_induced['alpha_beta'].data.mean(1).argsort()
vert_descend_v1 = stcs_slow_v1_induced['alpha_beta'].vertices[1][
pow_idx_v1[::-1]]
#take only 20 vertices with max power
v1_label.vertices = vert_descend_v1[0]
psi_slow_v1_mt = np.zeros([len(mt_label_part), 1])
for label_num_mt in np.arange(len(mt_label_part)):
stcs_slow_mt_induced = source_band_induced_power(
epochs.crop(None, 1.1)
epochs.resample(200)
pow_ent_left = []
pow_ent_right = []
pow_ctl_left = []
for i in range(len(epochs["ent_left"])):
print "Trial %d of %d" % (i + 1, len(epochs["ent_left"]) + 1)
tmp_pow = source_band_induced_power(epochs["ent_left"][i],
inverse_operator,
bands=dict(alpha=[8, 12]),
label=labels_occ[1],
lambda2=lambda2,
method=method,
n_cycles=4,
baseline=(None, 0),
baseline_mode="zscore",
pca=True,
n_jobs=n_jobs)
pow_ent_left.append(tmp_pow["alpha"])
for i in range(len(epochs["ent_right"])):
print "Trial %d of %d" % (i + 1, len(epochs["ent_right"]) + 1)
tmp_pow = source_band_induced_power(epochs["ent_right"][i],
inverse_operator,
bands=dict(alpha=[8, 12]),
label=labels_occ[1],
lambda2=lambda2,
method=method,
