def run_ica(name, save_dir, lowpass, overwrite):
ica_name = name + filter_string(lowpass) + '-ica.fif'
ica_path = join(save_dir, ica_name)
if overwrite or not isfile(ica_path):
raw = io.read_filtered(name, save_dir, lowpass)
epochs = io.read_epochs(name, save_dir, lowpass)
ica = mne.preprocessing.ICA(n_components=0.95, method='fastica')
ica.fit(epochs)
eog_epochs = mne.preprocessing.create_eog_epochs(raw)
ecg_epochs = mne.preprocessing.create_ecg_epochs(raw)
eog_indices, eog_scores = ica.find_bads_eog(eog_epochs)
ecg_indices, ecg_scores = ica.find_bads_ecg(ecg_epochs)
ica.exclude += eog_indices
ica.exclude += ecg_indices
ica.save(ica_path)
else:
print('ica file: ' + ica_path + ' already exists')
def apply_ica(name, save_dir, lowpass, overwrite):
ica_epochs_name = name + filter_string(lowpass) + '-ica-epo.fif'
ica_epochs_path = join(save_dir, ica_epochs_name)
if overwrite or not isfile(ica_epochs_path):
epochs = io.read_epochs(name, save_dir, lowpass)
ica = io.read_ica(name, save_dir, lowpass)
ica_epochs = ica.apply(epochs)
ica_epochs.save(ica_epochs_path)
else:
print('ica epochs file: ' + ica_epochs_path + ' already exists')
def estimate_noise_covariance(name, save_dir, lowpass, overwrite):
covariance_name = name + filter_string(lowpass) + '-cov.fif'
covariance_path = join(save_dir, covariance_name)
if overwrite or not isfile(covariance_path):
epochs = io.read_epochs(name, save_dir, lowpass)
noise_covariance = mne.compute_covariance(epochs, n_jobs=1)
noise_covariance = mne.cov.regularize(noise_covariance, epochs.info)
mne.cov.write_cov(covariance_path, noise_covariance)
else:
print('noise covariance file: '+ covariance_path + \
' already exists')
def plot_epochs_image(name, save_dir, lowpass, subject, save_plots,
figures_path):
channel = 'MEG1812'
epochs = io.read_epochs(name, save_dir, lowpass)
picks = mne.pick_channels(epochs.info['ch_names'], [channel])
for trial_type in epochs.event_id:
epochs_image = mne.viz.plot_epochs_image(epochs[trial_type], picks)
plt.title(trial_type)
if save_plots:
save_path = join(figures_path, subject, 'epochs',
trial_type + '_' + channel + '_' + name + \
filter_string(lowpass) + '.jpg')
epochs_image[0].savefig(save_path, dpi=600)
print 'figure: ' + save_path + ' has been saved'
else:
print 'Not saving plots; set "save_plots" to "True" to save'