# For the right hemi
rh_labels = [label[:-2] + 'rh' for label in lh_labels]
# %%
# Compute point-spread function summaries (PCA) for all labels
# ------------------------------------------------------------
#
# We summarise the PSFs per label by their first five principal components, and
# use the first component to evaluate label-to-label leakage below.
# Compute first PCA component across PSFs within labels.
# Note the differences in explained variance, probably due to different
# spatial extents of labels.
n_comp = 5
stcs_psf_mne, pca_vars_mne = get_point_spread(
rm_mne, src, labels, mode='pca', n_comp=n_comp, norm=None,
return_pca_vars=True)
n_verts = rm_mne.shape[0]
del rm_mne
# %%
# We can show the explained variances of principal components per label. Note
# how they differ across labels, most likely due to their varying spatial
# extent.
with np.printoptions(precision=1):
for [name, var] in zip(label_names, pca_vars_mne):
print(f'{name}: {var.sum():.1f}% {var}')
# %%
# The output shows the summed variance explained by the first five principal
# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr**2
method = 'MNE' # can be 'MNE' or 'sLORETA'
# compute resolution matrix for sLORETA
rm_lor = make_inverse_resolution_matrix(forward,
inverse_operator,
method='sLORETA',
lambda2=lambda2)
# get PSF and CTF for sLORETA at one vertex
sources = [1000]
stc_psf = get_point_spread(rm_lor, forward['src'], sources, norm=True)
stc_ctf = get_cross_talk(rm_lor, forward['src'], sources, norm=True)
del rm_lor
##############################################################################
# Visualize
# ---------
# PSF:
# Which vertex corresponds to selected source
vertno_lh = forward['src'][0]['vertno']
verttrue = [vertno_lh[sources[0]]] # just one vertex
# find vertices with maxima in PSF and CTF
vert_max_psf = vertno_lh[stc_psf.data.argmax()]
