def main():
"""Run sliding estimator."""
if not config.contrasts:
msg = 'No contrasts specified; not performing decoding.'
logger.info(**gen_log_kwargs(message=msg))
return
if not config.decode:
msg = 'No decoding requested by user.'
logger.info(**gen_log_kwargs(message=msg))
return
# Here we go parallel inside the :class:`mne.decoding.SlidingEstimator`
# so we don't dispatch manually to multiple jobs.
parallel, run_func, _ = parallel_func(run_time_decoding, n_jobs=1)
logs = parallel(
run_func(cfg=get_config(),
subject=subject,
condition1=cond_1,
condition2=cond_2,
session=session)
for subject, session, (cond_1, cond_2) in itertools.product(
config.get_subjects(), config.get_sessions(), config.contrasts))
config.save_logs(logs)
def make_ecg_epochs(*,
cfg,
raw: mne.io.BaseRaw,
subject: str,
session: str,
run: Optional[str] = None) -> Optional[mne.BaseEpochs]:
# ECG either needs an ecg channel, or avg of the mags (i.e. MEG data)
if ('ecg' in raw.get_channel_types() or 'meg' in cfg.ch_types
or 'mag' in cfg.ch_types):
msg = 'Creating ECG epochs …'
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
ecg_epochs = create_ecg_epochs(raw,
baseline=(None, -0.2),
tmin=-0.5,
tmax=0.5)
if len(ecg_epochs) == 0:
msg = ('No ECG events could be found. Not running ECG artifact '
'detection.')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
ecg_epochs = None
else:
msg = ('No ECG or magnetometer channels are present. Cannot '
'automate artifact detection for ECG')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
ecg_epochs = None
return ecg_epochs
def make_scalp_surface(*, cfg, subject):
fs_subject = cfg.fs_subject
bem_dir = Path(cfg.fs_subjects_dir) / fs_subject / 'bem'
generate_surface = cfg.recreate_scalp_surface
# Even if the user didn't ask for re-creation of the surface, we check if
# the required file exist; if it doesn't, we create it
surface_fname = bem_dir / f'sub-{subject}-head-dense.fif'
if not generate_surface and not surface_fname.exists():
generate_surface = True
if not generate_surface:
# Seems everything is in place, so we can safely skip surface creation
msg = 'Not generating high-resolution scalp surface.'
logger.info(**gen_log_kwargs(message=msg, subject=subject))
return
msg = 'Generating high-resolution scalp surface for coregistration.'
logger.info(**gen_log_kwargs(message=msg, subject=subject))
mne.bem.make_scalp_surfaces(subject=fs_subject,
subjects_dir=cfg.fs_subjects_dir,
no_decimate=True,
force=True,
overwrite=True)
def detect_bad_components(*, cfg, which: Literal['eog', 'ecg'],
epochs: mne.BaseEpochs, ica: mne.preprocessing.ICA,
subject: str,
session: str) -> Tuple[List[int], np.ndarray]:
artifact = which.upper()
msg = f'Performing automated {artifact} artifact detection …'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
if which == 'eog':
inds, scores = ica.find_bads_eog(epochs,
threshold=cfg.ica_eog_threshold)
else:
inds, scores = ica.find_bads_ecg(epochs,
method='ctps',
threshold=cfg.ica_ctps_ecg_threshold)
if not inds:
adjust_setting = ('ica_eog_threshold'
if which == 'eog' else 'ica_ctps_ecg_threshold')
warn = (f'No {artifact}-related ICs detected, this is highly '
f'suspicious. A manual check is suggested. You may wish to '
f'lower "{adjust_setting}".')
logger.warning(
**gen_log_kwargs(message=warn, subject=subject, session=session))
else:
msg = (f'Detected {len(inds)} {artifact}-related ICs in '
f'{len(epochs)} {artifact} epochs.')
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
return inds, scores
def average_evokeds(cfg, session):
# Container for all conditions:
all_evokeds = defaultdict(list)
for subject in cfg.subjects:
fname_in = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
suffix='ave',
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
msg = f'Input: {fname_in}'
logger.info(**gen_log_kwargs(message=msg, subject=subject,
session=session))
evokeds = mne.read_evokeds(fname_in)
for idx, evoked in enumerate(evokeds):
all_evokeds[idx].append(evoked) # Insert into the container
for idx, evokeds in all_evokeds.items():
all_evokeds[idx] = mne.grand_average(
evokeds, interpolate_bads=cfg.interpolate_bads_grand_average
) # Combine subjects
# Keep condition in comment
all_evokeds[idx].comment = 'Grand average: ' + evokeds[0].comment
subject = 'average'
fname_out = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
processing=cfg.proc,
recording=cfg.rec,
space=cfg.space,
suffix='ave',
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
if not fname_out.fpath.parent.exists():
os.makedirs(fname_out.fpath.parent)
msg = f'Saving grand-averaged evoked sensor data: {fname_out}'
logger.info(**gen_log_kwargs(message=msg, subject=subject,
session=session))
mne.write_evokeds(fname_out, list(all_evokeds.values()))
return list(all_evokeds.values())
def detect_bad_components(*, cfg, which: Literal['eog', 'ecg'],
epochs: mne.BaseEpochs, ica: mne.preprocessing.ICA,
subject: str, session: str,
report: mne.Report) -> List[int]:
evoked = epochs.average()
artifact = which.upper()
msg = f'Performing automated {artifact} artifact detection …'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
if which == 'eog':
inds, scores = ica.find_bads_eog(epochs,
threshold=cfg.ica_eog_threshold)
else:
inds, scores = ica.find_bads_ecg(epochs,
method='ctps',
threshold=cfg.ica_ctps_ecg_threshold)
if not inds:
adjust_setting = ('ica_eog_threshold'
if which == 'eog' else 'ica_ctps_ecg_threshold')
warn = (f'No {artifact}-related ICs detected, this is highly '
f'suspicious. A manual check is suggested. You may wish to '
f'lower "{adjust_setting}".')
logger.warning(
**gen_log_kwargs(message=warn, subject=subject, session=session))
else:
msg = (f'Detected {len(inds)} {artifact}-related ICs in '
f'{len(epochs)} {artifact} epochs.')
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
# Mark the artifact-related components for removal
ica.exclude = inds
# Plot scores
fig = ica.plot_scores(scores, labels=which, show=cfg.interactive)
report.add_figs_to_section(figs=fig,
captions=f'Scores - {artifact}',
section=f'sub-{subject}')
# Plot source time course
fig = ica.plot_sources(evoked, show=cfg.interactive)
report.add_figs_to_section(figs=fig,
captions=f'Source time course - {artifact}',
section=f'sub-{subject}')
# Plot original & corrected data
fig = ica.plot_overlay(evoked, show=cfg.interactive)
report.add_figs_to_section(figs=fig,
captions=f'Corrections - {artifact}',
section=f'sub-{subject}')
return inds
def main():
"""Initialize the output directories."""
msg = 'Running: Initializing output directories.'
logger.info(**gen_log_kwargs(message=msg))
init_dataset(cfg=get_config())
parallel, run_func, _ = parallel_func(init_subject_dirs,
n_jobs=config.get_n_jobs())
parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
msg = 'Completed: Initializing output directories.'
logger.info(**gen_log_kwargs(message=msg))
def fit_ica(
*,
cfg,
epochs: mne.BaseEpochs,
subject: str,
session: str,
) -> mne.preprocessing.ICA:
algorithm = cfg.ica_algorithm
fit_params = None
if algorithm == 'picard':
fit_params = dict(fastica_it=5)
elif algorithm == 'extended_infomax':
algorithm = 'infomax'
fit_params = dict(extended=True)
ica = ICA(method=algorithm,
random_state=cfg.random_state,
n_components=cfg.ica_n_components,
fit_params=fit_params,
max_iter=cfg.ica_max_iterations)
ica.fit(epochs, decim=cfg.ica_decim)
explained_var = (ica.pca_explained_variance_[:ica.n_components_].sum() /
ica.pca_explained_variance_.sum())
msg = (f'Fit {ica.n_components_} components (explaining '
f'{round(explained_var * 100, 1)}% of the variance) in '
f'{ica.n_iter_} iterations.')
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
return ica
def main():
"""Make reports."""
parallel, run_func, _ = parallel_func(run_report,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(
cfg=get_config(subject=subject), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
sessions = config.get_sessions()
if not sessions:
sessions = [None]
if (config.get_task() is not None and config.get_task().lower() == 'rest'):
msg = ' … skipping "average" report for "rest" task.'
logger.info(**gen_log_kwargs(message=msg))
return
for session in sessions:
run_report_average(cfg=get_config(subject='average'),
subject='average',
session=session)
def run_forward(*, cfg, subject, session=None):
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
fname_info = bids_path.copy().update(**cfg.source_info_path_update)
fname_trans = bids_path.copy().update(suffix='trans')
fname_fwd = bids_path.copy().update(suffix='fwd')
if cfg.use_template_mri:
src, trans, bem_sol = _prepare_forward_fsaverage(cfg)
else:
src, trans, bem_sol = _prepare_forward(cfg, bids_path, fname_trans)
# Finally, calculate and save the forward solution.
msg = 'Calculating forward solution'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
info = mne.io.read_info(fname_info)
fwd = mne.make_forward_solution(info,
trans=trans,
src=src,
bem=bem_sol,
mindist=cfg.mindist)
mne.write_trans(fname_trans, fwd['mri_head_t'], overwrite=True)
mne.write_forward_solution(fname_fwd, fwd, overwrite=True)
def compute_cov_from_epochs(cfg, subject, session, tmin, tmax):
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
processing=cfg.proc,
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
processing = None
if cfg.spatial_filter is not None:
processing = 'clean'
epo_fname = bids_path.copy().update(processing=processing, suffix='epo')
cov_fname = bids_path.copy().update(suffix='cov')
msg = (f"Computing regularized covariance based on epochs' baseline "
f"periods. Input: {epo_fname}, Output: {cov_fname}")
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
epochs = mne.read_epochs(epo_fname, preload=True)
cov = mne.compute_covariance(epochs,
tmin=tmin,
tmax=tmax,
method='shrunk',
rank='info')
cov.save(cov_fname)
def run_group_average_source(*, cfg, subject='average'):
"""Run group average in source space"""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
mne.datasets.fetch_fsaverage(subjects_dir=config.get_fs_subjects_dir())
parallel, run_func, _ = parallel_func(morph_stc,
n_jobs=config.get_n_jobs())
all_morphed_stcs = parallel(
run_func(cfg=cfg,
subject=subject,
fs_subject=config.get_fs_subject(subject),
session=session) for subject, session in itertools.product(
config.get_subjects(), config.get_sessions()))
mean_morphed_stcs = np.array(all_morphed_stcs).mean(axis=0)
# XXX to fix
sessions = config.get_sessions()
if sessions:
session = sessions[0]
else:
session = None
run_average(cfg=cfg, session=session, mean_morphed_stcs=mean_morphed_stcs)
def compute_cov_from_empty_room(cfg, subject, session):
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
raw_er_fname = bids_path.copy().update(processing='filt',
task='noise',
suffix='raw')
cov_fname = bids_path.copy().update(suffix='cov')
msg = (f'Computing regularized covariance based on empty-room recording. '
f'Input: {raw_er_fname}, Output: {cov_fname}')
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
raw_er = mne.io.read_raw_fif(raw_er_fname, preload=True)
cov = mne.compute_raw_covariance(raw_er, method='shrunk', rank='info')
cov.save(cov_fname)
def filter(raw: mne.io.BaseRaw, subject: str, session: Optional[str],
run: Optional[str], l_freq: Optional[float],
h_freq: Optional[float],
l_trans_bandwidth: Optional[Union[float, Literal['auto']]],
h_trans_bandwidth: Optional[Union[float, Literal['auto']]]) -> None:
"""Filter data channels (MEG and EEG)."""
data_type = 'empty-room' if subject == 'emptyroom' else 'experimental'
if l_freq is not None and h_freq is None:
msg = (f'High-pass filtering {data_type} data; lower bound: '
f'{l_freq} Hz')
elif l_freq is None and h_freq is not None:
msg = (f'Low-pass filtering {data_type} data; upper bound: '
f'{h_freq} Hz')
elif l_freq is not None and h_freq is not None:
msg = (f'Band-pass filtering {data_type} data; range: '
f'{l_freq} – {h_freq} Hz')
else:
msg = (f'Not applying frequency filtering to {data_type} data.')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
if l_freq is None and h_freq is None:
return
raw.filter(l_freq=l_freq,
h_freq=h_freq,
l_trans_bandwidth=l_trans_bandwidth,
h_trans_bandwidth=h_trans_bandwidth,
filter_length='auto',
phase='zero',
fir_window='hamming',
fir_design='firwin')
def filter_for_ica(*,
cfg,
raw: mne.io.BaseRaw,
subject: str,
session: str,
run: Optional[str] = None) -> None:
"""Apply a high-pass filter if needed."""
if cfg.ica_l_freq is None:
msg = (f'Not applying high-pass filter (data is already filtered, '
f'cutoff: {raw.info["highpass"]} Hz).')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
else:
msg = f'Applying high-pass filter with {cfg.ica_l_freq} Hz cutoff …'
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
raw.filter(l_freq=cfg.ica_l_freq, h_freq=None)
def main():
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
log = run_group_average_source(cfg=get_config())
config.save_logs([log])
def make_ecg_epochs(*,
cfg,
raw_path: BIDSPath,
subject: str,
session: str,
run: Optional[str] = None,
n_runs: int) -> Optional[mne.BaseEpochs]:
# ECG either needs an ecg channel, or avg of the mags (i.e. MEG data)
raw = mne.io.read_raw(raw_path, preload=False)
if ('ecg' in raw.get_channel_types() or 'meg' in cfg.ch_types
or 'mag' in cfg.ch_types):
msg = 'Creating ECG epochs …'
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
# We want to extract a total of 5 min of data for ECG epochs generation
# (across all runs)
total_ecg_dur = 5 * 60
ecg_dur_per_run = total_ecg_dur / n_runs
t_mid = (raw.times[-1] + raw.times[0]) / 2
raw = raw.crop(tmin=max(t_mid - 1 / 2 * ecg_dur_per_run, 0),
tmax=min(t_mid + 1 / 2 * ecg_dur_per_run,
raw.times[-1])).load_data()
ecg_epochs = create_ecg_epochs(raw,
baseline=(None, -0.2),
tmin=-0.5,
tmax=0.5)
del raw # Free memory
if len(ecg_epochs) == 0:
msg = ('No ECG events could be found. Not running ECG artifact '
'detection.')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
ecg_epochs = None
else:
msg = ('No ECG or magnetometer channels are present. Cannot '
'automate artifact detection for ECG')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
ecg_epochs = None
return ecg_epochs
def main():
"""Run BEM surface extraction."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
if config.use_template_mri:
msg = ' … skipping BEM computating when using MRI template.'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(make_bem_and_scalp_surface,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(subject=subject), subject=subject)
for subject in config.get_subjects())
config.save_logs(logs)
def make_bem(*, cfg, subject):
fs_subject = cfg.fs_subject
mri_dir = Path(cfg.fs_subjects_dir) / fs_subject / 'mri'
bem_dir = Path(cfg.fs_subjects_dir) / fs_subject / 'bem'
watershed_bem_dir = bem_dir / 'watershed'
flash_bem_dir = bem_dir / 'flash'
flash_dir = mri_dir / 'flash' / 'parameter_maps'
show = True if cfg.interactive else False
if cfg.bem_mri_images == 'FLASH' and not flash_dir.exists():
raise RuntimeError('Cannot locate FLASH MRI images.')
elif cfg.bem_mri_images == 'FLASH':
mri_images = 'FLASH'
elif cfg.bem_mri_images == 'auto' and flash_dir.exists():
mri_images = 'FLASH'
else:
mri_images = 'T1'
if ((mri_images == 'FLASH' and flash_bem_dir.exists())
or (mri_images == 'T1' and watershed_bem_dir.exists())):
msg = 'Found existing BEM surfaces. '
if cfg.recreate_bem:
msg += 'Overwriting as requested in configuration.'
logger.info(**gen_log_kwargs(message=msg, subject=subject))
else:
msg = 'Skipping surface extraction as requested in configuration.'
logger.info(**gen_log_kwargs(message=msg, subject=subject))
return
if mri_images == 'FLASH':
msg = 'Creating BEM surfaces from FLASH MRI images'
bem_func = mne.bem.make_flash_bem
else:
msg = ('Creating BEM surfaces from T1-weighted MRI images using '
'watershed algorithm')
bem_func = mne.bem.make_watershed_bem
logger.info(**gen_log_kwargs(message=msg, subject=subject))
bem_func(subject=fs_subject,
subjects_dir=cfg.fs_subjects_dir,
copy=True,
overwrite=True,
show=show)
def main():
"""Run cov."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
if config.noise_cov == "ad-hoc":
msg = ' … skipping: using ad-hoc diagonal covariance.'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(run_covariance,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def run_time_frequency(*, cfg, subject, session=None):
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
processing = None
if cfg.spatial_filter is not None:
processing = 'clean'
fname_in = bids_path.copy().update(suffix='epo',
processing=processing,
extension='.fif')
msg = f'Input: {fname_in}'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
epochs = mne.read_epochs(fname_in)
if cfg.analyze_channels:
# We special-case the average reference here.
# See 02-sliding_estimator.py for more info.
if 'eeg' in cfg.ch_types and cfg.eeg_reference == 'average':
epochs.set_eeg_reference('average')
else:
epochs.apply_proj()
epochs.pick(cfg.analyze_channels)
freqs = np.arange(cfg.time_frequency_freq_min, cfg.time_frequency_freq_max)
n_cycles = freqs / 3.
for condition in cfg.time_frequency_conditions:
this_epochs = epochs[condition]
power, itc = mne.time_frequency.tfr_morlet(this_epochs,
freqs=freqs,
return_itc=True,
n_cycles=n_cycles)
condition_str = sanitize_cond_name(condition)
power_fname_out = bids_path.copy().update(
suffix=f'power+{condition_str}+tfr', extension='.h5')
itc_fname_out = bids_path.copy().update(
suffix=f'itc+{condition_str}+tfr', extension='.h5')
power.save(power_fname_out, overwrite=True)
itc.save(itc_fname_out, overwrite=True)
def apply_ssp(*, cfg, subject, session=None):
# load epochs to reject ICA components
# compute SSP on first run of raw
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root)
fname_in = bids_path.copy().update(suffix='epo', check=False)
fname_out = bids_path.copy().update(processing='ssp',
suffix='epo',
check=False)
epochs = mne.read_epochs(fname_in, preload=True)
msg = f'Input: {fname_in}, Output: {fname_out}'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
proj_fname_in = bids_path.copy().update(suffix='proj', check=False)
msg = f'Reading SSP projections from : {proj_fname_in}'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
projs = mne.read_proj(proj_fname_in)
epochs_cleaned = epochs.copy().add_proj(projs).apply_proj()
msg = 'Saving epochs with projectors.'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
epochs_cleaned.save(fname_out, overwrite=True)
def make_eog_epochs(*,
raw: mne.io.BaseRaw,
eog_channels: Optional[Iterable[str]],
subject: str,
session: str,
run: Optional[str] = None) -> Optional[mne.Epochs]:
"""Create EOG epochs. No rejection thresholds will be applied.
"""
if eog_channels:
ch_names = eog_channels
assert all([ch_name in raw.ch_names for ch_name in ch_names])
else:
ch_idx = mne.pick_types(raw.info, meg=False, eog=True)
ch_names = [raw.ch_names[i] for i in ch_idx]
del ch_idx
if ch_names:
msg = 'Creating EOG epochs …'
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
eog_epochs = create_eog_epochs(raw,
ch_name=ch_names,
baseline=(None, -0.2))
if len(eog_epochs) == 0:
msg = ('No EOG events could be found. Not running EOG artifact '
'detection.')
logger.warning(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
eog_epochs = None
else:
msg = ('No EOG channel is present. Cannot automate IC detection '
'for EOG')
logger.info(**gen_log_kwargs(
message=msg, subject=subject, session=session, run=run))
eog_epochs = None
return eog_epochs
def resample(raw: mne.io.BaseRaw, subject: str, session: Optional[str],
run: Optional[str], sfreq: Optional[float]) -> None:
if not sfreq:
return
data_type = 'empty-room' if subject == 'emptyroom' else 'experimental'
msg = f'Resampling {data_type} data to {sfreq:.1f} Hz'
logger.info(**gen_log_kwargs(
message=msg,
subject=subject,
session=session,
run=run,
))
raw.resample(sfreq, npad='auto')
def main():
"""Run Time-frequency decomposition."""
if not config.time_frequency_conditions:
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(run_time_frequency,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run ICA."""
if not config.spatial_filter == 'ica':
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(run_ica, n_jobs=config.get_n_jobs())
logs = parallel(
run_func(
cfg=get_config(subject=subject), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run inv."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(run_inverse,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def run_group_average_sensor(*, cfg, subject='average'):
if config.get_task().lower() == 'rest':
msg = ' … skipping: for "rest" task.'
logger.info(**gen_log_kwargs(message=msg))
return
sessions = config.get_sessions()
if not sessions:
sessions = [None]
for session in sessions:
evokeds = average_evokeds(cfg, session)
if config.interactive:
for evoked in evokeds:
evoked.plot()
if config.decode:
average_decoding(cfg, session)
def main():
"""Run maxwell_filter."""
if not config.use_maxwell_filter:
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(run_maxwell_filter,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(subject, session),
subject=subject,
session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Apply ssp."""
if not config.spatial_filter == 'ssp':
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(
apply_ssp,
n_jobs=config.get_n_jobs()
)
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in
itertools.product(
config.get_subjects(),
config.get_sessions()
)
)
config.save_logs(logs)
