pl.figure()
pl.title('Source most correlated with the EOG channel')
pl.plot(sources[eog_source_idx].T)
pl.show()
###############################################################################
# Reject artifact sources and compare results
# join the detected artifact indices
exclude = np.r_[ecg_source_idx, eog_source_idx]
# Restore sources, use 64 PCA components which include the ICA cleaned sources
# plus additional PCA components not supplied to ICA (up to rank 64).
# This allows to control the trade-off between denoising and preserving data.
epochs_ica = ica.pick_sources_epochs(epochs,
include=None,
exclude=exclude,
n_pca_components=64,
copy=True)
# plot original epochs
pl.figure()
epochs.average().plot()
pl.show()
# plot cleaned epochs
pl.figure()
epochs_ica.average().plot()
pl.show()
# get sources
sources = ica.get_sources_epochs(epochs, concatenate=True)
pl.figure()
pl.title('Source most correlated with the EOG channel')
pl.plot(sources[eog_source_idx].T)
pl.show()
###############################################################################
# Reject artifact sources and compare results
# join the detected artifact indices
exclude = np.r_[ecg_source_idx, eog_source_idx]
# Restore sources, use 64 PCA components which include the ICA cleaned sources
# plus additional PCA components not supplied to ICA (up to rank 64).
# This allows to control the trade-off between denoising and preserving data.
epochs_ica = ica.pick_sources_epochs(epochs, include=None, exclude=exclude,
n_pca_components=64, copy=True)
# plot original epochs
pl.figure()
epochs.average().plot()
pl.show()
# plot cleaned epochs
pl.figure()
epochs_ica.average().plot()
pl.show()
