def _prepare_forward_fsaverage(cfg):
assert cfg.fs_subject == 'fsaverage'
trans = 'fsaverage' # MNE has a built-in fsaverage transformation
bem_sol = cfg.fs_subjects_dir / 'fsaverage' / \
'bem' / 'fsaverage-5120-5120-5120-bem-sol.fif'
if not bem_sol.exists():
fetch_fsaverage(cfg.fs_subjects_dir)
src = mne.setup_source_space(subject='fsaverage',
subjects_dir=cfg.fs_subjects_dir,
spacing=cfg.spacing,
add_dist=False,
n_jobs=cfg.n_jobs)
return src, trans, str(bem_sol)
def get_FS_data():
"""Obtains the directory of the trans, src, bem and subjects_dir.
:return: trans, src, bem, subjects_dir (all in strings)
"""
# Use the fetch_fsaverage function from MNE to get fs data
fs_dir = fetch_fsaverage(verbose=True)
# Obtain the subject directory name
subjects_dir = op.dirname(fs_dir)
# Set the directory of the trans, src and bem
trans = op.join(fs_dir, 'bem', 'fsaverage-trans.fif')
src = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')
return trans, src, bem, subjects_dir
""" # Authors: Alexandre Gramfort <*****@*****.**> # Joan Massich <*****@*****.**> # # License: BSD Style. import os.path as op import mne from mne.datasets import eegbci from mne.datasets import fetch_fsaverage # Download fsaverage files fs_dir = fetch_fsaverage(verbose=True) subjects_dir = op.dirname(fs_dir) # The files live in: subject = 'fsaverage' trans = op.join(fs_dir, 'bem', 'fsaverage-trans.fif') src = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif') bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif') ############################################################################## # Load the data # ------------- # # We use here EEG data from the BCI dataset. raw_fname, = eegbci.load_data(subject=1, runs=[6])
ylabel='Amplitude (a. u.)')
mne.viz.utils.plt_show()
del stc_vec
###############################################################################
# Morph the output to fsaverage
# -----------------------------
#
# We can also use volumetric morphing to get the data to fsaverage space. This
# is for example necessary when comparing activity across subjects. Here, we
# will use the scalar beamformer example.
# We pass a :class:`mne.SourceMorph` as the ``src`` argument to
# `mne.VolSourceEstimate.plot`. To save some computational load when applying
# the morph, we will crop the ``stc``:
fetch_fsaverage(subjects_dir) # ensure fsaverage src exists
fname_fs_src = subjects_dir + '/fsaverage/bem/fsaverage-vol-5-src.fif'
src_fs = mne.read_source_spaces(fname_fs_src)
morph = mne.compute_source_morph(
src,
subject_from='sample',
src_to=src_fs,
subjects_dir=subjects_dir,
niter_sdr=[10, 10, 5],
niter_affine=[10, 10, 5], # just for speed
verbose=True)
stc_fs = morph.apply(stc)
del stc
stc_fs.plot(src=src_fs,
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import fetch_fsaverage
# paths to mne datasets - sample sEEG and FreeSurfer's fsaverage subject
# which is in MNI space
misc_path = mne.datasets.misc.data_path()
sample_path = mne.datasets.sample.data_path()
subjects_dir = op.join(sample_path, 'subjects')
# use mne-python's fsaverage data
fetch_fsaverage(subjects_dir=subjects_dir, verbose=True) # downloads if needed
# %%
# Let's load some sEEG data with channel locations and make epochs.
raw = mne.io.read_raw(op.join(misc_path, 'seeg', 'sample_seeg_ieeg.fif'))
events, event_id = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw, events, event_id, detrend=1, baseline=None)
epochs = epochs['Response'][0] # just process one epoch of data for speed
# %%
# Let use the Talairach transform computed in the Freesurfer recon-all
# to apply the Freesurfer surface RAS ('mri') to MNI ('mni_tal') transform.
montage = epochs.get_montage()
def construct_subject():
MNE_Repo_Mat.subjects_dir = os.path.dirname(fetch_fsaverage())
MNE_Repo_Mat.subject='fsaverage'
return MNE_Repo_Mat.subject
# Create dummy info
info=mne.create_info(
ch_names=montage.ch_names,
sfreq=1,
ch_types='eeg',
montage=montage,
),
)
set_3d_view(figure=fig, azimuth=135, elevation=80)
set_3d_title(figure=fig, title=current_montage)
###############################################################################
# Check all montages against fsaverage
#
subjects_dir = op.dirname(fetch_fsaverage())
for current_montage in get_builtin_montages():
montage = mne.channels.make_standard_montage(current_montage)
fig = mne.viz.plot_alignment(
# Plot options
show_axes=True,
dig=True,
surfaces='head',
trans=None,
subject='fsaverage',
subjects_dir=subjects_dir,
# Create dummy info
info=mne.create_info(
ch_names=montage.ch_names,
shipped in MNE-python, and display it on fsaverage template.
""" # noqa: D205, D400
# Authors: Alexandre Gramfort <*****@*****.**>
# Joan Massich <*****@*****.**>
#
# License: BSD Style.
from mayavi import mlab
import os.path as op
import mne
from mne.channels.montage import get_builtin_montages
from mne.datasets import fetch_fsaverage
from mne.viz import plot_alignment
subjects_dir = op.dirname(fetch_fsaverage())
###############################################################################
# check all montages
#
for current_montage in get_builtin_montages():
montage = mne.channels.read_montage(current_montage,
unit='auto',
transform=False)
info = mne.create_info(ch_names=montage.ch_names,
sfreq=1,
ch_types='eeg',
montage=montage)
def boxy2mne(*, boxy_file=None, mtg_file=None, coord_file=None):
# =============================================================================
# ready raw data from boxy file
# =============================================================================
###this keeps track of the line we're on###
###mostly to know the start and stop of data (probably an easier way)###
line_num = 0
###load and read data to get some meta information###
###there is alot of information at the beginning of a file###
###but this only grabs some of it###
with open(boxy_file, 'r') as data:
for i_line in data:
line_num += 1
if '#DATA ENDS' in i_line:
end_line = line_num - 1
break
if 'Detector Channels' in i_line:
detect_num = int(i_line.rsplit(' ')[0])
elif 'External MUX Channels' in i_line:
source_num = int(i_line.rsplit(' ')[0])
elif 'Auxiliary Channels' in i_line:
aux_num = int(i_line.rsplit(' ')[0])
elif 'Waveform (CCF) Frequency (Hz)' in i_line:
ccf_ha = float(i_line.rsplit(' ')[0])
elif 'Update Rate (Hz)' in i_line:
srate = float(i_line.rsplit(' ')[0])
elif 'Updata Rate (Hz)' in i_line:
srate = float(i_line.rsplit(' ')[0])
elif '#DATA BEGINS' in i_line:
start_line = line_num
###now let's go through and parse our raw data###
raw_data = pd.read_csv(boxy_file, skiprows=start_line, sep='\t')
###detectors, sources, and data types###
detectors = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',
'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
]
data_types = ['AC', 'DC', 'Ph']
sources = np.arange(1, source_num + 1, 1)
###since we can save boxy files in two different styles###
###this will check to see which style the data is saved###
###seems to also work with older boxy files###
if 'exmux' in raw_data.columns:
filetype = 'non-parsed'
###drop the last line as this is just '#DATA ENDS'###
raw_data = raw_data.drop([len(raw_data) - 1])
###store some extra info###
record = raw_data['record'].to_numpy()
exmux = raw_data['exmux'].to_numpy()
###make some empty variables to store our data###
raw_ac = np.zeros(
(detect_num * source_num, int(len(raw_data) / source_num)))
raw_dc = np.zeros(
(detect_num * source_num, int(len(raw_data) / source_num)))
raw_ph = np.zeros(
(detect_num * source_num, int(len(raw_data) / source_num)))
else:
filetype = 'parsed'
###drop the last line as this is just '#DATA ENDS'###
###also drop the first line since this is empty###
raw_data = raw_data.drop([0, len(raw_data) - 1])
###make some empty variables to store our data###
raw_ac = np.zeros(((detect_num * source_num), len(raw_data)))
raw_dc = np.zeros(((detect_num * source_num), len(raw_data)))
raw_ph = np.zeros(((detect_num * source_num), len(raw_data)))
###store some extra data, might not need these though###
time = raw_data['time'].to_numpy() if 'time' in raw_data.columns else []
time = raw_data['time'].to_numpy() if 'time' in raw_data.columns else []
group = raw_data['group'].to_numpy() if 'group' in raw_data.columns else []
step = raw_data['step'].to_numpy() if 'step' in raw_data.columns else []
mark = raw_data['mark'].to_numpy() if 'mark' in raw_data.columns else []
flag = raw_data['flag'].to_numpy() if 'flag' in raw_data.columns else []
aux1 = raw_data['aux-1'].to_numpy() if 'aux-1' in raw_data.columns else []
digaux = raw_data['digaux'].to_numpy(
) if 'digaux' in raw_data.columns else []
bias = np.zeros((detect_num, len(raw_data)))
###loop through detectors###
for i_detect in detectors[0:detect_num]:
###older boxy files don't seem to keep track of detector bias###
###probably due to specific boxy settings actually###
if 'bias-A' in raw_data.columns:
bias[detectors.index(i_detect), :] = raw_data['bias-' +
i_detect].to_numpy()
###loop through data types###
for i_data in data_types:
###loop through sources###
for i_source in sources:
###where to store our data###
index_loc = detectors.index(i_detect) * source_num + (
i_source - 1)
###need to treat our filetypes differently###
if filetype == 'non-parsed':
###filetype saves timepoints in groups###
###this should account for that###
time_points = np.arange(i_source - 1,
int(record[-1]) * source_num,
source_num)
###determine which channel to look for###
channel = i_detect + '-' + i_data
###save our data based on data type###
if data_types.index(i_data) == 0:
raw_ac[index_loc, :] = raw_data[channel][
time_points].to_numpy()
elif data_types.index(i_data) == 1:
raw_dc[index_loc, :] = raw_data[channel][
time_points].to_numpy()
elif data_types.index(i_data) == 2:
raw_ph[index_loc, :] = raw_data[channel][
time_points].to_numpy()
elif filetype == 'parsed':
###determine which channel to look for###
channel = i_detect + '-' + i_data + str(i_source)
###save our data based on data type###
if data_types.index(i_data) == 0:
raw_ac[index_loc, :] = raw_data[channel].to_numpy()
elif data_types.index(i_data) == 1:
raw_dc[index_loc, :] = raw_data[channel].to_numpy()
elif data_types.index(i_data) == 2:
raw_ph[index_loc, :] = raw_data[channel].to_numpy()
# =============================================================================
# read source and electrode locations from .mtg and .tol/.elp files
# =============================================================================
###set up some variables###
chan_num = []
source_label = []
detect_label = []
chan_wavelength = []
chan_modulation = []
###load and read each line of the .mtg file###
with open(mtg_file, 'r') as data:
for i_ignore in range(2):
next(data)
for i_line in data:
chan1, chan2, source, detector, wavelength, modulation = i_line.split(
)
chan_num.append(chan1)
source_label.append(source)
detect_label.append(detector)
chan_wavelength.append(wavelength)
chan_modulation.append(modulation)
###check if we are given a .tol or .elp file###
all_labels = []
all_coords = []
fiducial_coords = []
if coord_file[-3:].lower() == 'elp'.lower():
get_label = 0
get_coords = 0
###load and read .elp file###
with open(coord_file, 'r') as data:
for i_line in data:
###first let's get our fiducial coordinates###
if '%F' in i_line:
fiducial_coords.append(i_line.split()[1:])
###check where sensor info starts###
if '//Sensor name' in i_line:
get_label = 1
elif get_label == 1:
###grab the part after '%N' for the label###
label = i_line.split()[1]
all_labels.append(label)
get_label = 0
get_coords = 1
elif get_coords == 1:
X, Y, Z = i_line.split()
all_coords.append([float(X), float(Y), float(Z)])
get_coords = 0
for i_index in range(3):
fiducial_coords[i_index] = np.asarray(
[float(x) for x in fiducial_coords[i_index]])
elif coord_file[-3:] == 'tol':
###load and read .tol file###
with open(coord_file, 'r') as data:
for i_line in data:
label, X, Y, Z = i_line.split()
all_labels.append(label)
###convert coordinates from mm to m##
all_coords.append([(float(X) * 0.001), (float(Y) * 0.001),
(float(Z) * 0.001)])
###get coordinates for sources###
source_coords = []
for i_chan in source_label:
if i_chan in all_labels:
chan_index = all_labels.index(i_chan)
source_coords.append(all_coords[chan_index])
###get coordinates for detectors###
detect_coords = []
for i_chan in detect_label:
if i_chan in all_labels:
chan_index = all_labels.index(i_chan)
detect_coords.append(all_coords[chan_index])
###need to rename labels to make other functions happy###
###get our unique labels for sources and detectors###
unique_source_labels = []
unique_detect_labels = []
[
unique_source_labels.append(label) for label in source_label
if label not in unique_source_labels
]
[
unique_detect_labels.append(label) for label in detect_label
if label not in unique_detect_labels
]
###now let's label each channel in our data###
###data is channels X timepoint where the first source_num rows correspond to###
###the first detector, and each row within that group is a different source###
###should note that current .mtg files contain channels for multiple data files###
###going to move to have a single .mtg file per participant, condition, and montage###
###combine coordinates and label our channels###
###will label them based on ac, dc, and ph data###
boxy_coords = []
boxy_labels = []
data_types = ['AC', 'DC', 'Ph']
total_chans = detect_num * source_num
for i_type in data_types:
for i_coord in range(len(source_coords[0:total_chans])):
boxy_coords.append(
np.mean(np.vstack((source_coords[i_coord],
detect_coords[i_coord])),
axis=0).tolist() + source_coords[i_coord] +
detect_coords[i_coord] + [chan_wavelength[i_coord]] + [0] +
[0])
boxy_labels.append(
'S' +
str(unique_source_labels.index(source_label[i_coord]) + 1) +
'_D' +
str(unique_detect_labels.index(detect_label[i_coord]) + 1) +
' ' + chan_wavelength[i_coord] + ' ' + i_type)
###montage only wants channel coords, so need to grab those, convert to###
###array, then make a dict with labels###
for i_chan in range(len(boxy_coords)):
boxy_coords[i_chan] = np.asarray(boxy_coords[i_chan], dtype=np.float64)
for i_chan in range(len(all_coords)):
all_coords[i_chan] = np.asarray(all_coords[i_chan], dtype=np.float64)
all_chan_dict = dict(zip(all_labels, all_coords))
###make our montage###
montage_orig = mne.channels.make_dig_montage(ch_pos=all_chan_dict,
coord_frame='head',
nasion=fiducial_coords[0],
lpa=fiducial_coords[1],
rpa=fiducial_coords[2])
###for some reason make_dig_montage put our channels in a different order than what we input###
###let's fix that. should be fine to just change coords and ch_names###
for i_chan in range(len(all_coords)):
montage_orig.dig[i_chan + 3]['r'] = all_coords[i_chan]
montage_orig.ch_names[i_chan] = all_labels[i_chan]
###add an extra channel for our triggers###
boxy_labels.append('Markers')
###create info structure###
info = mne.create_info(boxy_labels, srate, ch_types='fnirs_raw')
info.update(dig=montage_orig.dig)
###get our fiducials and transform matrix from fsaverage###
subjects_dir = op.dirname(fetch_fsaverage())
fid_path = op.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-fiducials.fif')
fiducials = read_fiducials(fid_path)
trans = coregister_fiducials(info, fiducials[0], tol=0.02)
###remake montage using the transformed coordinates###
all_coords_trans = apply_trans(trans, all_coords)
all_chan_dict_trans = dict(zip(all_labels, all_coords_trans))
fiducial_coords_trans = apply_trans(trans, fiducial_coords)
###make our montage###
montage_trans = mne.channels.make_dig_montage(
ch_pos=all_chan_dict_trans,
coord_frame='head',
nasion=fiducial_coords_trans[0],
lpa=fiducial_coords_trans[1],
rpa=fiducial_coords_trans[2])
###let's fix montage order again###
for i_chan in range(len(all_coords_trans)):
montage_trans.dig[i_chan + 3]['r'] = all_coords_trans[i_chan]
montage_trans.ch_names[i_chan] = all_labels[i_chan]
###add data type and channel wavelength to info###
info.update(dig=montage_trans.dig, trans=trans)
###place our coordinates and wavelengths for each channel###
for i_chan in range(len(boxy_labels) - 1):
temp_chn = apply_trans(trans, boxy_coords[i_chan][0:3])
temp_src = apply_trans(trans, boxy_coords[i_chan][3:6])
temp_det = apply_trans(trans, boxy_coords[i_chan][6:9])
temp_other = np.asarray(boxy_coords[i_chan][9:], dtype=np.float64)
info['chs'][i_chan]['loc'] = test = np.concatenate(
(temp_chn, temp_src, temp_det, temp_other), axis=0)
info['chs'][-1]['loc'] = np.zeros((12, ))
###now combine our data types into a single array###
all_data = np.append(raw_ac, np.append(raw_dc, raw_ph, axis=0), axis=0)
###add our markers to the data array based on filetype###
if filetype == 'non-parsed':
if type(digaux) is list and digaux != []:
markers = digaux[np.arange(0, len(digaux), source_num)]
else:
markers = np.zeros(np.size(all_data, axis=1))
elif filetype == 'parsed':
markers = digaux
all_data = np.vstack((all_data, markers))
###create our raw data object###
raw_data_obj = mne.io.RawArray(all_data, info)
return raw_data_obj
def extract_ts(dir_prepro_dat, dir_save, lower, upper, atlas):
"""
Parameters
----------
dir_prepro_dat : string
Path to saved preprocessed data.
dir_save : string
Path to where the extracted time series should be saved.
lower : list of floats
Lower limit of the desired frequency ranges. Needs to have same length
as upper.
upper : list of floats
Upper limit of the desired frequency ranges. Needs to have same length
as lower.
Notes
----------
Saves the extracted time series and the run time as a dictionary, in the
chosen path (dir_save).
"""
##################################################################
# Initialize parameters
##################################################################
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.dirname(fs_dir)
#fs_dir = '/home/kmsa/mne_data/MNE-fsaverage-data/fsaverage'
#subjects_dir = '/home/kmsa/mne_data/MNE-fsaverage-data'
# The files live in:
subject = 'fsaverage'
trans = 'fsaverage' # MNE has a built-in fsaverage transformation
src = mne.setup_source_space(subject,
spacing='oct6',
subjects_dir=None,
add_dist=False)
# Boundary element method
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')
# Inverse parameters
method = "eLORETA" #other options are minimum norm, dSPM, and sLORETA
snr = 3.
lambda2 = 1. / snr**2
buff_sz = 250
# Create folder if it does not exist
if not op.exists(dir_save):
mkdir(dir_save)
print('\nCreated new path : ' + dir_save)
# Check what atlas to use and read labels
if atlas == 'DK':
# Desikan-Killiany Atlas = aparc
parc = 'Yeo2011_7Networks_N1000' # 'aparc'
labels = read_labels_from_annot(subject,
parc='aparc',
hemi='both',
surf_name='white',
annot_fname=None,
regexp=None,
subjects_dir=subjects_dir,
verbose=None)
labels = labels[:-1]
# elif atlas == 'BA':
# # Broadmann areas
# labels = read_labels_from_annot(subject, parc = 'PALS_B12_Brodmann', hemi='both',
# surf_name= 'white', annot_fname = None, regexp = None,
# subjects_dir = subjects_dir, verbose = None)
# labels = labels[5:87]
elif atlas == 'BAita':
# Brodmann areas collected as in Di Lorenzo et al.
labels = read_labels_from_annot(subject,
parc='PALS_B12_Brodmann',
hemi='both',
surf_name='white',
annot_fname=None,
regexp=None,
subjects_dir=subjects_dir,
verbose=None)
labels = labels[5:87]
lab_dict = {}
for lab in labels:
lab_dict[lab.name] = lab
ita_ba = [
[1, 2, 3, 4],
[5, 7],
[6, 8],
[9, 10],
[11, 47],
[44, 45, 46], #[13],
[20, 21, 22, 38, 41, 42],
[24, 25, 32], #[24,25,32,33],
[23, 29, 30, 31],
[27, 28, 35, 36], #[27,28,34,35,36],
[39, 40, 43],
[19, 37],
[17, 18]
]
# ita_label = ['SMA', 'SPL', 'SFC', 'AFC', 'OFC', 'LFC', #'INS',
# 'LTL', 'ACC_new', 'PCC', 'PHG_new', 'IPL', 'FLC', 'PVC']
# Sort labels according to connectivity featurers
new_label = []
for idx, i in enumerate(ita_ba):
for j in i:
ba_lh = 'Brodmann.' + str(j) + '-lh'
ba_rh = 'Brodmann.' + str(j) + '-rh'
if j == i[0]:
sum_lh = lab_dict[ba_lh]
sum_rh = lab_dict[ba_rh]
else:
sum_lh += lab_dict[ba_lh]
sum_rh += lab_dict[ba_rh]
new_label.append(sum_lh)
new_label.append(sum_rh)
labels = new_label
elif atlas == 'DKLobes':
# Brodmann areas collected as in Di Lorenzo et al.
labels = read_labels_from_annot(subject,
parc='aparc',
hemi='both',
surf_name='white',
annot_fname=None,
regexp=None,
subjects_dir=subjects_dir,
verbose=None)
# Divide into lobes based on
# https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation
frontal = [
'superiorfrontal', 'rostralmiddlefrontal', 'caudalmiddlefrontal',
'parsopercularis', 'parstriangularis', 'parsorbitalis',
'lateralorbitofrontal', 'medialorbitofrontal', 'precentral',
'paracentral', 'frontalpole', 'rostralanteriorcingulate',
'caudalanteriorcingulate'
]
parietal = [
'superiorparietal', 'inferiorparietal', 'supramarginal',
'postcentral', 'precuneus', 'posteriorcingulate',
'isthmuscingulate'
]
temporal = [
'superiortemporal', 'middletemporal', 'inferiortemporal',
'bankssts', 'fusiform', 'transversetemporal', 'entorhinal',
'temporalpole', 'parahippocampal'
]
occipital = ['lateraloccipital', 'lingual', 'cuneus', 'pericalcarine']
all_lobes = {
'frontal': frontal,
'parietal': parietal,
'occipital': occipital,
'temporal': temporal
}
labels = labels[:-1]
lab_dict = {}
for lab in labels:
lab_dict[lab.name] = lab
# Sort labels according to connectivity featurers
new_label = []
for lobes in list(all_lobes.keys()):
for idx, name in enumerate(all_lobes[lobes]):
name_lh = name + '-lh'
name_rh = name + '-rh'
if idx == 0:
sum_lh = lab_dict[name_lh]
sum_rh = lab_dict[name_rh]
else:
sum_lh += lab_dict[name_lh]
sum_rh += lab_dict[name_rh]
sum_lh.name = lobes + '-lh'
sum_rh.name = lobes + '-rh'
new_label.append(sum_lh)
new_label.append(sum_rh)
labels = new_label
# elif finished
# Create folder if it does not exist
if not op.exists(dir_save):
mkdir(dir_save)
print('\nCreated new path : ' + dir_save)
# List of time series that have already been saved
already_saved = [i.split('_' + atlas)[0] for i in listdir(dir_save)]
count = 0
run_time = 0
for filename in tqdm(listdir(dir_prepro_dat)):
# Only loop over ".set" files
if not filename.endswith(".set"):
continue
# Only choose the files that are not already in the save directory
if filename.split('.')[0] in already_saved:
count += 1
continue
start = timeit.default_timer()
timeseries_dict = {}
###################################
# Load preprocessed data
###################################
ID_list = op.join(dir_prepro_dat, filename)
raw = mne.io.read_raw_eeglab(ID_list, preload=True)
# Set montage (number of used channels = 64)
raw.set_montage('biosemi64')
##################################################################
# Forward soultion: from brain to electrode
##################################################################
fwd = mne.make_forward_solution(raw.info,
trans=trans,
src=src,
bem=bem,
eeg=True,
mindist=5.0,
n_jobs=-1)
##################################################################
# Inverse modeling
##################################################################
# Compute noise covariance
noise_cov = mne.compute_raw_covariance(raw, n_jobs=-1)
# make an EEG inverse operator
inverse_operator = make_inverse_operator(raw.info,
fwd,
noise_cov,
loose=0.2,
depth=0.8)
raw.set_eeg_reference('average', projection=True)
# Hardcoded print to give an overview of how much is done and left
print('\n####################################################' +
'\n####################################################' +
'\n####################################################' +
'\nSubject number: ' + str(count) + ', ' +
filename.split('.')[0] + '\nRun time/subject = ' +
str(run_time) + '\nRun time left in hours ~' +
str((85 - (count + 1)) * (run_time / 60)) +
'\n####################################################' +
'\n####################################################' +
'\n####################################################')
# Compute inverse solution
stc = apply_inverse_raw(raw,
inverse_operator,
lambda2,
method=method,
nave=1,
pick_ori=None,
verbose=True,
buffer_size=buff_sz)
#pdb.set_trace()
del raw
# Hardcoded print to give an overview of how much is done and left
print('\n####################################################' +
'\n####################################################' +
'\n####################################################' +
'\nSubject number: ' + str(count) + ', ' +
filename.split('.')[0] + '\nRun time/subject = ' +
str(run_time) + '\nRun time left in hours ~' +
str((85 - (count + 1)) * (run_time / 60)) +
'\n####################################################' +
'\n####################################################' +
'\n####################################################')
##################################################################
# Extract timeseries from DK regions
##################################################################
# Label time series by Desikan-Killiany Atlas -> 68 ts
label_ts = mne.extract_label_time_course(stc,
labels,
inverse_operator['src'],
mode='pca_flip',
return_generator=True)
del stc
###################################################################
# Construct and save dictionary
###################################################################
subject = filename.split('_')[0]
stop = timeit.default_timer()
run_time = (stop - start) / 60
timeseries_dict = {'timeseries': label_ts, 'time': run_time}
del label_ts
# Save to computer
save_name = dir_save + '/' + subject + '_' + atlas + '_timeseries' + '.pkl'
#save_name = '/share/FannyMaster/PythonNew/DK_timeseries/DK_source_timeseries_'+ date +'.pkl'
with open(save_name, 'wb') as file:
pickle.dump(timeseries_dict, file)
del timeseries_dict
count += 1
""" # Authors: Alexandre Gramfort <*****@*****.**> # Joan Massich <*****@*****.**> # # License: BSD Style. import os.path as op import mne from mne.datasets import eegbci from mne.datasets import fetch_fsaverage # Download fsaverage files fs_dir = fetch_fsaverage(verbose=True) subjects_dir = op.dirname(fs_dir) # The files live in: subject = 'fsaverage' trans = op.join(fs_dir, 'bem', 'fsaverage-trans.fif') src = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif') bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif') ############################################################################## # Load the data # ------------- # # We use here EEG data from the BCI dataset. raw_fname, = eegbci.load_data(subject=1, runs=[6])