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
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)
# 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)
