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/'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif'
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

# Read raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)

# 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)

# 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')

# read forward solution
forward = mne.read_forward_solution(fname_fwd)
# use forward operator with fixed source orientations
forward = mne.convert_forward_solution(forward, surf_ori=True,
                                       force_fixed=True)

# read noise covariance matrix
noise_cov = mne.read_cov(fname_cov)

# get valid measurement info
raw = raw.pick_types(meg=True, eeg=True, exclude='bads')
info = raw.info

# 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')

Out:

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.
Current compensation grade : 0
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
319 events found
Event IDs: [ 1  2  3  4  5 32]
143 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
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)
    MEG and EEG forward solutions combined
    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

rm_pre = make_lcmv_resolution_matrix(filters_pre, forward, info)

rm_post = make_lcmv_resolution_matrix(filters_post, forward, info)

# compute cross-talk functions (CTFs) for one target vertex
sources = [3000]

stc_pre = get_cross_talk(rm_pre, forward['src'], sources, norm=True)

stc_post = get_cross_talk(rm_post, forward['src'], sources, norm=True)

Out:

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

Visualise

vertno_lh = forward['src'][0]['vertno']  # vertex of selected source
verttrue = [vertno_lh[sources[0]]]  # pick one vertex

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)

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)

# mark true source location for CTFs
brain_pre.add_foci(verttrue, coords_as_verts=True, scale_factor=1., hemi='lh',
                   color='green')

brain_post.add_foci(verttrue, coords_as_verts=True, scale_factor=1.,
                    hemi='lh', color='green')
  • plot psf ctf vertices lcmv
  • plot 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 16.648 seconds)

Estimated memory usage: 1161 MB

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