def run_ssp(subject):
print("processing subject: %s" % subject)
meg_subject_dir = op.join(config.meg_dir, subject)
print(" Loading runs")
for run in config.runs:
extension = run + '_sss_raw'
raw_fname_in = op.join(meg_subject_dir,
config.base_fname.format(**locals()))
proj_fname_out = op.splitext(raw_fname_in)[0] + '-proj.fif'
eve_ecg_fname_out = op.splitext(raw_fname_in)[0] + '_ecg-eve.fif'
eve_eog_fname_out = op.splitext(raw_fname_in)[0] + '_eog-eve.fif'
print("Input: ", raw_fname_in)
print("Output: ", proj_fname_out)
print("Output: ", eve_ecg_fname_out)
print("Output: ", eve_eog_fname_out)
raw = mne.io.read_raw_fif(raw_fname_in)
ecg_projs, ecg_events = \
compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
eog_projs, eog_events = \
compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
mne.write_events(eve_ecg_fname_out, ecg_events)
mne.write_events(eve_eog_fname_out, eog_events)
def ssp_preprocessing(eeg, participant, session_name, reject):
try:
projs, eog_events = compute_proj_eog(eeg,
n_grad=0,
n_mag=0,
n_eeg=2,
reject=reject,
average=True)
eog_projs = projs[-2:]
f_eog = mne.viz.plot_projs_topomap(eog_projs,
info=eeg.info,
show=False)
f_eog.savefig(data_dir + 'results/EOG_projections_' + participant +
'_' + session_name + '.png',
dpi=500)
plt.close(f_eog)
eeg = eeg.add_proj(eog_projs[-1:])
eeg = eeg.apply_proj()
except Exception as e:
print('Problem with EOG plot:', e)
return eeg
def run_ssp(subject):
print("Processing subject: %s" % subject)
meg_subject_dir = op.join(config.meg_dir, subject)
print(" Loading one run to compute SSPs")
# compute SSP on first run of raw
run = config.runs[0]
if config.use_maxwell_filter:
extension = run + '_sss_raw'
else:
extension = run + '_filt_raw'
raw_fname_in = op.join(meg_subject_dir,
config.base_fname.format(**locals()))
extension = run + '_ssp-proj'
proj_fname_out = op.join(meg_subject_dir,
config.base_fname.format(**locals()))
print("Input: ", raw_fname_in)
print("Output: ", proj_fname_out)
raw = mne.io.read_raw_fif(raw_fname_in)
# XXX : n_xxx should be options in config
print(" Computing SSPs for ECG")
ecg_projs, ecg_events = \
compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
print(" Computing SSPs for EOG")
eog_projs, eog_events = \
compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
def run_ssp(subject, session=None):
# compute SSP on first run of raw
run = config.get_runs()[0]
bids_path = BIDSPath(subject=subject,
session=session,
task=config.get_task(),
acquisition=config.acq,
run=run,
recording=config.rec,
space=config.space,
extension='.fif',
datatype=config.get_datatype(),
root=config.deriv_root)
# Prepare a name to save the data
raw_fname_in = bids_path.copy().update(processing='filt',
suffix='raw',
check=False)
# when saving proj, use run=None
proj_fname_out = bids_path.copy().update(run=None,
suffix='proj',
check=False)
msg = f'Input: {raw_fname_in}, Output: {proj_fname_out}'
logger.info(
gen_log_message(message=msg, step=4, subject=subject, session=session))
if raw_fname_in.copy().update(split='01').fpath.exists():
raw_fname_in.update(split='01')
raw = mne.io.read_raw_fif(raw_fname_in)
# XXX : n_xxx should be options in config
msg = 'Computing SSPs for ECG'
logger.debug(
gen_log_message(message=msg, step=4, subject=subject, session=session))
ecg_projs, ecg_events = compute_proj_ecg(raw,
n_grad=1,
n_mag=1,
n_eeg=0,
average=True)
msg = 'Computing SSPs for EOG'
logger.debug(
gen_log_message(message=msg, step=4, subject=subject, session=session))
if config.eog_channels:
assert all(
[ch_name in raw.ch_names for ch_name in config.eog_channels])
ch_name = ','.join(config.eog_channels)
else:
ch_name = None
eog_projs, eog_events = compute_proj_eog(raw,
ch_name=ch_name,
n_grad=1,
n_mag=1,
n_eeg=1,
average=True)
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
def run_ssp(subject, session=None):
print("Processing subject: %s" % subject)
print(" Loading one run to compute SSPs")
# Construct the search path for the data file. `sub` is mandatory
subject_path = op.join('sub-{}'.format(subject))
# `session` is optional
if session is not None:
subject_path = op.join(subject_path, 'ses-{}'.format(session))
subject_path = op.join(subject_path, config.kind)
# compute SSP on first run of raw
run = config.runs[0]
bids_basename = make_bids_basename(subject=subject,
session=session,
task=config.task,
acquisition=config.acq,
run=run,
processing=config.proc,
recording=config.rec,
space=config.space)
# Prepare a name to save the data
fpath_deriv = op.join(config.bids_root, 'derivatives',
config.PIPELINE_NAME, subject_path)
raw_fname_in = \
op.join(fpath_deriv, bids_basename + '_filt_raw.fif')
# when saving proj, use bids_basename=None
bids_basename = make_bids_basename(subject=subject,
session=session,
task=config.task,
acquisition=config.acq,
run=None,
processing=config.proc,
recording=config.rec,
space=config.space)
proj_fname_out = op.join(fpath_deriv, bids_basename + '_ssp-proj.fif')
print("Input: ", raw_fname_in)
print("Output: ", proj_fname_out)
raw = mne.io.read_raw_fif(raw_fname_in)
# XXX : n_xxx should be options in config
print(" Computing SSPs for ECG")
ecg_projs, ecg_events = \
compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
print(" Computing SSPs for EOG")
eog_projs, eog_events = \
compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
def do_pca(raw, proj_nums):
ecg_projs, ecg_events = compute_proj_ecg(raw_sss, n_grad=proj_nums[0][0],
n_mag=proj_nums[0][1],
average=True)
eog_projs, eog_events = compute_proj_eog(raw_sss, n_grad=proj_nums[1][0],
n_mag=proj_nums[1][1],
average=True)
raw_sss.info['projs'] += eog_projs + ecg_projs
return raw_sss, (ecg_projs, eog_projs, ecg_events, eog_events)
def run_ssp(subject, session=None):
deriv_path = config.get_subject_deriv_path(subject=subject,
session=session,
kind=config.get_kind())
# compute SSP on first run of raw
run = config.get_runs()[0]
bids_basename = make_bids_basename(subject=subject,
session=session,
task=config.get_task(),
acquisition=config.acq,
run=run,
processing=config.proc,
recording=config.rec,
space=config.space)
# Prepare a name to save the data
raw_fname_in = op.join(deriv_path, bids_basename + '_filt_raw.fif')
# when saving proj, use bids_basename=None
bids_basename = make_bids_basename(subject=subject,
session=session,
task=config.get_task(),
acquisition=config.acq,
run=None,
processing=config.proc,
recording=config.rec,
space=config.space)
proj_fname_out = op.join(deriv_path, bids_basename + '_ssp-proj.fif')
msg = f'Input: {raw_fname_in}, Output: {proj_fname_out}'
logger.info(
gen_log_message(message=msg, step=4, subject=subject, session=session))
raw = mne.io.read_raw_fif(raw_fname_in)
# XXX : n_xxx should be options in config
msg = 'Computing SSPs for ECG'
logger.debug(
gen_log_message(message=msg, step=4, subject=subject, session=session))
ecg_projs, ecg_events = \
compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
msg = 'Computing SSPs for EOG'
logger.debug(
gen_log_message(message=msg, step=4, subject=subject, session=session))
eog_projs, eog_events = \
compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
def preproc_gen_ssp(subj_fold, raw, hcp_params, annots):
"""Helper to apply artifact removal on raw data
NOTE: Only use this on one run of data to create projectors. Subsequent
runs should load and apply the same projectors. This is required to make
sure machine learning algorithm isn't classifying differences in projectors
(like the ICA projectors shipped with HCP) instead of differences in
activity."""
proj_dir = op.join(hcp_path, subj_fold, 'ssp_pca_fif')
check_and_create_dir(proj_dir)
# Compute EOG and ECG projectors
# XXX Note: do not add these to raw obj, as all raw files need to use the
# same projectors. Instead, save and then reapply later
preproc.set_eog_ecg_channels(raw)
proj_eog1, eog_events1 = compute_proj_eog(raw, ch_name='HEOG', n_jobs=-1)
proj_eog2, eog_events2 = compute_proj_eog(raw, ch_name='VEOG', n_jobs=-1)
proj_ecg, ecg_events = compute_proj_ecg(raw, ch_name='ECG', n_jobs=-1)
# Save to disk so these can be used in future processing
all_projs = proj_eog1 + proj_eog2 + proj_ecg
write_proj(op.join(proj_dir, 'preproc_all-proj.fif'), all_projs)
def run_ssp(subject, session=None):
deriv_path = config.get_subject_deriv_path(subject=subject,
session=session,
kind=config.get_kind())
# compute SSP on first run of raw
run = config.get_runs()[0]
bids_basename = BIDSPath(subject=subject,
session=session,
task=config.get_task(),
acquisition=config.acq,
run=run,
recording=config.rec,
space=config.space,
prefix=deriv_path)
# Prepare a name to save the data
raw_fname_in = bids_basename.copy().update(processing='filt',
kind=config.get_kind(),
extension='.fif')
# when saving proj, use run=None
proj_fname_out = bids_basename.copy().update(run=None,
kind='proj',
extension='.fif',
check=False)
msg = f'Input: {raw_fname_in}, Output: {proj_fname_out}'
logger.info(
gen_log_message(message=msg, step=4, subject=subject, session=session))
raw = mne.io.read_raw_fif(raw_fname_in)
# XXX : n_xxx should be options in config
msg = 'Computing SSPs for ECG'
logger.debug(
gen_log_message(message=msg, step=4, subject=subject, session=session))
ecg_projs, ecg_events = \
compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
msg = 'Computing SSPs for EOG'
logger.debug(
gen_log_message(message=msg, step=4, subject=subject, session=session))
eog_projs, eog_events = \
compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
def run_ssp(subject):
print("processing subject: %s" % subject)
meg_subject_dir = op.join(config.meg_dir, subject)
raw_fnames_in = op.join(meg_subject_dir, '%s_audvis_filt_raw.fif' % subject)
proj_fnames_out = op.join(meg_subject_dir, '%s_audvis_filt-proj.fif' % subject)
print(" Loading runs")
for raw_fname_in, proj_fname_out in zip(raw_fnames_in, proj_fnames_out):
# XXX TODO
raw = mne.io.read_raw_fif(raw_fnames_in)
projs, events = compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
ecg_projs = projs[-2:]
projs, events = compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
eog_projs = projs[-3:]
raw.info['projs'] += eog_projs + ecg_projs
raw.apply_proj()
raw.save(proj_fnames_out, overwrite=True)
def compute_ecg_eog_proj(raw,
ecg=True,
eog=True,
n_grad_ecg=1,
n_grad_eog=1,
n_mag_ecg=1,
n_mag_eog=1,
average=True,
vis=False,
add_proj=True):
if ecg:
projs, events_ecg = compute_proj_ecg(raw,
n_grad=n_grad_ecg,
n_mag=n_mag_ecg,
average=average)
ecg_projs = projs[-(n_mag_ecg + n_grad_ecg):]
if add_proj:
raw.info['projs'] += ecg_projs
if vis:
layout = mne.channels.read_layout('Vectorview-all')
names = [name.replace(' ', '') for name in layout.names]
layout.names = names
mne.viz.plot_projs_topomap(ecg_projs, layout=layout)
# Now for EOG
if eog:
projs, events_eog = compute_proj_eog(raw,
n_grad=n_grad_eog,
n_mag=n_mag_eog,
average=average)
eog_projs = projs[-(n_mag_eog + n_grad_eog):]
if add_proj:
raw.info['projs'] += eog_projs
if vis:
layout = mne.channels.read_layout('Vectorview-all')
names = [name.replace(' ', '') for name in layout.names]
layout.names = names
mne.viz.plot_projs_topomap(eog_projs, layout=layout)
return ecg_projs, eog_projs, events_ecg, events_eog
def run_correlation(subjects_dir, subject, volume_spacing, freq):
num_threads(8)
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
eye_proj1 = f'{DATA_DIR}/{subject}_eyes1-proj.fif.gz'
eye_proj2 = f'{DATA_DIR}/{subject}_eyes2-proj.fif.gz'
fname_meg = f'{DATA_DIR}/{subject}_ses-rest_task-rest.fif'
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
heartbeat_proj = f'{DATA_DIR}/{subject}_heartbeat-proj.fif.gz'
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd-label.fif.gz'
src_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-src-label.fif.gz'
cov_fname = f'{DATA_DIR}/{subject}-cov_{volume_spacing}-label.fif.gz'
raw_cov_fname = f'{DATA_DIR}/{subject}-rawcov_{volume_spacing}-label.fif.gz'
raw_proj = f'{DATA_DIR}/{subject}_ses-rest_task-rest_proj-label.fif.gz'
source_voxel_coords = f'{DATA_DIR}/{subject}_coords_{volume_spacing}.pkl'
freesurfer_label = f'{DATA_DIR}/{subject}_freesurferlabel_{volume_spacing}-label.pkl'
corr_true_file_label = f'{DATA_DIR}/{subject}_corr_ortho_true_{volume_spacing}_{frequency}_label.npy'
check_for_files = []
check_for_files.append(corr_true_file_label)
file_exist = [f for f in check_for_files if os.path.isfile(f)]
file_not_exist = list(set(file_exist) ^ set(check_for_files))
if not file_not_exist:
print('correlation files exists...')
else:
trans = f'/home/senthilp/caesar/camcan/cc700/camcan_coreg-master/trans/{subject}-trans.fif' # The transformation file obtained by coregistration
file_trans = pathlib.Path(trans)
file_ss = pathlib.Path(src_fname)
file_fm = pathlib.Path(fwd_fname)
file_proj = pathlib.Path(raw_proj)
file_cov = pathlib.Path(cov_fname)
file_rawcov = pathlib.Path(raw_cov_fname)
t1 = nib.load(t1_fname)
if not file_trans.exists():
print (f'{trans} File doesnt exist...')
sys.exit(0)
info = mne.io.read_info(fname_meg)
# plot_registration(info, trans, subject, subjects_dir)
print(file_ss)
if not file_ss.exists():
src = compute_SourceSpace(subject, subjects_dir, src_fname, source_voxel_coords, plot=True, ss='volume',
volume_spacing=volume_spacing)
src.save(src_fname, overwrite=True)
src = mne.read_source_spaces(src_fname)
#view_SS_brain(subject, subjects_dir, src)
if not file_fm.exists():
forward_model(subject, subjects_dir, fname_meg, trans, src, fwd_fname)
fwd = mne.read_forward_solution(fwd_fname)
# sensitivty_plot(subject, subjects_dir, fwd)
raw = mne.io.read_raw_fif(fname_meg, verbose='error', preload=True)
srate = raw.info['sfreq']
n_time_samps = raw.n_times
time_secs = raw.times
ch_names = raw.ch_names
n_chan = len(ch_names)
freq_res = srate/n_time_samps
print('\n')
print('-------------------------- Data summary-------------------------------')
print(f'Subject {subject}')
print(f"Frequency resolution {freq_res} Hz")
print(f"The first few channel names are {ch_names[:3]}")
print(f"The last time sample at {time_secs[-1]} seconds.")
print(f"Sampling Frequency (No of time points/sec) {srate} Hz")
print(f"Miscellaneous acquisition info {raw.info['description']}")
print(f"Bad channels marked during data acquisition {raw.info['bads']}")
print(f"Convert time in sec ( 60s ) to ingeter index {raw.time_as_index(60)}") # Convert time to indices
print(f"The raw data object has {n_time_samps} time samples and {n_chan} channels.")
print('------------------------------------------------------------------------')
print('\n')
# raw.plot(n_channels=10, scalings='auto', title='Data from arrays', show=True, block=True)
if not file_proj.exists():
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2, ch_name='ECG063')
projs_eog1, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG061')
projs_eog2, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG062')
if projs_ecg is not None:
mne.write_proj(heartbeat_proj, projs_ecg) # Saving projectors
raw.info['projs'] += projs_ecg
if projs_eog1 is not None:
mne.write_proj(eye_proj1, projs_eog1)
raw.info['projs'] += projs_eog1
if projs_eog2 is not None:
mne.write_proj(eye_proj2, projs_eog2)
raw.info['projs'] += projs_eog2
raw.apply_proj()
raw.save(raw_proj, proj=True, overwrite=True)
print(raw_proj)
raw_proj_applied = mne.io.read_raw_fif(raw_proj, verbose='error', preload=True)
print(f'High-pass filtering data at 0.5 Hz')
raw_proj_applied.filter(l_freq=0.5, h_freq=None, method='iir')
if not file_cov.exists():
cov = mne.compute_raw_covariance(raw_proj_applied) # compute before band-pass of interest
mne.write_cov(cov_fname, cov)
cov = mne.read_cov(cov_fname)
# cov.plot(raw.info, proj=True, exclude='bads', show_svd=False
# raw_proj_applied.crop(tmax=10)
do_epochs = False
l_freq = freq-2.0
h_freq = freq+2.0
print(f'Band pass filter data [{l_freq}, {h_freq}]')
raw_proj_filtered = raw_proj_applied.filter(l_freq=l_freq, h_freq=h_freq)
if do_epochs:
print('Segmenting raw data...')
events = mne.make_fixed_length_events(raw_proj_filtered, duration=5.)
raw_proj_filtered = mne.Epochs(raw_proj_filtered, events=events, tmin=0, tmax=5.,
baseline=None, preload=True)
data_cov = mne.compute_covariance(raw_proj_filtered)
else:
if not file_rawcov.exists():
data_cov = mne.compute_raw_covariance(raw_proj_filtered)
mne.write_cov(raw_cov_fname, data_cov)
else:
data_cov = mne.read_cov(file_rawcov)
filters = make_lcmv(raw_proj_filtered.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
raw_proj_filtered_comp = raw_proj_filtered.apply_hilbert()
if do_epochs:
stcs = apply_lcmv_epochs(raw_proj_filtered_comp, filters, return_generator=False)
else:
stcs = apply_lcmv_raw(raw_proj_filtered_comp, filters, verbose=True)
print('Extracting label time course...')
atlas = f'{subjects_dir}/{subject}/mri/aparc.a2009s+aseg.mgz'
label_ts = mne.extract_label_time_course(stcs, atlas, fwd['src'], return_generator=False)
label_ts = [label_ts]
# Power Envelope Correlation
print(f'Computing Power Envelope Correlation for {subject}....Orthogonalize True')
all_corr = envelope_correlation(label_ts, combine=None, orthogonalize="pairwise",
log=True, absolute=True, verbose=None)
print(f'Correlation saved to {corr_true_file_label}')
np.save(corr_true_file_label, all_corr)
del stcs
# Compute SSP projections # ----------------------- # # First let's do ECG. projs, events = compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True) print(projs) ecg_projs = projs[-2:] mne.viz.plot_projs_topomap(ecg_projs) ############################################################################### # Now let's do EOG. Here we compute an EEG projector, and need to pass # the measurement info so the topomap coordinates can be created. projs, events = compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True) print(projs) eog_projs = projs[-3:] mne.viz.plot_projs_topomap(eog_projs, info=raw.info) ############################################################################## # Apply SSP projections # --------------------- # # MNE is handling projections at the level of the info, # so to register them populate the list that you find in the 'proj' field raw.info['projs'] += eog_projs + ecg_projs #############################################################################
def raw_preprocessing(self, raw, use_ica=True, use_ssp=True):
# Filter
raw.info['bads'] = self.bad_channels
# Remove power-line noise
raw.notch_filter(np.arange(60, 241, 60),
picks=self.picks,
filter_length='auto',
phase=self.phase)
raw.filter(l_freq=self.l_freq,
h_freq=self.h_freq,
phase=self.phase,
fir_window=self.fir_window,
fir_design=self.fir_design)
# Add EEG reference
raw.set_eeg_reference(projection=True)
# TODO: some mechanism to control this
if use_ica:
# supported ica_channels = ['mag', 'grad', 'eeg', 'seeg', 'ecog', 'hbo', 'hbr', 'eog']
ica_picks = mne.pick_types(raw.info,
meg=True,
eeg=False,
eog=False,
stim=False,
exclude='bads')
n_components = 25
decim = 3
# maximum number of components to reject
n_max_ecg, n_max_eog = 3, 1 # here we don't expect horizontal EOG components
# ica = run_ica(raw, n_components=0.95)
ica = ICA(n_components=n_components,
method='fastica',
noise_cov=None)
ica.fit(raw, decim=decim, picks=ica_picks, reject=self.reject)
# generate ECG epochs use detection via phase statistics
ecg_epochs = create_ecg_epochs(raw, reject=self.reject)
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs, method='ctps')
ecg_inds = ecg_inds[:n_max_ecg]
ica.exclude += ecg_inds
# detect EOG by correlation
eog_inds, scores = ica.find_bads_eog(raw)
eog_inds = eog_inds[:n_max_eog]
ica.exclude += eog_inds
ica.apply(raw)
if use_ssp:
if self.ecg:
ecg_projs, _ = compute_proj_ecg(raw)
raw.info['projs'] += ecg_projs
if self.blink:
eog_projs, _ = compute_proj_eog(raw)
raw.info['projs'] += eog_projs
raw.apply_proj()
return raw
raw_sss = maxwell_filter(raw, head_pos=head_pos, st_duration=300)
raw_sss.save(raw_fname[:-4] + '_sss.fif')
else:
raw_sss = mne.io.read_raw_fif(raw_fname[:-4] + '_sss.fif', preload=True)
# artifact correction
ecg_projs, events = compute_proj_ecg(raw_sss,
n_grad=3,
n_mag=3,
n_eeg=0,
average=True)
print(ecg_projs)
mne.viz.plot_projs_topomap(ecg_projs)
eog_projs, events = compute_proj_eog(raw_sss,
n_grad=3,
n_mag=3,
n_eeg=0,
average=True)
print(eog_projs)
mne.viz.plot_projs_topomap(eog_projs)
raw_sss.info['projs'] += eog_projs + ecg_projs
picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
eog=False,
exclude='bads')
reject = dict(mag=5e-12, grad=4000e-13)
raw_filt = raw_sss.copy().pick_types(meg=True,
eeg=False,
stim=False,
def run_correlation(subjects_dir, subject, volume_spacing, freq, ortho_bool):
num_threads(8)
ortho_flag = str(ortho_bool)
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
eye_proj1 = f'{DATA_DIR}/{subject}_eyes1-proj.fif.gz'
eye_proj2 = f'{DATA_DIR}/{subject}_eyes2-proj.fif.gz'
fname_meg = f'{DATA_DIR}/{subject}_ses-rest_task-rest.fif'
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
heartbeat_proj = f'{DATA_DIR}/{subject}_heartbeat-proj.fif.gz'
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd.fif.gz'
src_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-src.fif.gz'
cov_fname = f'{DATA_DIR}/{subject}-cov_{volume_spacing}.fif.gz'
raw_cov_fname = f'{DATA_DIR}/{subject}-rawcov_{volume_spacing}.fif.gz'
raw_proj = f'{DATA_DIR}/{subject}_ses-rest_task-rest_proj.fif.gz'
source_voxel_coords = f'{DATA_DIR}/{subject}_coords_{volume_spacing}.pkl'
corr_file_acLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_acLeft.npy'
corr_file_scLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_scLeft.npy'
corr_file_vcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_vcLeft.npy'
corr_file_mtLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtLeft.npy'
corr_file_mtlLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtlLeft.npy'
corr_file_smcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_smcLeft.npy'
corr_file_lpcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_lpcLeft.npy'
corr_file_dpfcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_dpfcLeft.npy'
corr_file_tmpcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_tmpcLeft.npy'
corr_file_acRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_acRight.npy'
corr_file_scRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_scRight.npy'
corr_file_vcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_vcRight.npy'
corr_file_mtRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtRight.npy'
corr_file_mtlRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtlRight.npy'
corr_file_smcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_smcRight.npy'
corr_file_lpcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_lpcRight.npy'
corr_file_dpfcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_dpfcRight.npy'
corr_file_tmpcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_tmpcRight.npy'
corr_file_mpfc = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mpfc.npy'
corr_file_sma = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_sma.npy'
check_for_files = []
check_for_files.append(corr_file_acLeft)
check_for_files.append(corr_file_scLeft)
check_for_files.append(corr_file_vcLeft)
check_for_files.append(corr_file_mtLeft)
check_for_files.append(corr_file_mtlLeft)
check_for_files.append(corr_file_smcLeft)
check_for_files.append(corr_file_lpcLeft)
check_for_files.append(corr_file_dpfcLeft)
check_for_files.append(corr_file_tmpcLeft)
check_for_files.append(corr_file_acRight)
check_for_files.append(corr_file_scRight)
check_for_files.append(corr_file_vcRight)
check_for_files.append(corr_file_mtRight)
check_for_files.append(corr_file_mtlRight)
check_for_files.append(corr_file_smcRight)
check_for_files.append(corr_file_lpcRight)
check_for_files.append(corr_file_dpfcRight)
check_for_files.append(corr_file_tmpcRight)
check_for_files.append(corr_file_mpfc)
check_for_files.append(corr_file_sma)
file_exist = [f for f in check_for_files if os.path.isfile(f)]
file_not_exist = list(set(file_exist) ^ set(check_for_files))
if not file_not_exist:
print('SC, AC, VC correlation files exists...')
else:
trans = f'/home/senthilp/caesar/camcan/cc700/camcan_coreg-master/trans/{subject}-trans.fif' # The transformation file obtained by coregistration
file_trans = pathlib.Path(trans)
file_ss = pathlib.Path(src_fname)
file_fm = pathlib.Path(fwd_fname)
file_proj = pathlib.Path(raw_proj)
file_cov = pathlib.Path(cov_fname)
file_rawcov = pathlib.Path(raw_cov_fname)
t1 = nib.load(t1_fname)
if not file_trans.exists():
print (f'{trans} File doesnt exist...')
sys.exit(0)
#info = mne.io.read_info(fname_meg)
# plot_registration(info, trans, subject, subjects_dir)
if not file_ss.exists():
src = compute_SourceSpace(subject, subjects_dir, src_fname, source_voxel_coords, plot=True, ss='volume',
volume_spacing=volume_spacing)
seed_l_sc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SSC_Left'])
seed_r_sc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SSC_Right'])
seed_l_ac = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['AC_Left'])
seed_r_ac = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['AC_Right'])
seed_l_vc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['VC_Left'])
seed_r_vc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['VC_Right'])
seed_l_mt = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MT+_Left'])
seed_r_mt = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MT+_Right'])
seed_l_mtl = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MTL_Left'])
seed_r_mtl = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MTL_Right'])
seed_l_smc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMC_Left'])
seed_r_smc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMC_Right'])
seed_l_lpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['LPC_Left'])
seed_r_lpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['LPC_Right'])
seed_l_dpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['DPFC_Left'])
seed_r_dpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['DPFC_Right'])
seed_l_tmpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['TMPC_Left'])
seed_r_tmpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['TMPC_Right'])
seed_mpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MPFC_MidBrain'])
seed_sma = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMA_MidBrain'])
src_inuse = np.where(src[0]['inuse'] == 1)
loc_l_sc = src_inuse[0][0]
loc_r_sc = src_inuse[0][1]
loc_l_ac = src_inuse[0][2]
loc_r_ac = src_inuse[0][3]
loc_l_vc = src_inuse[0][4]
loc_r_vc = src_inuse[0][5]
loc_l_mt = src_inuse[0][6]
loc_r_mt = src_inuse[0][7]
loc_l_mtl = src_inuse[0][8]
loc_r_mtl = src_inuse[0][9]
loc_l_smc = src_inuse[0][10]
loc_r_smc = src_inuse[0][11]
loc_l_lpc = src_inuse[0][12]
loc_r_lpc = src_inuse[0][13]
loc_l_dpfc = src_inuse[0][14]
loc_r_dpfc = src_inuse[0][15]
loc_l_tmpc = src_inuse[0][16]
loc_r_tmpc = src_inuse[0][17]
loc_mpfc = src_inuse[0][18]
loc_sma = src_inuse[0][19]
src[0]['rr'][loc_l_sc] = seed_l_sc
src[0]['rr'][loc_r_sc] = seed_r_sc
src[0]['rr'][loc_l_ac] = seed_l_ac
src[0]['rr'][loc_r_ac] = seed_r_ac
src[0]['rr'][loc_l_vc] = seed_l_vc
src[0]['rr'][loc_r_vc] = seed_r_vc
src[0]['rr'][loc_l_mt] = seed_l_mt
src[0]['rr'][loc_r_mt] = seed_r_mt
src[0]['rr'][loc_l_mtl] = seed_l_mtl
src[0]['rr'][loc_r_mtl] = seed_r_mtl
src[0]['rr'][loc_l_smc] = seed_l_smc
src[0]['rr'][loc_r_smc] = seed_r_smc
src[0]['rr'][loc_l_lpc] = seed_l_lpc
src[0]['rr'][loc_r_lpc] = seed_r_lpc
src[0]['rr'][loc_l_dpfc] = seed_l_dpfc
src[0]['rr'][loc_r_dpfc] = seed_r_dpfc
src[0]['rr'][loc_l_tmpc] = seed_l_tmpc
src[0]['rr'][loc_r_tmpc] = seed_r_tmpc
src[0]['rr'][loc_mpfc] = seed_mpfc
src[0]['rr'][loc_sma] = seed_sma
src.save(src_fname, overwrite=True)
src = mne.read_source_spaces(src_fname)
#view_SS_brain(subject, subjects_dir, src)
if not file_fm.exists():
forward_model(subject, subjects_dir, fname_meg, trans, src, fwd_fname)
fwd = mne.read_forward_solution(fwd_fname)
# sensitivty_plot(subject, subjects_dir, fwd)
raw = mne.io.read_raw_fif(fname_meg, verbose='error', preload=True)
srate = raw.info['sfreq']
n_time_samps = raw.n_times
time_secs = raw.times
ch_names = raw.ch_names
n_chan = len(ch_names)
freq_res = srate/n_time_samps
print('\n')
print('-------------------------- Data summary-------------------------------')
print(f'Subject {subject}')
print(f"Frequency resolution {freq_res} Hz")
print(f"The first few channel names are {ch_names[:3]}")
print(f"The last time sample at {time_secs[-1]} seconds.")
print(f"Sampling Frequency (No of time points/sec) {srate} Hz")
print(f"Miscellaneous acquisition info {raw.info['description']}")
print(f"Bad channels marked during data acquisition {raw.info['bads']}")
print(f"Convert time in sec ( 60s ) to ingeter index {raw.time_as_index(60)}") # Convert time to indices
print(f"The raw data object has {n_time_samps} time samples and {n_chan} channels.")
print('------------------------------------------------------------------------')
print('\n')
# raw.plot(n_channels=10, scalings='auto', title='Data from arrays', show=True, block=True)
if not file_proj.exists():
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2, ch_name='ECG063')
projs_eog1, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG061')
projs_eog2, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG062')
if projs_ecg is not None:
mne.write_proj(heartbeat_proj, projs_ecg) # Saving projectors
raw.info['projs'] += projs_ecg
if projs_eog1 is not None:
mne.write_proj(eye_proj1, projs_eog1)
raw.info['projs'] += projs_eog1
if projs_eog2 is not None:
mne.write_proj(eye_proj2, projs_eog2)
raw.info['projs'] += projs_eog2
raw.apply_proj()
raw.save(raw_proj, proj=True, overwrite=True)
print(raw_proj)
raw_proj_applied = mne.io.read_raw_fif(raw_proj, verbose='error', preload=True)
print(f'High-pass filtering data at 0.5 Hz')
raw_proj_applied.filter(l_freq=0.5, h_freq=None, method='iir')
if not file_cov.exists():
cov = mne.compute_raw_covariance(raw_proj_applied) # compute before band-pass of interest
mne.write_cov(cov_fname, cov)
cov = mne.read_cov(cov_fname)
# cov.plot(raw.info, proj=True, exclude='bads', show_svd=False
# raw_proj_applied.crop(tmax=10)
do_epochs = False
l_freq = freq-2.0
h_freq = freq+2.0
print(f'Band pass filter data [{l_freq}, {h_freq}]')
raw_proj_filtered = raw_proj_applied.filter(l_freq=l_freq, h_freq=h_freq)
if do_epochs:
print('Segmenting raw data...')
events = mne.make_fixed_length_events(raw_proj_filtered, duration=5.)
raw_proj_filtered = mne.Epochs(raw_proj_filtered, events=events, tmin=0, tmax=5.,
baseline=None, preload=True)
data_cov = mne.compute_covariance(raw_proj_filtered)
else:
if not file_rawcov.exists():
data_cov = mne.compute_raw_covariance(raw_proj_filtered)
mne.write_cov(raw_cov_fname, data_cov)
else:
data_cov = mne.read_cov(file_rawcov)
filters = make_lcmv(raw_proj_filtered.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
raw_proj_filtered_comp = raw_proj_filtered.apply_hilbert()
if do_epochs:
stcs = apply_lcmv_epochs(raw_proj_filtered_comp, filters, return_generator=False)
else:
stcs = apply_lcmv_raw(raw_proj_filtered_comp, filters, verbose=True)
stcs = [stcs]
# Power Envelope Correlation
print(f'Computing Power Envelope Correlation for {subject}....Orthogonalize {ortho_flag}')
all_corr = envelope_correlation(stcs, combine=None, orthogonalize=False,
log=False, absolute=True, verbose=None)
np.save(corr_file_scLeft, all_corr[seed_left_sc])
np.save(corr_file_acLeft, all_corr[seed_left_ac])
np.save(corr_file_vcLeft, all_corr[seed_left_vc])
np.save(corr_file_mtLeft, all_corr[seed_left_mt])
np.save(corr_file_mtlLeft, all_corr[seed_left_mtl])
np.save(corr_file_smcLeft, all_corr[seed_left_smc])
np.save(corr_file_lpcLeft, all_corr[seed_left_lpc])
np.save(corr_file_dpfcLeft, all_corr[seed_left_dpfc])
np.save(corr_file_tmpcLeft, all_corr[seed_left_tmpc])
np.save(corr_file_scRight, all_corr[seed_right_sc])
np.save(corr_file_acRight, all_corr[seed_right_ac])
np.save(corr_file_vcRight, all_corr[seed_right_vc])
np.save(corr_file_mtRight, all_corr[seed_right_mt])
np.save(corr_file_mtlRight, all_corr[seed_right_mtl])
np.save(corr_file_smcRight, all_corr[seed_right_smc])
np.save(corr_file_lpcRight, all_corr[seed_right_lpc])
np.save(corr_file_dpfcRight, all_corr[seed_right_dpfc])
np.save(corr_file_tmpcRight, all_corr[seed_right_tmpc])
np.save(corr_file_mpfc, all_corr[seed_mpfc_index])
np.save(corr_file_sma, all_corr[seed_sma_index])
del stcs
def compute_src_label_ts(subject,
crop_to=[0, 250],
resample_to=100.,
bads=None,
mag_reject=5e-12,
win_len=2000,
n_wins=11,
verbose=None,
lambda2=1. / 9.,
inv_method='dSPM',
extract_ts_mode='mean_flip'):
"""
Compute source label time series
"""
"""
Compute anatomy
"""
hcp.make_mne_anatomy(subject,
subjects_dir=subjects_dir,
hcp_path=hcp_path,
recordings_path=hcp_path)
"""
Read surface labels
"""
labels = read_labels_from_annot(subject,
parc='aparc',
subjects_dir=subjects_dir)
labels_fsav = read_labels_from_annot('fsaverage',
parc='aparc',
subjects_dir=subjects_dir)
"""
Read raw data
"""
raw = hcp.read_raw(subject=subject,
data_type=data_type,
hcp_path=hcp_path,
run_index=run_index)
raw.load_data()
raw.crop(crop_to[0], crop_to[1])
raw.resample(resample_to)
raw.info['bads'] = bads
hcp.preprocessing.set_eog_ecg_channels(raw)
hcp.preprocessing.apply_ref_correction(raw)
info = raw.info.copy()
raw.info['projs'] = []
ecg_ave = create_ecg_epochs(raw).average()
eog_ave = create_eog_epochs(raw).average()
ssp_eog, _ = compute_proj_eog(raw,
n_grad=1,
n_mag=1,
average=True,
reject=dict(mag=mag_reject))
raw.add_proj(ssp_eog, remove_existing=True)
n_fft = next_fast_len(int(round(4 * raw.info['sfreq'])))
sfreq = raw.info['sfreq']
"""
Compute forward model
"""
src_outputs = hcp.anatomy.compute_forward_stack(
subject=subject,
subjects_dir=subjects_dir,
hcp_path=hcp_path,
recordings_path=hcp_path,
src_params=dict(add_dist=False),
info_from=dict(data_type=data_type, run_index=run_index))
fwd = src_outputs['fwd']
"""
Compute noise covariance
"""
raw_noise = hcp.read_raw(subject=subject,
hcp_path=hcp_path,
data_type='noise_empty_room')
raw_noise.load_data()
hcp.preprocessing.apply_ref_correction(raw_noise)
raw_noise.add_proj(ssp_eog)
noise_cov = compute_raw_covariance(raw_noise, method='oas')
"""
Compute inverse operator
"""
raw.info = info
inv_op = make_inverse_operator(raw.info,
forward=fwd,
noise_cov=noise_cov,
verbose=verbose)
"""
Compute source activity
"""
wins = [[0, win_len]]
for i in range(n_wins):
new_wins = [
wins[0][0] + (win_len * (i + 1)), wins[0][1] + (win_len * (i + 1))
]
wins.append(new_wins)
raw_srcs = []
for win in wins:
res = apply_inverse_raw(raw,
inv_op,
lambda2=lambda2,
method=inv_method,
label=None,
start=win[0],
stop=win[1],
nave=1,
time_func=None,
pick_ori=None,
buffer_size=None,
prepared=False,
method_params=None,
verbose=verbose)
raw_srcs.append(res)
"""
Compute source label time series
"""
src = inv_op['src']
label_ts = extract_label_time_course(raw_srcs,
labels,
src,
mode=extract_ts_mode,
return_generator=False)
return label_ts, sfreq
#def process_maxfilter(subject):
raw_fname = op.join(data_path, 'rest', 'sub-' + subject, 'meg', 'rest_raw.fif')
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw_length = (raw.last_samp - raw.first_samp) / raw.info['sfreq']
raw.info['bads'] += [u'MEG2113', u'MEG1941', u'MEG1412', u'MEG2331']
raw = mne.preprocessing.maxwell_filter(raw,
calibration=cal,
cross_talk=ctc,
st_duration=35.,
st_correlation=.96,
origin=(0., 0., 0.04))
projs_ecg, ecg_events = compute_proj_ecg(raw, n_grad=1, n_mag=2)
projs_eog, eog_events = compute_proj_eog(raw, n_grad=1, n_mag=2)
raw.info['projs'] += projs_ecg
raw.info['projs'] += projs_eog
raw.apply_proj()
raw.filter(None,
45,
l_trans_bandwidth='auto',
h_trans_bandwidth='auto',
filter_length='auto',
phase='zero',
fir_window='hann')
cov = mne.compute_raw_covariance(raw, tmin=0, tmax=None)
subject = 'bst_resting'
trans = op.join(data_path, 'MEG', 'bst_resting', 'bst_resting-trans.fif')
bem = op.join(subjects_dir, subject, 'bem', subject + '-5120-bem-sol.fif')
raw_fname = op.join(data_path, 'MEG', 'bst_resting',
'subj002_spontaneous_20111102_01_AUX.ds')
crop_to = 60.
##############################################################################
# Here we do some things in the name of speed, such as crop (which will
# hurt SNR) and downsample. Then we compute SSP projectors and apply them.
raw = mne.io.read_raw_ctf(raw_fname, verbose='error')
raw.crop(0, crop_to).load_data().pick_types(meg=True, eeg=False).resample(80)
raw.apply_gradient_compensation(3)
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2)
projs_eog, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='MLT31-4407')
raw.info['projs'] += projs_ecg
raw.info['projs'] += projs_eog
raw.apply_proj()
cov = mne.compute_raw_covariance(raw) # compute before band-pass of interest
##############################################################################
# Now we band-pass filter our data and create epochs.
raw.filter(14, 30)
events = mne.make_fixed_length_events(raw, duration=5.)
epochs = mne.Epochs(raw, events=events, tmin=0, tmax=5.,
baseline=None, reject=dict(mag=8e-13), preload=True)
del raw
##############################################################################
eog_evoked.plot_joint()
###############################################################################
# Just like we did with the heartbeat artifact, we can compute SSP projectors
# for the ocular artifact using `~mne.preprocessing.compute_proj_eog`,
# which again takes a `~mne.io.Raw` object as input and returns the
# requested number of projectors for magnetometers, gradiometers, and EEG
# channels (default is two projectors for each channel type). This time, we'll
# pass ``no_proj`` parameter (so we get back only the new EOG projectors, not
# also the existing projectors in the `~mne.io.Raw` object), and we'll
# ignore the events array by assigning it to ``_`` (the conventional way of
# handling unwanted return elements in Python).
eog_projs, _ = compute_proj_eog(raw,
n_grad=1,
n_mag=1,
n_eeg=1,
reject=None,
no_proj=True)
###############################################################################
# Just like with the empty-room and ECG projectors, we can visualize the scalp
# distribution:
mne.viz.plot_projs_topomap(eog_projs, info=raw.info)
###############################################################################
# Now we repeat the plot from above (with empty room and ECG projectors) and
# compare it to a plot with empty room, ECG, and EOG projectors, to see how
# well the ocular artifacts have been repaired:
for title in ('Without', 'With'):
subject = 'bst_resting'
trans = op.join(data_path, 'MEG', 'bst_resting', 'bst_resting-trans.fif')
bem = op.join(subjects_dir, subject, 'bem', subject + '-5120-bem-sol.fif')
raw_fname = op.join(data_path, 'MEG', 'bst_resting',
'subj002_spontaneous_20111102_01_AUX.ds')
crop_to = 60.
##############################################################################
# Here we do some things in the name of speed, such as crop (which will
# hurt SNR) and downsample. Then we compute SSP projectors and apply them.
raw = mne.io.read_raw_ctf(raw_fname, verbose='error')
raw.crop(0, crop_to).pick_types(meg=True, eeg=False).load_data().resample(80)
raw.apply_gradient_compensation(3)
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2)
projs_eog, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='MLT31-4407')
raw.info['projs'] += projs_ecg
raw.info['projs'] += projs_eog
raw.apply_proj()
cov = mne.compute_raw_covariance(raw) # compute before band-pass of interest
##############################################################################
# Now we band-pass filter our data and create epochs.
raw.filter(14, 30)
events = mne.make_fixed_length_events(raw, duration=5.)
epochs = mne.Epochs(raw,
events=events,
tmin=0,
tmax=5.,
baseline=None,
if pieces[-15:] == 'tsss_mc_EOG.fif':
final_path = subject_folder + pieces
print(final_path)
raw = mne.io.read_raw_fif(final_path, preload=True)
projection_ecg, ecg_events = compute_proj_ecg(raw,
reject=reject,
l_freq=1,
h_freq=100,
tstart=5,
ecg_h_freq=25,
ecg_l_freq=1,
average=True,
ch_name='MEG1541')
projection_eog, eog_events = compute_proj_eog(raw,
reject=reject,
l_freq=1,
h_freq=40,
average=True)
raw_ecg_removed = raw.add_proj(projection_ecg,
remove_existing=True)
raw_all_removed = raw_ecg_removed.add_proj(projection_eog)
save_path = subject_folder + pieces[:-4] + '_ssp.fif'
print(save_path)
raw_all_removed.apply_proj().save(save_path, overwrite=True)
del projection_ecg, ecg_events, projection_eog, eog_events
del raw_ecg_removed, raw_all_removed
log.write(subject + ' processed\n')
# second loop for the rest
for subject in dirnames:
subject_folder = root_path + subject + '/'
def run_ssp(*, cfg, subject, session=None):
# compute SSP on first run of raw
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=cfg.runs[0],
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root)
# Prepare a name to save the data
raw_fname_in = bids_path.copy().update(processing='filt', suffix='raw',
check=False)
# when saving proj, use run=None
proj_fname_out = bids_path.copy().update(run=None, suffix='proj',
check=False)
msg = f'Input: {raw_fname_in}, Output: {proj_fname_out}'
logger.info(**gen_log_kwargs(message=msg, subject=subject,
session=session))
if raw_fname_in.copy().update(split='01').fpath.exists():
raw_fname_in.update(split='01')
raw = mne.io.read_raw_fif(raw_fname_in)
msg = 'Computing SSPs for ECG'
logger.debug(**gen_log_kwargs(message=msg, subject=subject,
session=session))
ecg_projs = []
ecg_epochs = create_ecg_epochs(raw)
if len(ecg_epochs) >= config.min_ecg_epochs:
if cfg.ssp_reject_ecg == 'autoreject_global':
reject_ecg_ = config.get_ssp_reject(
ssp_type='ecg',
epochs=ecg_epochs)
ecg_projs, _ = compute_proj_ecg(raw,
average=cfg.ecg_proj_from_average,
reject=reject_ecg_,
**cfg.n_proj_ecg)
else:
reject_ecg_ = config.get_ssp_reject(
ssp_type='ecg',
epochs=None)
ecg_projs, _ = compute_proj_ecg(raw,
average=cfg.ecg_proj_from_average,
reject=reject_ecg_,
**cfg.n_proj_ecg)
if not ecg_projs:
msg = ('Not enough ECG events could be found. No ECG projectors are '
'computed.')
logger.info(**gen_log_kwargs(message=msg, subject=subject,
session=session))
msg = 'Computing SSPs for EOG'
logger.debug(**gen_log_kwargs(message=msg, subject=subject,
session=session))
if cfg.eog_channels:
ch_names = cfg.eog_channels
assert all([ch_name in raw.ch_names for ch_name in ch_names])
else:
ch_names = None
eog_projs = []
eog_epochs = create_eog_epochs(raw)
if len(eog_epochs) >= config.min_eog_epochs:
if cfg.ssp_reject_eog == 'autoreject_global':
reject_eog_ = config.get_ssp_reject(
ssp_type='eog',
epochs=eog_epochs)
eog_projs, _ = compute_proj_eog(raw,
average=cfg.eog_proj_from_average,
reject=reject_eog_,
**cfg.n_proj_eog)
else:
reject_eog_ = config.get_ssp_reject(
ssp_type='eog',
epochs=None)
eog_projs, _ = compute_proj_eog(raw,
average=cfg.eog_proj_from_average,
reject=reject_eog_,
**cfg.n_proj_eog)
if not eog_projs:
msg = ('Not enough EOG events could be found. No EOG projectors are '
'computed.')
logger.info(**gen_log_kwargs(message=msg, subject=subject,
session=session))
mne.write_proj(proj_fname_out, eog_projs + ecg_projs)
# Compute SSP projections # ----------------------- # # First let's do ECG. projs, events = compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True) print(projs) ecg_projs = projs[-2:] mne.viz.plot_projs_topomap(ecg_projs) ############################################################################### # Now let's do EOG. Here we compute an EEG projector, and need to pass # the measurement info so the topomap coordinates can be created. projs, events = compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True) print(projs) eog_projs = projs[-3:] mne.viz.plot_projs_topomap(eog_projs, info=raw.info) ############################################################################## # Apply SSP projections # --------------------- # # MNE is handling projections at the level of the info, # so to register them populate the list that you find in the 'proj' field raw.info['projs'] += eog_projs + ecg_projs #############################################################################
raw.pick_channels(chan_list)
# set eog channels
raw.set_channel_types({'LOc':'eog','ROc':'eog'})
# load the montage to the raw object
raw.set_montage(montage)
# we don't filter the eog channels
picks=mne.pick_types(raw.info,meg=False,eeg=True,eog=False,stim=False)
raw.filter(None,100,l_trans_bandwidth=0.01,
h_trans_bandwidth='auto',filter_length=30,picks=picks,)#n_jobs=4)
raw.notch_filter(np.arange(60,241,60), picks=picks)
# re-reference to the average
raw.set_eeg_reference().apply_proj()
# compute the eog projects, and here is one parameter we will tune: n_eeg.
# too high will throw away too much signal with the noise, but too low, we
# will keep too much noise
projs, events = compute_proj_eog(raw, n_eeg=3, average=True,reject={'eeg':80,'eog':100},)#n_jobs=4)
#layout = mne.channels.find_layout(raw.info,ch_type='eeg')
#mne.viz.plot_projs_topomap(projs,layout)
raw.info['projs'] += projs[1:]
raw.apply_proj() # apply projects
fileName = raw_file[:-5]
raw.save(saving_dir + fileName+'_raw_ssp.fif',proj=True,overwrite=True)
del raw
#noise_cov=mne.compute_raw_covariance(raw,picks=picks)
working_dir = 'D:\\NING - spindle\\'
saving_dir = 'D:\\NING - spindle\\training set\\'
sub_folders = [f for f in os.listdir(working_dir) if ('suj' in f)]
vhdrs = []
for suj_folder in sub_folders: