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')
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
319 events found
Event IDs: [ 1  2  3  4  5 32]
Not setting metadata
143 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 4)
4 projection items activated
Loading data for 143 events and 69 original time points ...
0 bad epochs dropped
Computing rank from data with rank=None
    Using tolerance 4.9e-09 (2.2e-16 eps * 305 dim * 7.3e+04  max singular value)
    Estimated rank (mag + grad): 302
    MEG: rank 302 computed from 305 data channels with 3 projectors
    Using tolerance 2.8e-11 (2.2e-16 eps * 59 dim * 2.1e+03  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
    Created an SSP operator (subspace dimension = 4)
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
Reducing data rank from 364 -> 360
Estimating covariance using EMPIRICAL
Done.
Number of samples used : 4433
[done]
Computing rank from data with rank=None
    Using tolerance 5.9e-09 (2.2e-16 eps * 305 dim * 8.7e+04  max singular value)
    Estimated rank (mag + grad): 302
    MEG: rank 302 computed from 305 data channels with 3 projectors
    Using tolerance 3.8e-11 (2.2e-16 eps * 59 dim * 2.9e+03  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
    Created an SSP operator (subspace dimension = 4)
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
Reducing data rank from 364 -> 360
Estimating covariance using EMPIRICAL
Done.
Number of samples used : 4433
[done]
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    Forward solutions combined: MEG, EEG
    Source spaces transformed to the forward solution coordinate frame
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
    366 x 366 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
Computing rank from covariance with rank='info'
    MEG: rank 302 after 3 projectors applied to 305 channels
    EEG: rank 58 after 1 projector applied to 59 channels
8 projection items activated
    MAG regularization : 0.1
    Created an SSP operator (subspace dimension = 3)
Computing rank from covariance with rank={'meg': 302, 'eeg': 58}
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
    Setting small MAG eigenvalues to zero (without PCA)
    GRAD regularization : 0.1
Computing rank from covariance with rank={'meg': 302, 'eeg': 58, 'mag': 99}
    Using tolerance 1.8e-13 (2.2e-16 eps * 203 dim * 3.9  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
    Setting small GRAD eigenvalues to zero (without PCA)
    EEG regularization : 0.1
    Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank={'meg': 302, 'eeg': 58, 'mag': 99, 'grad': 203}
    Setting small EEG eigenvalues to zero (without PCA)

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
    364 out of 366 channels remain after picking
Dimensions of LCMV resolution matrix: 7498 by 7498.
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
    364 out of 366 channels remain after picking
Dimensions of LCMV resolution matrix: 7498 by 7498.

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')
psf ctf vertices lcmv

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')
psf ctf vertices lcmv

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?).

Total running time of the script: ( 0 minutes 13.035 seconds)

Estimated memory usage: 438 MB

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