def runICA(subjectID):
jumpAmplitudes = dict(grad=400e-12, mag=6e-12)
subject = 'dh{:#02d}a'.format(subjectID)
subjectPath = data_path + '/MEG_mc_hp004/' + subject + '/block'
for block in ['1', '2']:
outfilename = subjectPath + block + '_ica.fif'
raw_fname = subjectPath + block + '.fif'
if os.path.exists(raw_fname):
raw = Raw(raw_fname, preload=True)
raw.info['bads'] = badChanLibrary[subjectID][int(block)]
MEG_channels = mne.fiff.pick_types(raw.info,
meg=True,
eeg=False,
eog=False,
stim=False)
ica = ICA(n_components=0.99,
n_pca_components=64,
max_pca_components=100,
noise_cov=None,
random_state=17259)
# decompose sources for raw data
ica.fit(raw, picks=MEG_channels, reject=jumpAmplitudes, tstep=1.0)
# Save ICA results for diagnosis and reconstruction
ica.save(outfilename)
else:
print(raw_fname + ' does not exist. Skipping ICA.')
