def read_raw_data(run_index, hcp_params, lfreq=0.5, hfreq=60):
raw = hcp.read_raw(run_index=run_index, **hcp_params)
raw.load_data()
# apply ref channel correction and drop ref channels
# preproc.apply_ref_correction(raw)
annots = hcp.read_annot(run_index=run_index, **hcp_params)
# construct MNE annotations
bad_seg = (annots['segments']['all']) / raw.info['sfreq']
annotations = mne.Annotations(
bad_seg[:, 0], (bad_seg[:, 1] - bad_seg[:, 0]),
description='bad')
raw.annotations = annotations
raw.info['bads'].extend(annots['channels']['all'])
raw.pick_types(meg=True, ref_meg=False)
raw.filter(lfreq, None, method='iir',
iir_params=dict(order=4, ftype='butter'), n_jobs=1)
raw.filter(None, hfreq, method='iir',
iir_params=dict(order=4, ftype='butter'), n_jobs=1)
# read ICA and remove EOG ECG
# note that the HCP ICA assumes that bad channels have already been removed
ica_mat = hcp.read_ica(run_index=run_index, **hcp_params)
# We will select the brain ICs only
exclude = annots['ica']['ecg_eog_ic']
preproc.apply_ica_hcp(raw, ica_mat=ica_mat, exclude=exclude)
return raw
def test_apply_ica():
"""Test ICA application."""
raw = hcp.read_raw(data_type='rest', verbose='error', **hcp_params)
annots = hcp.read_annot(data_type='rest', **hcp_params)
# construct MNE annotations
bad_seg = (annots['segments']['all']) / raw.info['sfreq']
annotations = mne.Annotations(bad_seg[:, 0],
(bad_seg[:, 1] - bad_seg[:, 0]),
description='bad')
raw.annotations = annotations
raw.info['bads'].extend(annots['channels']['all'])
ica_mat = hcp.read_ica(data_type='rest', **hcp_params)
exclude = [
ii for ii in range(annots['ica']['total_ic_number'][0])
if ii not in annots['ica']['brain_ic_vs']
]
assert_raises(RuntimeError,
apply_ica_hcp,
raw,
ica_mat=ica_mat,
exclude=exclude)
# XXX right now this is just a smoke test, should really check some
# values...
with warnings.catch_warnings(record=True):
raw.crop(0, 1).load_data()
apply_ica_hcp(raw, ica_mat=ica_mat, exclude=exclude)
def test_apply_ica():
"""Test ICA application."""
raw = hcp.read_raw(data_type='rest', verbose='error', **hcp_params)
annots = hcp.read_annot(data_type='rest', **hcp_params)
# construct MNE annotations
bad_seg = (annots['segments']['all']) / raw.info['sfreq']
annotations = mne.Annotations(
bad_seg[:, 0], (bad_seg[:, 1] - bad_seg[:, 0]), description='bad')
raw.annotations = annotations
raw.info['bads'].extend(annots['channels']['all'])
ica_mat = hcp.read_ica(data_type='rest', **hcp_params)
exclude = [ii for ii in range(annots['ica']['total_ic_number'][0])
if ii not in annots['ica']['brain_ic_vs']]
assert_raises(RuntimeError, apply_ica_hcp, raw, ica_mat=ica_mat,
exclude=exclude)
# XXX right now this is just a smoke test, should really check some
# values...
with warnings.catch_warnings(record=True):
raw.crop(0, 1).load_data()
apply_ica_hcp(raw, ica_mat=ica_mat, exclude=exclude)
bad_seg[:, 0], (bad_seg[:, 1] - bad_seg[:, 0]),
description='bad')
raw.annotations = annotations
raw.info['bads'].extend(annots['channels']['all'])
raw.pick_types(meg=True, ref_meg=False)
# Note: MNE complains on Python 2.7
raw.filter(0.50, None, method='iir',
iir_params=dict(order=4, ftype='butter'), n_jobs=1)
raw.filter(None, 60, method='iir',
iir_params=dict(order=4, ftype='butter'), n_jobs=1)
# read ICA and remove EOG ECG
# note that the HCP ICA assumes that bad channels have already been removed
ica_mat = hcp.read_ica(run_index=run_index, **hcp_params)
# We will select the brain ICs only
exclude = annots['ica']['ecg_eog_ic']
preproc.apply_ica_hcp(raw, ica_mat=ica_mat, exclude=exclude)
# now we can epoch
events = np.sort(events, 0)
epochs = mne.Epochs(raw, events=events[events[:, 2] == 1],
event_id=event_id, tmin=tmin, tmax=tmax,
reject=None, baseline=baseline, decim=decim,
preload=True)
evoked = epochs.average()
# now we need to add back out channels for comparison across runs.
evoked = preproc.interpolate_missing(evoked, **hcp_params)
def reprocess_the_data_from_scratch(all_events, event_id, tmin, tmax, baseline,
decim):
# now we can go ahead
evokeds = list()
all_epochs = list()
for run_index, events in zip([0, 1], all_events):
raw = hcp.read_raw(run_index=run_index, **hcp_params)
raw.load_data()
# apply ref channel correction and drop ref channels
# preproc.apply_ref_correction(raw)
annots = hcp.read_annot(run_index=run_index, **hcp_params)
# construct MNE annotations
bad_seg = (annots['segments']['all']) / raw.info['sfreq']
annotations = mne.Annotations(bad_seg[:, 0],
(bad_seg[:, 1] - bad_seg[:, 0]),
description='bad')
raw.annotations = annotations
raw.info['bads'].extend(annots['channels']['all'])
raw.pick_types(meg=True, ref_meg=False)
# Note: MNE complains on Python 2.7
raw.filter(0.50,
None,
method='iir',
iir_params=dict(order=4, ftype='butter'),
n_jobs=1)
raw.filter(None,
60,
method='iir',
iir_params=dict(order=4, ftype='butter'),
n_jobs=1)
# read ICA and remove EOG ECG
# note that the HCP ICA assumes that bad channels have already been removed
ica_mat = hcp.read_ica(run_index=run_index, **hcp_params)
# We will select the brain ICs only
exclude = annots['ica']['ecg_eog_ic']
preproc.apply_ica_hcp(raw, ica_mat=ica_mat, exclude=exclude)
# now we can epoch
events = np.sort(events, 0)
epochs = mne.Epochs(raw,
events=events[events[:, 2] == 1],
event_id=event_id,
tmin=tmin,
tmax=tmax,
reject=None,
baseline=baseline,
decim=decim,
preload=True)
all_epochs.append(epochs)
evoked = epochs.average()
# now we need to add back out channels for comparison across runs.
evoked = preproc.interpolate_missing(evoked, **hcp_params)
evokeds.append(evoked)
return all_epochs, evokeds
bad_seg[:, 0], (bad_seg[:, 1] - bad_seg[:, 0]),
description='bad')
raw.annotations = annotations
raw.info['bads'].extend(annots['channels']['all'])
raw.pick_types(meg=True, ref_meg=False)
# Note: MNE complains on Python 2.7
raw.filter(0.50, None, method='iir',
iir_params=dict(order=4, ftype='butter'), n_jobs=1)
raw.filter(None, 60, method='iir',
iir_params=dict(order=4, ftype='butter'), n_jobs=1)
# read ICA and remove EOG ECG
# note that the HCP ICA assumes that bad channels have already been removed
ica_mat = hcp.read_ica(run_index=run_index, **hcp_params)
# We will select the brain ICs only
exclude = annots['ica']['ecg_eog_ic']
preproc.apply_ica_hcp(raw, ica_mat=ica_mat, exclude=exclude)
# now we can epoch
events = np.sort(events, 0)
epochs = mne.Epochs(raw, events=events[events[:, 2] == 1],
event_id=event_id, tmin=tmin, tmax=tmax,
reject=None, baseline=baseline, decim=decim,
preload=True)
evoked = epochs.average()
# now we need to add back out channels for comparison across runs.
evoked = preproc.interpolate_missing(evoked, **hcp_params)