def calc_fwd_inv(subject, raw_fname, empty_fname, bad_channels_fname, overwrite_inv=False,
overwrite_fwd=False):
# python -m src.preproc.eeg -s nmr00857 -f calc_inverse_operator,make_forward_solution
# --overwrite_inv 0 --overwrite_fwd 0 -t epilepsy
# --raw_fname /autofs/space/frieda_001/users/valia/epilepsy/5241495_00857/subj_5241495/190123/5241495_01_raw.fif
# --empty_fname /autofs/space/frieda_001/users/valia/epilepsy/5241495_00857/subj_5241495/190123/5241495_roomnoise_raw.fif
# --use_empty_room_for_noise_cov 1
# --bad_channels EEG061,EEG02,EEG042,MEG0112,MEG0113
bad_channels = ','.join(matlab_utils.matlab_cell_arrays_to_dict(bad_channels_fname)['label'])
trans_fname = op.join(MEG_DIR, subject, '{}-trans.fif'.format(subject))
args = meg.read_cmd_args(dict(
subject=subject,
mri_subject=subject,
function='calc_inverse_operator,make_forward_solution',
task='kaggle',
inv_fname=op.join(MEG_DIR, subject, '{}-meeg-kaggle-inv.fif'.format(subject)),
fwd_fname=op.join(MEG_DIR, subject, '{}-meeg-kaggle-fwd.fif'.format(subject)),
fwd_usingEEG=True,
overwrite_inv=overwrite_inv,
overwrite_fwd=overwrite_fwd,
use_empty_room_for_noise_cov=True,
bad_channels=bad_channels,
raw_fname=raw_fname,
empty_fname=empty_fname,
cor_fname=trans_fname
))
meg.call_main(args)
def dipole_fit():
mu.add_mmvt_code_root_to_path()
from src.preproc import meg
importlib.reload(meg)
subject = mu.get_user()
args = meg.read_cmd_args(
dict(subject=subject, mri_subject=subject, atlas=mu.get_atlas()))
meg.init(subject, args)
dipoles_times = [(bpy.context.scene.meg_dipole_fit_tmin,
bpy.context.scene.meg_dipole_fit_tmax)]
dipoloes_title = mask_roi = MEGPanel.current_cluster['intersects'][0][
'name']
meg.dipoles_fit(dipoles_times,
dipoloes_title,
None,
mu.get_real_fname('meg_noise_cov_fname'),
mu.get_real_fname('meg_evoked_fname'),
mu.get_real_fname('head_to_mri_trans_mat_fname'),
5.,
bpy.context.scene.meg_dipole_fit_use_meg,
bpy.context.scene.meg_dipole_fit_use_eeg,
mask_roi=mask_roi,
do_plot=False,
n_jobs=4)
def meg_calc_labels_ts(subject, inv_method='MNE', em='mean_flip', atlas='electrodes_labels', remote_subject_dir='',
meg_remote_dir='', empty_fname='', cor_fname='', use_demi_events=True, n_jobs=-1):
functions = 'calc_epochs,calc_evokes,make_forward_solution,calc_inverse_operator,calc_stc,calc_labels_avg_per_condition'
meg_args = meg.read_cmd_args(dict(
subject=subject, mri_subject=subject,
task='rest', inverse_method=inv_method, extract_mode=em, atlas=atlas,
single_trial_stc=True,
recreate_src_spacing='ico5',
# fwd_recreate_source_space=True,
# recreate_bem_solution=True,
remote_subject_meg_dir=meg_remote_dir,
remote_subject_dir=remote_subject_dir,
empty_fname=empty_fname,
cor_fname=cor_fname,
function=functions,
use_demi_events=use_demi_events,
windows_length=10000,
windows_shift=5000,
# overwrite_fwd=True,
# overwrite_inv=True,
# overwrite_labels_data=True,
using_auto_reject=False,
use_empty_room_for_noise_cov=True,
read_only_from_annot=False,
n_jobs=n_jobs
))
return meg.call_main(meg_args)
def calc_sample_meg_data():
args = meg.read_cmd_args(
dict(subject='sample',
function='calc_epochs,calc_evokes,calc_stc',
contrast='audvis',
task='audvis',
pick_meg=True,
pick_eeg=False,
fwd_usingMEG=True,
fwd_usingEEG=False,
fname_format='{subject}_audvis_meg-{ana_type}.{file_type}',
fname_format_cond=
'{subject}_audvis_meg_{cond}-{ana_type}.{file_type}',
trans_fname=op.join(MNE_ROOT, 'sample_audvis_raw-trans.fif'),
events_fname=op.join(MNE_ROOT, 'sample_audvis_raw-eve.fif'),
raw_fname=op.join(MNE_ROOT, 'sample_audvis_raw.fif'),
inv_fname=op.join(MNE_ROOT,
'sample_audvis-meg-oct-6-meg-inv.fif'),
fwd_fname=op.join(MNE_ROOT, 'sample_audvis-meg-oct-6-fwd.fif'),
conditions=['LA', 'RA'],
read_events_from_file=True,
t_min=-0.2,
t_max=0.5,
overwrite_epochs=False,
overwrite_evoked=False,
overwrite_stc=True))
meg.call_main(args)
def calc_msit(args):
# python -m src.preproc.meg -s ep001 -m mg78 -a laus250 -t MSIT
# --contrast interference --t_max 2 --t_min -0.5 --data_per_task 1 --read_events_from_file 1
# --events_file_name {subject}_msit_nTSSS_interference-eve.txt --cleaning_method nTSSS
args = meg.read_cmd_args(
dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
function=args.real_function,
data_per_task=True,
atlas='laus250',
contrast='interference',
cleaning_method='nTSSS',
t_min=-0.5,
t_max=2,
# calc_epochs_from_raw=True,
read_events_from_file=True,
# remote_subject_meg_dir='/autofs/space/sophia_002/users/DARPA-MEG/project_orig_msit',
events_file_name='{subject}_msit_nTSSS_interference-eve.txt',
reject=False,
# save_smoothed_activity=True,
# stc_t=1189,
morph_to_subject='fsaverage5',
extract_mode=['mean_flip', 'mean', 'pca_flip']))
meg.call_main(args)
def calc_meg_epochs(args):
empty_fnames, cors, days = get_empty_fnames(args.subject[0], args.tasks,
args)
times = (-2, 4)
for task in args.tasks:
args = meg.read_cmd_args(
dict(subject=args.subject,
mri_subject=args.subject,
task=task,
remote_subject_dir=
'/autofs/space/lilli_001/users/DARPA-Recons/{subject}',
get_task_defaults=False,
fname_format='{}_{}_nTSSS-ica-raw'.format(
'{subject}', task.lower()),
empty_fname=empty_fnames[task],
function='calc_epochs,calc_evokes',
conditions=task.lower(),
data_per_task=True,
normalize_data=False,
t_min=times[0],
t_max=times[1],
read_events_from_file=False,
stim_channels='STI001',
use_empty_room_for_noise_cov=True,
n_jobs=args.n_jobs))
meg.call_main(args)
def calc_rest(args):
# '-s hc029 -a laus125 -t rest -f calc_evoked,make_forward_solution,calc_inverse_operator --reject 0 --remove_power_line_noise 0 --windows_length 1000 --windows_shift 500 --remote_subject_dir "/autofs/space/lilli_001/users/DARPA-Recons/hc029"''
# '-s hc029 -a laus125 -t rest -f calc_stc,calc_labels_avg_per_condition --single_trial_stc 1 --remote_subject_dir "/autofs/space/lilli_001/users/DARPA-Recons/hc029"'
# '-s subject-name -a atlas-name -t rest -f rest_functions' --l_freq 8 --h_freq 13 --windows_length 500 --windows_shift 100
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
atlas='laus125',
function='rest_functions',
task='rest',
cleaning_method='tsss',
reject=False, # Should be True here, unless you are dealling with bad data...
remove_power_line_noise=True,
l_freq=3, h_freq=80,
windows_length=500,
windows_shift=100,
inverse_method='MNE',
remote_subject_dir='/autofs/space/lilli_001/users/DARPA-Recons/{subject}',
# This properties are set automatically if task=='rest'
# calc_epochs_from_raw=True,
# single_trial_stc=True,
# use_empty_room_for_noise_cov=True,
# windows_num=10,
# baseline_min=0,
# baseline_max=0,
))
meg.call_main(args)
def _calc_pvals_fMRI_clusters(p, extract_time_series_for_clusters=False):
subject, overwrite = p
stc_name = 'dSPM_mean_flip_vertices_power_spectrum_stat'
if not utils.both_hemi_files_exist(
op.join(MMVT_DIR, subject, 'meg', '{}-{}.stc'.format(
stc_name, '{hemi}'))):
print('{}: Can\'t find {}!'.format(subject, stc_name))
return False
args.subject = subject
clusters_root_fol = utils.make_dir(
op.join(MMVT_DIR, subject, 'meg', 'clusters'))
res_fname = op.join(
clusters_root_fol,
'clusters_labels_dSPM_mean_flip_vertices_power_spectrum_stat.pkl')
if not op.isfile(res_fname) or args.overwrite:
utils.delete_folder_files(clusters_root_fol)
_args = meg.read_cmd_args(
dict(subject=subject,
mri_subject=subject,
atlas='MSIT_I-C',
function='find_functional_rois_in_stc',
stc_name=stc_name,
threshold=-np.log10(0.01),
threshold_is_precentile=False,
extract_time_series_for_clusters=False,
save_func_labels=True,
calc_cluster_contours=True,
n_jobs=args.n_jobs))
try:
meg.call_main(_args)
except:
print(traceback.format_exc())
if not op.isfile(res_fname):
print('Cluster output can\'t be found!')
return False
def calc_msit(args):
# python -m src.preproc.meg -s ep001 -m mg78 -a laus250 -t MSIT
# --contrast interference --t_max 2 --t_min -0.5 --data_per_task 1 --read_events_from_file 1
# --events_fname {subject}_msit_nTSSS_interference-eve.txt --cleaning_method nTSSS
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
# function=args.real_function,
function='read_sensors_layout,calc_epochs,calc_evokes,calc_stc,calc_labels_avg_per_condition,calc_labels_min_max',
data_per_task=True,
atlas=args.atlas,
contrast='interference',
cleaning_method='nTSSS',
t_min=-0.5,
t_max=2,
# calc_epochs_from_raw=True,
read_events_from_file=True,
# remote_subject_meg_dir='/autofs/space/sophia_002/users/DARPA-MEG/project_orig_msit',
events_fname='{subject}_msit_nTSSS_interference-eve.txt',
reject=False,
# save_smoothed_activity=True,
# stc_t=1189,
morph_to_subject = 'fsaverage5',
extract_mode=['mean_flip'], #, 'mean', 'pca_flip'],
pick_ori='normal',
overwrite_epochs=False,
overwrite_evoked=True,
overwrite_stc=False,
overwrite_labels_data=False,
overwrite_sensors=False
))
meg.call_main(args)
def meg_preproc(subject, inv_method='MNE', em='mean_flip', atlas='electrodes_labels', remote_subject_dir='',
meg_remote_dir='', empty_fname='', cor_fname='', use_demi_events=True, calc_labels_avg=False,
overwrite=False, n_jobs=-1):
functions = 'calc_epochs,calc_evokes,make_forward_solution,calc_inverse_operator'
if calc_labels_avg:
functions += ',calc_stc,calc_labels_avg_per_condition'
meg_args = meg.read_cmd_args(dict(
subject=subject, mri_subject=subject,
task='rest', inverse_method=inv_method, extract_mode=em, atlas=atlas,
remote_subject_meg_dir=meg_remote_dir,
remote_subject_dir=remote_subject_dir,
empty_fname=empty_fname,
cor_fname=cor_fname,
function=functions,
use_demi_events=use_demi_events,
windows_length=10000,
windows_shift=5000,
# power_line_notch_widths=5,
using_auto_reject=False,
# reject=False,
use_empty_room_for_noise_cov=True,
read_only_from_annot=False,
overwrite_epochs=overwrite,
overwrite_evoked=overwrite,
n_jobs=n_jobs
))
return meg.call_main(meg_args)
def calc_msit_functional_rois(args):
clusters_root_fol = utils.make_dir(
op.join(MMVT_DIR, args.subject[0], 'meg', 'clusters'))
utils.delete_folder_files(clusters_root_fol)
# conditions = ['neutral', 'interference']
# for cond in conditions:
_args = meg.read_cmd_args(
dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
data_per_task=True,
# atlas='laus125',
function='find_functional_rois_in_stc',
inverse_method='dSPM',
stc_name=
'{subject}_msit_nTSSS_interference_interference-neutral_1-15-dSPM',
inv_fname='{subject}_msit_nTSSS_interference_1-15-inv',
label_name_template='*',
peak_mode='pos',
threshold=99.5,
min_cluster_max=5,
min_cluster_size=100,
# recreate_src_spacing='ico5'
# clusters_label='precentral'
))
meg.call_main(_args)
def calc_mne_python_sample_data(args):
import mne
mne_sample_data_fol = mne.datasets.sample.data_path()
trans_fname = op.join(mne_sample_data_fol, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
args = meg.read_cmd_args(
dict(
subject=args.subject,
mri_subject=args.mri_subject,
function='read_sensors_layout,calc_evokes',
# atlas='laus250',
contrast='audvis',
task='audvis',
trans_fname=trans_fname,
fname_format='{subject}_audvis-{ana_type}.{file_type}',
fname_format_cond='{subject}_audvis_{cond}-{ana_type}.{file_type}',
conditions=['LA', 'RA'],
read_events_from_file=True,
t_min=-0.2,
t_max=0.5,
extract_mode=['mean_flip'], #, 'mean', 'pca_flip'],
overwrite_epochs=args.overwrite,
overwrite_evoked=True,
overwrite_sensors=True))
meg.call_main(args)
def crop_stc_no_baseline(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.fname_format = '{subject}_02_f2-35_all_correct_combined'
args.inv_fname_format = '{subject}_02_f2-35-ico-5-meg-eeg'
args.stc_t_min = -0.1
args.stc_t_max = 0.15
args.base_line_max = None
meg.run_on_subjects(args)
def meg_remove_artifcats(subject, raw_fname):
meg_args = meg.read_cmd_args(dict(
subject=subject, mri_subject=subject,
function='remove_artifacts',
raw_fname=raw_fname,
overwrite_ica=True
))
return meg.call_main(meg_args)
def read_cmd_args(argv=None):
args = meg.read_cmd_args(argv)
args.pick_meg = False
args.pick_eeg = True
args.reject = False
args.fwd_usingMEG = False
args.fwd_usingEEG = True
return args
def crop_stc_no_baseline(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.fname_format = '{subject}_02_f2-35_all_correct_combined'
args.inv_fname_format = '{subject}_02_f2-35-ico-5-meg-eeg'
args.stc_t_min = -0.1
args.stc_t_max = 0.15
args.base_line_max = None
meg.run_on_subjects(args)
def read_epoches_and_calc_activity(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.function = [
'calc_stc', 'calc_labels_avg_per_condition', 'smooth_stc',
'save_activity_map'
]
args.pick_ori = 'normal'
args.colors_map = 'jet'
meg.run_on_subjects(args)
def calc_power_spectrum(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
atlas='laus125',
function='calc_power_spectrum',
overwrite_labels_power_spectrum=True,
task='rest',
))
meg.call_main(args)
def calc_meg_power_spectrum(subject, atlas, inv_method, em, overwrite=False, n_jobs=-1):
meg_args = meg.read_cmd_args(dict(
subject=subject, mri_subject=subject,
task='rest', inverse_method=inv_method, extract_mode=em, atlas=atlas,
function='calc_labels_power_spectrum',
pick_ori='normal', # very important for calculation of the power spectrum
# max_epochs_num=20,
overwrite_labels_power_spectrum=overwrite,
n_jobs=n_jobs
))
return meg.call_main(meg_args)
def morph_stc(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
function='morph_stc',
task='MSIT',
data_per_task=True,
contrast='interference',
cleaning_method='nTSSS',
morph_to_subject='colin27'))
meg.call_main(args)
def calc_single_trial_labels_msit(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.task = 'MSIT'
args.atlas = 'laus250'
args.function = 'calc_stc_per_condition,calc_single_trial_labels_per_condition'
args.t_tmin = -0.5
args.t_tmax = 2
args.single_trial_stc = True
args.fwd_no_cond = False
args.files_includes_cond = True
args.constrast = 'interference'
meg.run_on_subjects(args)
def calc_single_trial_labels_msit(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.task = 'MSIT'
args.atlas = 'laus250'
args.function = 'calc_stc,calc_single_trial_labels_per_condition'
args.t_tmin = -0.5
args.t_tmax = 2
args.single_trial_stc = True
args.fwd_no_cond = False
args.files_includes_cond = True
args.constrast = 'interference'
meg.run_on_subjects(args)
def morph_stc(args):
args = meg.read_cmd_args(
dict(subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
data_per_task=True,
contrast='interference',
cleaning_method='nTSSS'))
morph_to_subject = 'ab' # 'fsaverage5'
fname_format, fname_format_cond, conditions = meg.init(
args.subject[0], args, args.mri_subject[0])
meg.morph_stc(conditions, morph_to_subject, args.inverse_method[0],
args.n_jobs)
def calc_functional_rois(args):
# -s DC -a laus250 -f find_functional_rois_in_stc --stc_name right-MNE-1-15 --label_name_template "precentral*" --inv_fname right-inv --threshold 99.5
args = meg.read_cmd_args(
dict(subject=args.subject,
mri_subject=args.mri_subject,
atlas='laus125',
function='find_functional_rois_in_stc',
inverse_method='MNE',
stc_name='right-MNE-1-15',
label_name_template='precentral*',
inv_fname='right-inv',
threshold=99.5))
meg.call_main(args)
def calc_labels_connectivity(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
function='calc_labels_connectivity',
data_per_task=True,
# atlas='laus125',
contrast='interference',
cleaning_method='nTSSS',
pick_ori='normal',
con_method='wpli2_debiased'
))
meg.call_main(args)
def calc_msit_stcs_diff(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
data_per_task=True,
contrast='interference',
cleaning_method='nTSSS'))
smooth = False
fname_format, fname_format_cond, conditions = meg.init(args.subject[0], args, args.mri_subject[0])
stc_template_name = meg.STC_HEMI_SMOOTH if smooth else meg.STC_HEMI
stc_fnames = [stc_template_name.format(cond=cond, method=args.inverse_method[0], hemi='lh') for cond in conditions.keys()]
output_fname = stc_template_name.format(cond='diff', method=args.inverse_method[0], hemi='lh')
meg.calc_stc_diff(*stc_fnames, output_fname)
def read_cmd_args(argv=None, subject='', mri_subject='', atlas=''):
if argv is None and subject != '':
mri_subject = subject if mri_subject == '' else mri_subject
argv = ['-s', subject, '-m', mri_subject]
args = meg.read_cmd_args(argv)
if atlas != '' and args.atlas != atlas:
args.atlas = atlas
args.pick_meg = False
args.pick_eeg = True
args.reject = False
args.fwd_usingMEG = False
args.fwd_usingEEG = True
args.modality = 'eeg'
return args
def calc_msit_evoked(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.task = 'MSIT'
args.atlas = 'laus250'
args.function = 'calc_evoked'
args.t_tmin = -0.5
args.t_tmax = 2
args.calc_epochs_from_raw = True
args.read_events_from_file = True
args.remote_subject_meg_dir = '/autofs/space/sophia_002/users/DARPA-MEG/project_orig_msit/events'
args.events_file_name = '{subject}_msit_nTSSS_interference-eve.txt'
args.reject = False
args.pick_eeg = True
meg.run_on_subjects(args)
def calc_mne_python_sample_data_stcs_diff(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
contrast = 'audvis',
fname_format = '{subject}_audvis-{ana_type}.{file_type}',
fname_format_cond = '{subject}_audvis_{cond}-{ana_type}.{file_type}',
conditions = ['LA', 'RA']
))
smooth = False
fname_format, fname_format_cond, conditions = meg.init(args.subject[0], args, args.mri_subject[0])
stc_template_name = meg.STC_HEMI_SMOOTH if smooth else meg.STC_HEMI
stc_fnames = [stc_template_name.format(cond=cond, method=args.inverse_method[0], hemi='lh') for cond in conditions.keys()]
output_fname = stc_template_name.format(cond='diff', method=args.inverse_method[0], hemi='lh')
meg.calc_stc_diff(*stc_fnames, output_fname)
def calc_mne_python_sample_data(args):
args = meg.read_cmd_args(
dict(
subject=args.subject,
mri_subject=args.mri_subject,
# atlas='laus250',
contrast='audvis',
fname_format='{subject}_audvis-{ana_type}.{file_type}',
fname_format_cond='{subject}_audvis_{cond}-{ana_type}.{file_type}',
conditions=['LA', 'RA'],
read_events_from_file=True,
t_min=-0.2,
t_max=0.5,
extract_mode=['mean_flip', 'mean', 'pca_flip']))
meg.call_main(args)
def init(subject, task):
args = pu.init_args(meg.read_cmd_args(dict(
subject=subject,
atlas='laus125',
task=task,
files_includes_cond=True,
inverse_method='MNE')))
fname_format_cond = '{subject}_hcp_{cond}-{ana_type}.{file_type}'
fname_format = '{subject}_hcp-{ana_type}.{file_type}'
meg.init_globals_args(
subject, '', fname_format, fname_format_cond, args=args)
hcp_params = dict(hcp_path=HCP_DIR, subject=subject, data_type=task)
return args, hcp_params
def analyze(subject):
flags = {}
args = meg.read_cmd_args(
dict(
subject=subject,
task='tapping',
conditions='left',
# atlas='laus250',
inverse_method='MNE',
t_min=-2,
t_max=2,
noise_t_min=-2.5,
noise_t_max=-1.5,
bad_channels=[],
stim_channels='STIM',
pick_ori='normal',
reject=False,
overwrite_epochs=True,
overwrite_inv=True,
overwrite_noise_cov=True,
overwrite_ica=True))
fname_format, fname_format_cond, conditions = meg.init(subject, args)
conditions['left'] = 4
args.conditions = conditions
if op.isfile(meg.RAW):
raw = mne.io.read_raw_fif(meg.RAW, preload=True)
else:
raw = mne.io.read_raw_ctf(op.join(MEG_DIR, subject, 'raw',
'DC_leftIndex_day1.ds'),
preload=True)
meg.remove_artifacts(raw,
remove_from_raw=True,
overwrite_ica=args.overwrite_ica,
do_plot=False)
# print(raw.info['sfreq'])
# if not op.isfile(meg.RAW):
# raw.save(meg.RAW)
# flags, evoked, epochs = meg.calc_evokes_wrapper(subject, conditions, args, flags, raw=raw)
# if evoked is not None:
# fig = evoked[0].plot_joint(times=[-0.5, 0.05, 0.150, 0.250, 0.6])
# plt.show()
# flags = meg.calc_fwd_inv_wrapper(subject, conditions, args, flags)
# flags, stcs_conds, _ = meg.calc_stc_per_condition_wrapper(subject, conditions, args.inverse_method, args, flags)
# flags = meg.calc_labels_avg_per_condition_wrapper(subject, conditions, args.atlas, args.inverse_method, stcs_conds, args, flags)
dipoles_times = [(0.25, 0.35)]
dipoles_names = ['peak_left_motor']
def meg_preproc(args):
atlas, inv_method, em = 'aparc.DKTatlas40', 'dSPM', 'mean_flip'
atlas = 'darpa_atlas'
bands = dict(theta=[4, 8], alpha=[8, 15], beta=[15, 30], gamma=[30, 55], high_gamma=[65, 200])
tasks = ['MSIT', 'ECR']
empty_fnames = get_empty_fnames(args.subject[0], tasks, args)
times = (-2, 4)
for task in tasks:
args = meg.read_cmd_args(dict(
subject=args.subject, mri_subject=args.subject,
task=task, inverse_method=inv_method, extract_mode=em, atlas=atlas,
meg_dir=args.meg_dir,
remote_subject_dir=args.remote_subject_dir, # Needed for finding COR
get_task_defaults=False,
fname_format='{}_{}_maxwell-raw'.format('{subject}', task),
empty_fname=empty_fnames[task],
# function='calc_epochs,calc_evokes,make_forward_solution,calc_inverse_operator,calc_stc_per_condition,calc_labels_avg_per_condition,calc_labels_min_max',
function='calc_epochs',
# function='calc_labels_connectivity',
conditions=task.lower(),
# data_per_task=True,
ica_overwrite_raw=False,
normalize_data=False,
t_min=times[0], t_max=times[1],
read_events_from_file=False, stim_channels='STI001',
use_empty_room_for_noise_cov=True,
calc_source_band_induced_power=True,
calc_inducde_power_per_label=False,
bands='', #dict(theta=[4, 8], alpha=[8, 15], beta=[15, 30], gamma=[30, 55], high_gamma=[65, 200]),
con_method='coh',
con_mode='cwt_morlet',
overwrite_connectivity=False,
read_only_from_annot=False,
# pick_ori='normal',
# overwrite_epochs=True,
# overwrite_evoked=True,
# overwrite_inv=True,
overwrite_stc=True,
overwrite_labels_data=True,
n_jobs=args.n_jobs
))
meg.call_main(args)
#
for subject in args.subject:
for task in tasks:
task = task.lower()
def read_cmd_args(argv=None, subject='', mri_subject='', atlas=''):
if argv is None and subject != '':
mri_subject = subject if mri_subject == '' else mri_subject
argv = ['-s', subject, '-m', mri_subject]
args = meg.read_cmd_args(argv)
if atlas != '' and args.atlas != atlas:
args.atlas = atlas
args.pick_meg = False
args.pick_eeg = True
args.reject = False
args.fwd_usingMEG = False
args.fwd_usingEEG = True
args.modality = 'eeg'
args.meg_dir = utils.get_link_dir(LINKS_DIR, 'eeg')
if not op.isdir(args.meg_dir):
raise Exception('EEG dir can\'t be found! Please rerun src.setup with -f create_links')
return args
def read_epoches_and_calc_activity(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.function = ['calc_stc_per_condition', 'calc_labels_avg_per_condition', 'smooth_stc', 'save_activity_map']
args.pick_ori = 'normal'
args.colors_map = 'jet'
meg.run_on_subjects(args)
def check_files_names(subject, mri_subject):
args = meg.read_cmd_args(['-s', subject, '-m', mri_subject])
args.fname_format = '{subject}_02_f2-35_all_correct_combined'
args.inv_fname_format = '{subject}_02_f2-35-ico-5-meg-eeg'
args.function = 'print_names'
meg.run_on_subjects(args)