def task_forward():
"""Step 07: Compute forward operators for each subject."""
for subject in subjects:
epo_fname = fname.epo(subject=subject)
fwd_fname = fname.fwd(subject=subject)
src_fname = fname.src(subject=subject)
yield dict(
name=subject,
task_dep=['fsaverage_src'],
file_dep=[fname.fsaverage_src, epo_fname, '07_forward.py'],
targets=[fwd_fname, src_fname],
actions=['python 07_forward.py %s' % subject],
)
def task_csd():
"""Step 05: Compute cross-spectral density (CSD) matrices"""
for subject in subjects:
epo_fname = fname.epo(subject=subject)
csd_fnames = [fname.csd(subject=subject, condition=cond)
for cond in conditions + ['baseline']]
yield dict(
name=subject,
task_dep=['epochs'],
file_dep=[epo_fname, '05_csd.py'],
targets=csd_fnames,
actions=['python 05_csd.py %s' % subject],
)
def task_figures():
"""Make all figures. Each figure is a sub-task."""
# Make figure 1: plot of the CSD matrices.
yield dict(
name='csd',
task_dep=['connectivity_stats'],
file_dep=[fname.epo(subject=subjects[0]),
fname.csd(subject=subjects[0], condition='face')],
targets=['../paper/figures/csd.pdf'],
actions=['python figure_csd.py'],
)
# Make figure 2: plot of the source space and forward model.
yield dict(
name='forward',
file_dep=[fname.fwd(subject=subjects[0]),
fname.fwd_r(subject=subjects[0]),
fname.trans(subject=subjects[0])],
targets=['../paper/figures/forward1.png',
'../paper/figures/forward2.png'],
actions=['python figure_forward.py'],
)
# Make figure 3: grand average power maps.
file_dep = [fname.ga_power_hemi(condition=cond, hemi='lh') for cond in conditions]
file_dep += [fname.ga_power_hemi(condition=cond, hemi='rh') for cond in conditions]
targets = ['../paper/figures/power_face_lh.png',
'../paper/figures/power_face_rh.png',
'../paper/figures/power_scrambled_lh.png',
'../paper/figures/power_scrambled_rh.png']
targets += ['../paper/figures/power_contrast_%s-%s-lh.png' % (freq[0], freq[1]) for freq in freq_bands]
yield dict(
name='power',
file_dep=file_dep,
targets=targets,
actions=['python figure_power.py'],
)
# Make figure 4: plot of the functional connectivity.
yield dict(
name='connectivity',
file_dep=[fname.ga_con(condition='pruned'),
fname.ga_con(condition='parcelled')],
targets=['../paper/figures/degree_lh.png',
'../paper/figures/degree_rh.png',
'../paper/figures/squircle.pdf'],
actions=['python figure_connectivity.py'],
)
def task_epochs():
"""Step 04: Cut the data into epochs"""
for subject in subjects:
filt_fnames = [fname.filt(subject=subject, run=run,
fmin=bandpass_fmin, fmax=bandpass_fmax)
for run in range(1, 7)]
ica_fname = fname.ica(subject=subject)
epo_fname = fname.epo(subject=subject)
yield dict(
name=subject,
task_dep=['ica'],
file_dep=filt_fnames + [ica_fname, '04_epochs.py'],
targets=[epo_fname],
actions=['python 04_epochs.py %s' % subject],
)
from config import (fname, n_jobs, csd_tmin, csd_tmax, freq_bands, conditions,
get_report, save_report)
# Be verbose
mne.set_log_level('INFO')
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('subject', metavar='sub###', help='The subject to process')
args = parser.parse_args()
subject = args.subject
print('Processing subject:', subject)
# Read the epochs
print('Reading epochs...')
epochs = mne.read_epochs(fname.epo(subject=subject))
report = get_report(subject)
# Suppress warning about wavelet length.
warnings.simplefilter('ignore')
# Individual frequencies to estimate the CSD for
fmin = freq_bands[0][0]
fmax = freq_bands[-1][1]
frequencies = np.arange(fmin, fmax + 1, 2)
# Compute CSD matrices for each frequency and each condition.
for condition in conditions:
print('Condition:', condition)
# Remove the mean during the time interval for which we compute the CSD
preload=True)
# Save evoked plot to the report
with mne.open_report(fname.report(subject=subject)) as report:
report.add_figs_to_section([epochs.average().plot(show=False)],
['Evoked without ICA'],
section='Sensor-level',
replace=True)
report.save(fname.report_html(subject=subject), overwrite=True)
# Apply ICA to the epochs, dropping components that correlate with ECG and EOG
ica.apply(epochs)
# Drop epochs that have too large signals (most likely due to the subject
# moving or muscle artifacts)
epochs.drop_bad(reject)
print(' Dropped %0.1f%% of epochs' % (epochs.drop_log_stats(), ))
print(' Writing to disk')
epochs.save(fname.epo(subject=subject))
# Save evoked plot to report
with mne.open_report(fname.report(subject=subject)) as report:
report.add_figs_to_section([epochs.average().plot(show=False)],
['Evoked with ICA'],
section='Sensor-level',
replace=True)
report.save(fname.report_html(subject=subject),
overwrite=True,
open_browser=False)
from matplotlib import pyplot as plt
import mne
from mne.time_frequency import read_csd, pick_channels_csd
from config import fname, subjects, freq_bands
info = mne.io.read_info(fname.epo(subject=subjects[0]))
grads = [info['ch_names'][ch] for ch in mne.pick_types(info, meg='grad')]
csd = read_csd(fname.csd(subject=subjects[0], condition='face'))
csd = pick_channels_csd(csd, grads)
csd = csd.mean([f[0] for f in freq_bands], [f[1] for f in freq_bands])
# Plot theta, alpha, low beta
csd[:3].plot(info, n_cols=3, show=False)
plt.savefig('../paper/figures/csd1.pdf', bbox_inches='tight')
# Plot high beta 1, high beta 2 and low gamma
csd[3:].plot(info, n_cols=3, show=False)
plt.savefig('../paper/figures/csd2.pdf', bbox_inches='tight')
