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# -*- coding: utf-8 -*-
"""
.. _ex-psf-ctf-lcmv:
=================================================
Compute cross-talk functions for LCMV beamformers
=================================================
Visualise cross-talk functions at one vertex for LCMV beamformers computed
with different data covariance matrices, which affects their cross-talk
functions.
"""
# Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#
# License: BSD-3-Clause
# %%
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, make_lcmv_resolution_matrix
from mne.minimum_norm import get_cross_talk
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path / 'subjects'
meg_path = data_path / 'MEG' / 'sample'
fname_fwd = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_cov = meg_path / 'sample_audvis-cov.fif'
fname_evo = meg_path / 'sample_audvis-ave.fif'
raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'
# Read raw data
raw = mne.io.read_raw_fif(raw_fname)
# only pick good EEG/MEG sensors
raw.info['bads'] += ['EEG 053'] # bads + 1 more
picks = mne.pick_types(raw.info, meg=True, eeg=True, exclude='bads')
# Find events
events = mne.find_events(raw)
# event_id = {'aud/l': 1, 'aud/r': 2, 'vis/l': 3, 'vis/r': 4}
event_id = {'vis/l': 3, 'vis/r': 4}
tmin, tmax = -.2, .25 # epoch duration
epochs = mne.Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,
picks=picks, baseline=(-.2, 0.), preload=True)
del raw
# covariance matrix for pre-stimulus interval
tmin, tmax = -.2, 0.
cov_pre = mne.compute_covariance(epochs, tmin=tmin, tmax=tmax,
method='empirical')
# covariance matrix for post-stimulus interval (around main evoked responses)
tmin, tmax = 0.05, .25
cov_post = mne.compute_covariance(epochs, tmin=tmin, tmax=tmax,
method='empirical')
info = epochs.info
del epochs
# read forward solution
forward = mne.read_forward_solution(fname_fwd)
# use forward operator with fixed source orientations
mne.convert_forward_solution(forward, surf_ori=True,
force_fixed=True, copy=False)
# read noise covariance matrix
noise_cov = mne.read_cov(fname_cov)
# regularize noise covariance (we used 'empirical' above)
noise_cov = mne.cov.regularize(noise_cov, info, mag=0.1, grad=0.1,
eeg=0.1, rank='info')
##############################################################################
# Compute LCMV filters with different data covariance matrices
# ------------------------------------------------------------
# compute LCMV beamformer filters for pre-stimulus interval
filters_pre = make_lcmv(info, forward, cov_pre, reg=0.05,
noise_cov=noise_cov,
pick_ori=None, rank=None,
weight_norm=None,
reduce_rank=False,
verbose=False)
# compute LCMV beamformer filters for post-stimulus interval
filters_post = make_lcmv(info, forward, cov_post, reg=0.05,
noise_cov=noise_cov,
pick_ori=None, rank=None,
weight_norm=None,
reduce_rank=False,
verbose=False)
##############################################################################
# Compute resolution matrices for the two LCMV beamformers
# --------------------------------------------------------
# compute cross-talk functions (CTFs) for one target vertex
sources = [3000]
verttrue = [forward['src'][0]['vertno'][sources[0]]] # pick one vertex
rm_pre = make_lcmv_resolution_matrix(filters_pre, forward, info)
stc_pre = get_cross_talk(rm_pre, forward['src'], sources, norm=True)
del rm_pre
##############################################################################
rm_post = make_lcmv_resolution_matrix(filters_post, forward, info)
stc_post = get_cross_talk(rm_post, forward['src'], sources, norm=True)
del rm_post
##############################################################################
# Visualize
# ---------
# Pre:
brain_pre = stc_pre.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir,
figure=1, clim=dict(kind='value', lims=(0, .2, .4)))
brain_pre.add_text(0.1, 0.9, 'LCMV beamformer with pre-stimulus\ndata '
'covariance matrix', 'title', font_size=16)
# mark true source location for CTFs
brain_pre.add_foci(verttrue, coords_as_verts=True, scale_factor=1., hemi='lh',
color='green')
# %%
# Post:
brain_post = stc_post.plot('sample', 'inflated', 'lh',
subjects_dir=subjects_dir,
figure=2, clim=dict(kind='value', lims=(0, .2, .4)))
brain_post.add_text(0.1, 0.9, 'LCMV beamformer with post-stimulus\ndata '
'covariance matrix', 'title', font_size=16)
brain_post.add_foci(verttrue, coords_as_verts=True, scale_factor=1.,
hemi='lh', color='green')
# %%
# The pre-stimulus beamformer's CTF has lower values in parietal regions
# suppressed alpha activity?) but larger values in occipital regions (less
# suppression of visual activity?).
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