def get_config(subject: Optional[str] = None,
session: Optional[str] = None) -> BunchConst:
cfg = BunchConst(
process_er=config.process_er,
runs=config.get_runs(subject=subject),
use_maxwell_filter=config.use_maxwell_filter,
proc=config.proc,
task=config.get_task(),
datatype=config.get_datatype(),
acq=config.acq,
rec=config.rec,
space=config.space,
bids_root=config.get_bids_root(),
deriv_root=config.get_deriv_root(),
l_freq=config.l_freq,
h_freq=config.h_freq,
l_trans_bandwidth=config.l_trans_bandwidth,
h_trans_bandwidth=config.h_trans_bandwidth,
resample_sfreq=config.resample_sfreq,
crop_runs=config.crop_runs,
interactive=config.interactive,
rename_events=config.rename_events,
eeg_bipolar_channels=config.eeg_bipolar_channels,
eeg_template_montage=config.eeg_template_montage,
fix_stim_artifact=config.fix_stim_artifact,
stim_artifact_tmin=config.stim_artifact_tmin,
stim_artifact_tmax=config.stim_artifact_tmax,
find_flat_channels_meg=config.find_flat_channels_meg,
find_noisy_channels_meg=config.find_noisy_channels_meg,
reference_run=config.get_mf_reference_run(),
drop_channels=config.drop_channels,
find_breaks=config.find_breaks,
min_break_duration=config.min_break_duration,
t_break_annot_start_after_previous_event=config.
t_break_annot_start_after_previous_event, # noqa:E501
t_break_annot_stop_before_next_event=config.
t_break_annot_stop_before_next_event, # noqa:E501
)
return cfg
def get_config(
subject: Optional[str] = None,
session: Optional[str] = None
) -> BunchConst:
cfg = BunchConst(
mf_cal_fname=config.get_mf_cal_fname(subject, session),
mf_ctc_fname=config.get_mf_ctc_fname(subject, session),
mf_st_duration=config.mf_st_duration,
mf_head_origin=config.mf_head_origin,
process_er=config.process_er,
runs=config.get_runs(subject=subject), # XXX needs to accept session!
use_maxwell_filter=config.use_maxwell_filter,
proc=config.proc,
task=config.get_task(),
datatype=config.get_datatype(),
acq=config.acq,
rec=config.rec,
space=config.space,
bids_root=config.get_bids_root(),
deriv_root=config.get_deriv_root(),
crop_runs=config.crop_runs,
interactive=config.interactive,
rename_events=config.rename_events,
eeg_template_montage=config.eeg_template_montage,
fix_stim_artifact=config.fix_stim_artifact,
stim_artifact_tmin=config.stim_artifact_tmin,
stim_artifact_tmax=config.stim_artifact_tmax,
find_flat_channels_meg=config.find_flat_channels_meg,
find_noisy_channels_meg=config.find_noisy_channels_meg,
mf_reference_run=config.get_mf_reference_run(),
drop_channels=config.drop_channels,
find_breaks=config.find_breaks,
min_break_duration=config.min_break_duration,
t_break_annot_start_after_previous_event=config.t_break_annot_start_after_previous_event, # noqa:E501
t_break_annot_stop_before_next_event=config.t_break_annot_stop_before_next_event, # noqa:E501
)
return cfg
def run_maxwell_filter(subject, session=None):
if config.proc and 'sss' in config.proc and config.use_maxwell_filter:
raise ValueError(f'You cannot set use_maxwell_filter to True '
f'if data have already processed with Maxwell-filter.'
f' Got proc={config.proc}.')
bids_path_in = BIDSPath(subject=subject,
session=session,
task=config.get_task(),
acquisition=config.acq,
processing=config.proc,
recording=config.rec,
space=config.space,
suffix=config.get_datatype(),
datatype=config.get_datatype(),
root=config.bids_root)
bids_path_out = bids_path_in.copy().update(suffix='raw',
root=config.deriv_root,
check=False)
# Load dev_head_t and digitization points from MaxFilter reference run.
# Re-use in all runs and for processing empty-room recording.
if config.use_maxwell_filter:
reference_run = config.get_mf_reference_run()
msg = f'Loading reference run: {reference_run}.'
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
bids_path_in.update(run=reference_run)
info = mne.io.read_info(bids_path_in.fpath)
dev_head_t = info['dev_head_t']
dig = info['dig']
del reference_run, info
for run_idx, run in enumerate(config.get_runs()):
bids_path_in.update(run=run)
bids_path_out.update(run=run)
raw = load_data(bids_path_in)
# Fix stimulation artifact
if config.fix_stim_artifact:
events, _ = mne.events_from_annotations(raw)
raw = mne.preprocessing.fix_stim_artifact(
raw,
events=events,
event_id=None,
tmin=config.stim_artifact_tmin,
tmax=config.stim_artifact_tmax,
mode='linear')
# Auto-detect bad channels.
if config.find_flat_channels_meg or config.find_noisy_channels_meg:
find_bad_channels(raw=raw,
subject=subject,
session=session,
task=config.get_task(),
run=run)
# Maxwell-filter experimental data.
if config.use_maxwell_filter:
msg = 'Applying Maxwell filter to experimental data.'
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
# Warn if no bad channels are set before Maxwell filter
if not raw.info['bads']:
msg = '\nFound no bad channels. \n '
logger.warning(
gen_log_message(message=msg,
subject=subject,
step=1,
session=session))
if config.mf_st_duration:
msg = ' st_duration=%d' % (config.mf_st_duration)
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
# Keyword arguments shared between Maxwell filtering of the
# experimental and the empty-room data.
common_mf_kws = dict(calibration=get_mf_cal_fname(
subject, session),
cross_talk=get_mf_ctc_fname(subject, session),
st_duration=config.mf_st_duration,
origin=config.mf_head_origin,
coord_frame='head',
destination=dev_head_t)
raw_sss = mne.preprocessing.maxwell_filter(raw, **common_mf_kws)
raw_out = raw_sss
raw_fname_out = (bids_path_out.copy().update(processing='sss',
extension='.fif'))
elif config.ch_types == ['eeg']:
msg = 'Not applying Maxwell filter to EEG data.'
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
raw_out = raw
raw_fname_out = bids_path_out.copy().update(extension='.fif')
else:
msg = ('Not applying Maxwell filter.\nIf you wish to apply it, '
'set use_maxwell_filter=True in your configuration.')
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
raw_out = raw
raw_fname_out = bids_path_out.copy().update(extension='.fif')
# Save only the channel types we wish to analyze (including the
# channels marked as "bad").
# We do not run `raw_out.pick()` here because it uses too much memory.
chs_to_include = config.get_channels_to_analyze(raw_out.info)
raw_out.save(raw_fname_out,
picks=chs_to_include,
overwrite=True,
split_naming='bids')
del raw_out
if config.interactive:
# Load the data we have just written, because it contains only
# the relevant channels.
raw = mne.io.read_raw_fif(raw_fname_out, allow_maxshield=True)
raw.plot(n_channels=50, butterfly=True)
# Empty-room processing.
#
# We pick the empty-room recording closest in time to the first run
# of the experimental session.
if run_idx == 0 and config.process_er:
msg = 'Processing empty-room recording …'
logger.info(
gen_log_message(step=1,
subject=subject,
session=session,
message=msg))
bids_path_er_in = bids_path_in.find_empty_room()
raw_er = load_data(bids_path_er_in)
raw_er.info['bads'] = [
ch for ch in raw.info['bads'] if ch.startswith('MEG')
]
# Maxwell-filter empty-room data.
if config.use_maxwell_filter:
msg = 'Applying Maxwell filter to empty-room recording'
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
# We want to ensure we use the same coordinate frame origin in
# empty-room and experimental data processing. To do this, we
# inject the sensor locations and the head <> device transform
# into the empty-room recording's info, and leave all other
# parameters the same as for the experimental data. This is not
# very clean, as we normally should not alter info manually,
# except for info['bads']. Will need improvement upstream in
# MNE-Python.
raw_er.info['dig'] = dig
raw_er.info['dev_head_t'] = dev_head_t
raw_er_sss = mne.preprocessing.maxwell_filter(
raw_er, **common_mf_kws)
# Perform a sanity check: empty-room rank should match the
# experimental data rank after Maxwell filtering.
rank_exp = mne.compute_rank(raw, rank='info')['meg']
rank_er = mne.compute_rank(raw_er, rank='info')['meg']
if not np.isclose(rank_exp, rank_er):
msg = (f'Experimental data rank {rank_exp:.1f} does not '
f'match empty-room data rank {rank_er:.1f} after '
f'Maxwell filtering. This indicates that the data '
f'were processed differenlty.')
raise RuntimeError(msg)
raw_er_out = raw_er_sss
raw_er_fname_out = bids_path_out.copy().update(
processing='sss')
else:
raw_er_out = raw_er
raw_er_fname_out = bids_path_out.copy()
raw_er_fname_out = raw_er_fname_out.update(task='noise',
extension='.fif',
run=None)
# Save only the channel types we wish to analyze
# (same as for experimental data above).
raw_er_out.save(raw_er_fname_out,
picks=chs_to_include,
overwrite=True,
split_naming='bids')
del raw_er_out