Compute LCMV beamformer on evoked data

Compute LCMV beamformer solutions on an evoked dataset for three different choices of source orientation and store the solutions in stc files for visualisation.

# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt
import numpy as np

import mne
from mne.datasets import sample
from mne.beamformer import lcmv

print(__doc__)

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
subjects_dir = data_path + '/subjects'

Out:

Successfully extracted to: [u'/home/ubuntu/mne_data/MNE-sample-data']

Get epochs

event_id, tmin, tmax = 1, -0.2, 0.5

# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels
events = mne.read_events(event_fname)

# Set up pick list: EEG + MEG - bad channels (modify to your needs)
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
                       exclude='bads')

# Pick the channels of interest
raw.pick_channels([raw.ch_names[pick] for pick in picks])
# Re-normalize our empty-room projectors, so they are fine after subselection
raw.info.normalize_proj()

# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                    baseline=(None, 0), preload=True, proj=True,
                    reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
evoked = epochs.average()

forward = mne.read_forward_solution(fname_fwd)
forward = mne.convert_forward_solution(forward, surf_ori=True)

# Compute regularized noise and data covariances
noise_cov = mne.compute_covariance(epochs, tmin=tmin, tmax=0, method='shrunk')
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15,
                                  method='shrunk')

plt.close('all')

pick_oris = [None, 'normal', 'max-power']
names = ['free', 'normal', 'max-power']
descriptions = ['Free orientation, voxel: %i', 'Normal orientation, voxel: %i',
                'Max-power orientation, voxel: %i']
colors = ['b', 'k', 'r']

for pick_ori, name, desc, color in zip(pick_oris, names, descriptions, colors):
    # compute unit-noise-gain beamformer with whitening of the leadfield and
    # data (enabled by passing a noise covariance matrix)
    stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.05,
               pick_ori=pick_ori, weight_norm='unit-noise-gain',
               max_ori_out='signed')

    # View activation time-series in maximum voxel at 100 ms:
    time_idx = stc.time_as_index(0.1)
    max_vox = np.argmax(stc.data[:, time_idx])
    plt.plot(stc.times, stc.data[max_vox, :], color, hold=True,
             label=desc % max_vox)

plt.xlabel('Time (ms)')
plt.ylabel('LCMV value')
plt.ylim(-0.8, 2.2)
plt.title('LCMV in maximum voxel')
plt.legend()
plt.show()


# take absolute value for plotting
stc.data[:, :] = np.abs(stc.data)

# Plot last stc in the brain in 3D with PySurfer if available
brain = stc.plot(hemi='lh', subjects_dir=subjects_dir,
                 initial_time=0.1, time_unit='s')
brain.show_view('lateral')
  • ../../_images/sphx_glr_plot_lcmv_beamformer_001.png
  • ../../_images/sphx_glr_plot_lcmv_beamformer_002.png

Out:

Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Current compensation grade : 0
Reading 0 ... 166799  =      0.000 ...   277.714 secs...
72 matching events found
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 72 events and 421 original time points ...
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on MAG : [u'MEG 1711']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
17 bad epochs dropped
Reading forward solution from /home/ubuntu/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]
Estimating covariance using SHRUNK
Done.
Number of samples used : 6655
[done]
Estimating covariance using SHRUNK
Done.
Number of samples used : 3685
[done]
    305 out of 366 channels remain after picking
    Created an SSP operator (subspace dimension = 3)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
Total rank is 302
combining the current components...
    305 out of 366 channels remain after picking
    Created an SSP operator (subspace dimension = 3)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
Total rank is 302
    305 out of 366 channels remain after picking
    Created an SSP operator (subspace dimension = 3)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
Total rank is 302

Total running time of the script: ( 0 minutes 47.418 seconds)

Gallery generated by Sphinx-Gallery