def get_epochs_and_cov(X, y, window=500):
"""return epochs from array."""
raw_train = toMNE(X, y)
picks = range(len(getChannelNames()))
events = list()
events_id = dict()
for j, eid in enumerate(getEventNames()):
tmp = find_events(raw_train, stim_channel=eid, verbose=False)
tmp[:, -1] = j + 1
events.append(tmp)
events_id[eid] = j + 1
# concatenate and sort events
events = np.concatenate(events, axis=0)
order_ev = np.argsort(events[:, 0])
events = events[order_ev]
epochs = Epochs(raw_train,
events,
events_id,
tmin=-(window / 500.0) + 1 / 500.0 + 0.150,
tmax=0.150,
proj=False,
picks=picks,
baseline=None,
preload=True,
add_eeg_ref=False,
verbose=False)
cov_signal = compute_raw_data_covariance(raw_train, verbose=False)
return epochs, cov_signal
def get_epochs_and_cov(X, y, window=500):
"""return epochs from array."""
raw_train = toMNE(X, y)
picks = range(len(getChannelNames()))
events = list()
events_id = dict()
for j, eid in enumerate(getEventNames()):
tmp = find_events(raw_train, stim_channel=eid, verbose=False)
tmp[:, -1] = j + 1
events.append(tmp)
events_id[eid] = j + 1
# concatenate and sort events
events = np.concatenate(events, axis=0)
order_ev = np.argsort(events[:, 0])
events = events[order_ev]
epochs = Epochs(raw_train, events, events_id,
tmin=-(window / 500.0) + 1 / 500.0 + 0.150,
tmax=0.150, proj=False, picks=picks, baseline=None,
preload=True, add_eeg_ref=False, verbose=False)
cov_signal = compute_raw_data_covariance(raw_train, verbose=False)
return epochs, cov_signal
def toMNE(X, y=None):
"""Tranform array into MNE for epoching."""
ch_names = deepcopy(getChannelNames())
montage = read_montage('standard_1005', ch_names)
ch_type = ['eeg']*len(ch_names)
data = X.T
if y is not None:
y = y.transpose()
ch_type.extend(['stim']*N_EVENTS)
event_names = getEventNames()
ch_names.extend(event_names)
# concatenate event file and data
data = np.concatenate((data, y))
info = create_info(ch_names, sfreq=128.0, ch_types=ch_type,
montage=montage)
raw = RawArray(data, info, verbose=False)
return raw
def toMNE(X, y=None):
"""Tranform array into MNE for epoching."""
ch_names = getChannelNames()
montage = read_montage('standard_1005', ch_names)
ch_type = ['eeg']*len(ch_names)
data = X.T
if y is not None:
y = y.transpose()
ch_type.extend(['stim']*6)
event_names = getEventNames()
ch_names.extend(event_names)
# concatenate event file and data
data = np.concatenate((data, y))
info = create_info(ch_names, sfreq=500.0, ch_types=ch_type,
montage=montage)
raw = RawArray(data, info, verbose=False)
return raw
