def _fit(self, X, y):
"""fit and return epochs."""
epochs, cov_signal = get_epochs_and_cov(X, y, self.window)
xd = Xdawn(n_components=self.nfilters, signal_cov=cov_signal,
correct_overlap=False)
xd.fit(epochs)
P = []
for eid in getEventNames():
P.append(np.dot(xd.filters_[eid][:, 0:self.nfilters].T,
xd.evokeds_[eid].data))
self.P = np.concatenate(P, axis=0)
self.labels_train = epochs.events[:, -1]
return epochs
def spatial_filter(raw, epochs):
print('spatial filtering using xDawn')
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
signal_cov = compute_raw_covariance(raw, picks=picks)
xd = Xdawn(n_components=2, signal_cov=signal_cov)
print('fiting xDawn')
xd.fit(epochs)
print('epoch denoising started')
epochs_denoised = xd.apply(epochs)
print('epoch_denoise complete')
return epochs_denoised
def _fit(self, X, y):
"""fit and return epochs."""
epochs, cov_signal = get_epochs_and_cov(X, y, self.window)
xd = Xdawn(n_components=self.nfilters,
signal_cov=cov_signal,
correct_overlap=False)
xd.fit(epochs)
P = []
for eid in getEventNames():
P.append(
np.dot(xd.filters_[eid][:, 0:self.nfilters].T,
xd.evokeds_[eid].data))
self.P = np.concatenate(P, axis=0)
self.labels_train = epochs.events[:, -1]
return epochs
def _fit(self, X, y):
"""fit and return epochs."""
epochs, cov_signal = get_epochs_and_cov(X, y, self.window)
# pdb.set_trace()
# TODO: Try to understand what is Xdawn (something that improve SSRN by spatial information?)
xd = Xdawn(n_components=self.nfilters, signal_cov=cov_signal,
correct_overlap=False)
xd.fit(epochs)
P = []
for eid in getEventNames():
# set_breakpoint('xd info in getEVetnNames for-lop');
# pdb.set_trace()
P.append(np.dot(xd.filters_[eid][:, 0:self.nfilters].T,
xd.evokeds_[eid].data))
self.P = np.concatenate(P, axis=0)
self.labels_train = epochs.events[:, -1]
# pdb.set_trace()
return epochs
exclude='bads')
# Epoching
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=False,
picks=picks,
baseline=None,
preload=True,
verbose=False)
# Plot image epoch before xdawn
plot_epochs_image(epochs['vis_r'], picks=[230], vmin=-500, vmax=500)
# Estimates signal covariance
signal_cov = compute_raw_covariance(raw, picks=picks)
# Xdawn instance
xd = Xdawn(n_components=2, signal_cov=signal_cov)
# Fit xdawn
xd.fit(epochs)
# Denoise epochs
epochs_denoised = xd.apply(epochs)
# Plot image epoch after Xdawn
plot_epochs_image(epochs_denoised['vis_r'], picks=[230], vmin=-500, vmax=500)
picks_train = pick_types(raw_train.info, eeg=True)
picks_val = pick_types(raw_val.info, eeg=True)
#picks_test = pick_types(raw_test.info, eeg=True)
data_train = raw_train._data[picks_train].T
labels_train = raw_train._data[32:].T
data_val = raw_val._data[picks_val].T
labels_val = raw_val._data[32:].T
#data_test = raw_test._data[picks_test].T
#labels_test = None
train_epochs, train_cov_signal = get_epochs_and_cov(raw_train, picks_train, WINDOW)
xd = Xdawn(n_components=NFILTERS, signal_cov=train_cov_signal, correct_overlap=False)
xd.fit(train_epochs)
val_epochs, val_cov_signal = get_epochs_and_cov(raw_val, picks_val, WINDOW)
xd = Xdawn(n_components=NFILTERS, signal_cov=val_cov_signal, correct_overlap=False)
xd.fit(val_epochs)
P = []
for eid in getEventNames():
P.append(np.dot(xd.filters_[eid][:, 0:NFILTERS].T, xd.evokeds_[eid].data))
print ("Saving data for subject{0} in files".format(subject))
np.save('data/processed/subj{0}_train_data.npy'.format(subject), train_epochs._data)
np.save('data/processed/subj{0}_train_labels.npy'.format(subject), train_epochs.events)
np.save('data/processed/subj{0}_val_data.npy'.format(subject), val_epochs._data)
np.save('data/processed/subj{0}_val_labels.npy'.format(subject), val_epochs.events)
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 20) # replace baselining with high-pass
events = read_events(event_fname)
raw.info['bads'] = ['MEG 2443'] # set bad channels
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
exclude='bads')
# Epoching
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
picks=picks, baseline=None, preload=True,
verbose=False)
# Plot image epoch before xdawn
plot_epochs_image(epochs['vis_r'], picks=[230], vmin=-500, vmax=500)
# Estimates signal covariance
signal_cov = compute_raw_covariance(raw, picks=picks)
# Xdawn instance
xd = Xdawn(n_components=2, signal_cov=signal_cov)
# Fit xdawn
xd.fit(epochs)
# Denoise epochs
epochs_denoised = xd.apply(epochs)
# Plot image epoch after Xdawn
plot_epochs_image(epochs_denoised['vis_r'], picks=[230], vmin=-500, vmax=500)