Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Here we examine 3 ways of localizing event-related synchronization (ERS) of beta band activity in this dataset: Somatosensory using DICS, LCMV beamformer, and dSPM applied to active and baseline covariance matrices.

# Authors: Luke Bloy <luke.bloy@gmail.com>
#          Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op

import numpy as np
import mne
from mne.cov import compute_covariance
from mne.datasets import somato
from mne.time_frequency import csd_morlet
from mne.beamformer import (make_dics, apply_dics_csd, make_lcmv,
                            apply_lcmv_cov)
from mne.minimum_norm import (make_inverse_operator, apply_inverse_cov)

print(__doc__)

Reading the raw data and creating epochs:

data_path = somato.data_path()
subject = '01'
task = 'somato'
raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',
                    'sub-{}_task-{}_meg.fif'.format(subject, task))

raw = mne.io.read_raw_fif(raw_fname)

# We are interested in the beta band (12-30 Hz)
raw.load_data().filter(12, 30)

# The DICS beamformer currently only supports a single sensor type.
# We'll use the gradiometers in this example.
picks = mne.pick_types(raw.info, meg='grad', exclude='bads')

# Read epochs
events = mne.find_events(raw)
epochs = mne.Epochs(raw, events, event_id=1, tmin=-1.5, tmax=2, picks=picks,
                    preload=True)

# Read forward operator and point to freesurfer subject directory
fname_fwd = op.join(data_path, 'derivatives', 'sub-{}'.format(subject),
                    'sub-{}_task-{}-fwd.fif'.format(subject, task))
subjects_dir = op.join(data_path, 'derivatives', 'freesurfer', 'subjects')

fwd = mne.read_forward_solution(fname_fwd)

Out:

Opening raw data file /home/circleci/mne_data/MNE-somato-data/sub-01/meg/sub-01_task-somato_meg.fif...
    Range : 237600 ... 506999 =    791.189 ...  1688.266 secs
Ready.
Reading 0 ... 269399  =      0.000 ...   897.077 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 12 - 30 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 12.00
- Lower transition bandwidth: 3.00 Hz (-6 dB cutoff frequency: 10.50 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 331 samples (1.102 sec)

111 events found
Event IDs: [1]
Not setting metadata
Not setting metadata
111 matching events found
Applying baseline correction (mode: mean)
0 projection items activated
Loading data for 111 events and 1052 original time points ...
0 bad epochs dropped
Reading forward solution from /home/circleci/mne_data/MNE-somato-data/derivatives/sub-01/sub-01_task-somato-fwd.fif...
    Reading a source space...
    [done]
    Reading a source space...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (8155 sources, 306 channels, free orientations)
    Source spaces transformed to the forward solution coordinate frame

Compute covariances

ERS activity starts at 0.5 seconds after stimulus onset.

active_win = (0.5, 1.5)
baseline_win = (-1, 0)

baseline_cov = compute_covariance(epochs, tmin=baseline_win[0],
                                  tmax=baseline_win[1], method='shrunk',
                                  rank=None)
active_cov = compute_covariance(epochs, tmin=active_win[0], tmax=active_win[1],
                                method='shrunk', rank=None)

# Weighted averaging is already in the addition of covariance objects.
common_cov = baseline_cov + active_cov

Out:

Computing rank from data with rank=None
    Using tolerance 1.9e-09 (2.2e-16 eps * 204 dim * 4.2e+04  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
Reducing data rank from 204 -> 204
Estimating covariance using SHRUNK
Done.
Number of samples used : 33411
[done]
Computing rank from data with rank=None
    Using tolerance 2.1e-09 (2.2e-16 eps * 204 dim * 4.7e+04  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
Reducing data rank from 204 -> 204
Estimating covariance using SHRUNK
Done.
Number of samples used : 33411
[done]

Compute some source estimates

Here we will use DICS, LCMV beamformer, and dSPM.

See Compute source power using DICS beamformer for more information about DICS.

def _gen_dics(active_win, baseline_win, epochs):
    freqs = np.logspace(np.log10(12), np.log10(30), 9)
    csd = csd_morlet(epochs, freqs, tmin=-1, tmax=1.5, decim=20)
    csd_baseline = csd_morlet(epochs, freqs, tmin=baseline_win[0],
                              tmax=baseline_win[1], decim=20)
    csd_ers = csd_morlet(epochs, freqs, tmin=active_win[0], tmax=active_win[1],
                         decim=20)
    filters = make_dics(epochs.info, fwd, csd.mean(), pick_ori='max-power')
    stc_base, freqs = apply_dics_csd(csd_baseline.mean(), filters)
    stc_act, freqs = apply_dics_csd(csd_ers.mean(), filters)
    stc_act /= stc_base
    return stc_act


# generate lcmv source estimate
def _gen_lcmv(active_cov, baseline_cov, common_cov):
    filters = make_lcmv(epochs.info, fwd, common_cov, reg=0.05,
                        noise_cov=None, pick_ori='max-power')
    stc_base = apply_lcmv_cov(baseline_cov, filters)
    stc_act = apply_lcmv_cov(active_cov, filters)
    stc_act /= stc_base
    return stc_act


# generate mne/dSPM source estimate
def _gen_mne(active_cov, baseline_cov, common_cov, fwd, info, method='dSPM'):
    inverse_operator = make_inverse_operator(info, fwd, common_cov)
    stc_act = apply_inverse_cov(active_cov, info, inverse_operator,
                                method=method, verbose=True)
    stc_base = apply_inverse_cov(baseline_cov, info, inverse_operator,
                                 method=method, verbose=True)
    stc_act /= stc_base
    return stc_act


# Compute source estimates
stc_dics = _gen_dics(active_win, baseline_win, epochs)
stc_lcmv = _gen_lcmv(active_cov, baseline_cov, common_cov)
stc_dspm = _gen_mne(active_cov, baseline_cov, common_cov, fwd, epochs.info)

Out:

Computing cross-spectral density from epochs...
    Computing CSD matrix for epoch 1
    Computing CSD matrix for epoch 2
    Computing CSD matrix for epoch 3
    Computing CSD matrix for epoch 4
    Computing CSD matrix for epoch 5
    Computing CSD matrix for epoch 6
    Computing CSD matrix for epoch 7
    Computing CSD matrix for epoch 8
    Computing CSD matrix for epoch 9
    Computing CSD matrix for epoch 10
    Computing CSD matrix for epoch 11
    Computing CSD matrix for epoch 12
    Computing CSD matrix for epoch 13
    Computing CSD matrix for epoch 14
    Computing CSD matrix for epoch 15
    Computing CSD matrix for epoch 16
    Computing CSD matrix for epoch 17
    Computing CSD matrix for epoch 18
    Computing CSD matrix for epoch 19
    Computing CSD matrix for epoch 20
    Computing CSD matrix for epoch 21
    Computing CSD matrix for epoch 22
    Computing CSD matrix for epoch 23
    Computing CSD matrix for epoch 24
    Computing CSD matrix for epoch 25
    Computing CSD matrix for epoch 26
    Computing CSD matrix for epoch 27
    Computing CSD matrix for epoch 28
    Computing CSD matrix for epoch 29
    Computing CSD matrix for epoch 30
    Computing CSD matrix for epoch 31
    Computing CSD matrix for epoch 32
    Computing CSD matrix for epoch 33
    Computing CSD matrix for epoch 34
    Computing CSD matrix for epoch 35
    Computing CSD matrix for epoch 36
    Computing CSD matrix for epoch 37
    Computing CSD matrix for epoch 38
    Computing CSD matrix for epoch 39
    Computing CSD matrix for epoch 40
    Computing CSD matrix for epoch 41
    Computing CSD matrix for epoch 42
    Computing CSD matrix for epoch 43
    Computing CSD matrix for epoch 44
    Computing CSD matrix for epoch 45
    Computing CSD matrix for epoch 46
    Computing CSD matrix for epoch 47
    Computing CSD matrix for epoch 48
    Computing CSD matrix for epoch 49
    Computing CSD matrix for epoch 50
    Computing CSD matrix for epoch 51
    Computing CSD matrix for epoch 52
    Computing CSD matrix for epoch 53
    Computing CSD matrix for epoch 54
    Computing CSD matrix for epoch 55
    Computing CSD matrix for epoch 56
    Computing CSD matrix for epoch 57
    Computing CSD matrix for epoch 58
    Computing CSD matrix for epoch 59
    Computing CSD matrix for epoch 60
    Computing CSD matrix for epoch 61
    Computing CSD matrix for epoch 62
    Computing CSD matrix for epoch 63
    Computing CSD matrix for epoch 64
    Computing CSD matrix for epoch 65
    Computing CSD matrix for epoch 66
    Computing CSD matrix for epoch 67
    Computing CSD matrix for epoch 68
    Computing CSD matrix for epoch 69
    Computing CSD matrix for epoch 70
    Computing CSD matrix for epoch 71
    Computing CSD matrix for epoch 72
    Computing CSD matrix for epoch 73
    Computing CSD matrix for epoch 74
    Computing CSD matrix for epoch 75
    Computing CSD matrix for epoch 76
    Computing CSD matrix for epoch 77
    Computing CSD matrix for epoch 78
    Computing CSD matrix for epoch 79
    Computing CSD matrix for epoch 80
    Computing CSD matrix for epoch 81
    Computing CSD matrix for epoch 82
    Computing CSD matrix for epoch 83
    Computing CSD matrix for epoch 84
    Computing CSD matrix for epoch 85
    Computing CSD matrix for epoch 86
    Computing CSD matrix for epoch 87
    Computing CSD matrix for epoch 88
    Computing CSD matrix for epoch 89
    Computing CSD matrix for epoch 90
    Computing CSD matrix for epoch 91
    Computing CSD matrix for epoch 92
    Computing CSD matrix for epoch 93
    Computing CSD matrix for epoch 94
    Computing CSD matrix for epoch 95
    Computing CSD matrix for epoch 96
    Computing CSD matrix for epoch 97
    Computing CSD matrix for epoch 98
    Computing CSD matrix for epoch 99
    Computing CSD matrix for epoch 100
    Computing CSD matrix for epoch 101
    Computing CSD matrix for epoch 102
    Computing CSD matrix for epoch 103
    Computing CSD matrix for epoch 104
    Computing CSD matrix for epoch 105
    Computing CSD matrix for epoch 106
    Computing CSD matrix for epoch 107
    Computing CSD matrix for epoch 108
    Computing CSD matrix for epoch 109
    Computing CSD matrix for epoch 110
    Computing CSD matrix for epoch 111
[done]
Computing cross-spectral density from epochs...
    Computing CSD matrix for epoch 1
    Computing CSD matrix for epoch 2
    Computing CSD matrix for epoch 3
    Computing CSD matrix for epoch 4
    Computing CSD matrix for epoch 5
    Computing CSD matrix for epoch 6
    Computing CSD matrix for epoch 7
    Computing CSD matrix for epoch 8
    Computing CSD matrix for epoch 9
    Computing CSD matrix for epoch 10
    Computing CSD matrix for epoch 11
    Computing CSD matrix for epoch 12
    Computing CSD matrix for epoch 13
    Computing CSD matrix for epoch 14
    Computing CSD matrix for epoch 15
    Computing CSD matrix for epoch 16
    Computing CSD matrix for epoch 17
    Computing CSD matrix for epoch 18
    Computing CSD matrix for epoch 19
    Computing CSD matrix for epoch 20
    Computing CSD matrix for epoch 21
    Computing CSD matrix for epoch 22
    Computing CSD matrix for epoch 23
    Computing CSD matrix for epoch 24
    Computing CSD matrix for epoch 25
    Computing CSD matrix for epoch 26
    Computing CSD matrix for epoch 27
    Computing CSD matrix for epoch 28
    Computing CSD matrix for epoch 29
    Computing CSD matrix for epoch 30
    Computing CSD matrix for epoch 31
    Computing CSD matrix for epoch 32
    Computing CSD matrix for epoch 33
    Computing CSD matrix for epoch 34
    Computing CSD matrix for epoch 35
    Computing CSD matrix for epoch 36
    Computing CSD matrix for epoch 37
    Computing CSD matrix for epoch 38
    Computing CSD matrix for epoch 39
    Computing CSD matrix for epoch 40
    Computing CSD matrix for epoch 41
    Computing CSD matrix for epoch 42
    Computing CSD matrix for epoch 43
    Computing CSD matrix for epoch 44
    Computing CSD matrix for epoch 45
    Computing CSD matrix for epoch 46
    Computing CSD matrix for epoch 47
    Computing CSD matrix for epoch 48
    Computing CSD matrix for epoch 49
    Computing CSD matrix for epoch 50
    Computing CSD matrix for epoch 51
    Computing CSD matrix for epoch 52
    Computing CSD matrix for epoch 53
    Computing CSD matrix for epoch 54
    Computing CSD matrix for epoch 55
    Computing CSD matrix for epoch 56
    Computing CSD matrix for epoch 57
    Computing CSD matrix for epoch 58
    Computing CSD matrix for epoch 59
    Computing CSD matrix for epoch 60
    Computing CSD matrix for epoch 61
    Computing CSD matrix for epoch 62
    Computing CSD matrix for epoch 63
    Computing CSD matrix for epoch 64
    Computing CSD matrix for epoch 65
    Computing CSD matrix for epoch 66
    Computing CSD matrix for epoch 67
    Computing CSD matrix for epoch 68
    Computing CSD matrix for epoch 69
    Computing CSD matrix for epoch 70
    Computing CSD matrix for epoch 71
    Computing CSD matrix for epoch 72
    Computing CSD matrix for epoch 73
    Computing CSD matrix for epoch 74
    Computing CSD matrix for epoch 75
    Computing CSD matrix for epoch 76
    Computing CSD matrix for epoch 77
    Computing CSD matrix for epoch 78
    Computing CSD matrix for epoch 79
    Computing CSD matrix for epoch 80
    Computing CSD matrix for epoch 81
    Computing CSD matrix for epoch 82
    Computing CSD matrix for epoch 83
    Computing CSD matrix for epoch 84
    Computing CSD matrix for epoch 85
    Computing CSD matrix for epoch 86
    Computing CSD matrix for epoch 87
    Computing CSD matrix for epoch 88
    Computing CSD matrix for epoch 89
    Computing CSD matrix for epoch 90
    Computing CSD matrix for epoch 91
    Computing CSD matrix for epoch 92
    Computing CSD matrix for epoch 93
    Computing CSD matrix for epoch 94
    Computing CSD matrix for epoch 95
    Computing CSD matrix for epoch 96
    Computing CSD matrix for epoch 97
    Computing CSD matrix for epoch 98
    Computing CSD matrix for epoch 99
    Computing CSD matrix for epoch 100
    Computing CSD matrix for epoch 101
    Computing CSD matrix for epoch 102
    Computing CSD matrix for epoch 103
    Computing CSD matrix for epoch 104
    Computing CSD matrix for epoch 105
    Computing CSD matrix for epoch 106
    Computing CSD matrix for epoch 107
    Computing CSD matrix for epoch 108
    Computing CSD matrix for epoch 109
    Computing CSD matrix for epoch 110
    Computing CSD matrix for epoch 111
[done]
Computing cross-spectral density from epochs...
    Computing CSD matrix for epoch 1
    Computing CSD matrix for epoch 2
    Computing CSD matrix for epoch 3
    Computing CSD matrix for epoch 4
    Computing CSD matrix for epoch 5
    Computing CSD matrix for epoch 6
    Computing CSD matrix for epoch 7
    Computing CSD matrix for epoch 8
    Computing CSD matrix for epoch 9
    Computing CSD matrix for epoch 10
    Computing CSD matrix for epoch 11
    Computing CSD matrix for epoch 12
    Computing CSD matrix for epoch 13
    Computing CSD matrix for epoch 14
    Computing CSD matrix for epoch 15
    Computing CSD matrix for epoch 16
    Computing CSD matrix for epoch 17
    Computing CSD matrix for epoch 18
    Computing CSD matrix for epoch 19
    Computing CSD matrix for epoch 20
    Computing CSD matrix for epoch 21
    Computing CSD matrix for epoch 22
    Computing CSD matrix for epoch 23
    Computing CSD matrix for epoch 24
    Computing CSD matrix for epoch 25
    Computing CSD matrix for epoch 26
    Computing CSD matrix for epoch 27
    Computing CSD matrix for epoch 28
    Computing CSD matrix for epoch 29
    Computing CSD matrix for epoch 30
    Computing CSD matrix for epoch 31
    Computing CSD matrix for epoch 32
    Computing CSD matrix for epoch 33
    Computing CSD matrix for epoch 34
    Computing CSD matrix for epoch 35
    Computing CSD matrix for epoch 36
    Computing CSD matrix for epoch 37
    Computing CSD matrix for epoch 38
    Computing CSD matrix for epoch 39
    Computing CSD matrix for epoch 40
    Computing CSD matrix for epoch 41
    Computing CSD matrix for epoch 42
    Computing CSD matrix for epoch 43
    Computing CSD matrix for epoch 44
    Computing CSD matrix for epoch 45
    Computing CSD matrix for epoch 46
    Computing CSD matrix for epoch 47
    Computing CSD matrix for epoch 48
    Computing CSD matrix for epoch 49
    Computing CSD matrix for epoch 50
    Computing CSD matrix for epoch 51
    Computing CSD matrix for epoch 52
    Computing CSD matrix for epoch 53
    Computing CSD matrix for epoch 54
    Computing CSD matrix for epoch 55
    Computing CSD matrix for epoch 56
    Computing CSD matrix for epoch 57
    Computing CSD matrix for epoch 58
    Computing CSD matrix for epoch 59
    Computing CSD matrix for epoch 60
    Computing CSD matrix for epoch 61
    Computing CSD matrix for epoch 62
    Computing CSD matrix for epoch 63
    Computing CSD matrix for epoch 64
    Computing CSD matrix for epoch 65
    Computing CSD matrix for epoch 66
    Computing CSD matrix for epoch 67
    Computing CSD matrix for epoch 68
    Computing CSD matrix for epoch 69
    Computing CSD matrix for epoch 70
    Computing CSD matrix for epoch 71
    Computing CSD matrix for epoch 72
    Computing CSD matrix for epoch 73
    Computing CSD matrix for epoch 74
    Computing CSD matrix for epoch 75
    Computing CSD matrix for epoch 76
    Computing CSD matrix for epoch 77
    Computing CSD matrix for epoch 78
    Computing CSD matrix for epoch 79
    Computing CSD matrix for epoch 80
    Computing CSD matrix for epoch 81
    Computing CSD matrix for epoch 82
    Computing CSD matrix for epoch 83
    Computing CSD matrix for epoch 84
    Computing CSD matrix for epoch 85
    Computing CSD matrix for epoch 86
    Computing CSD matrix for epoch 87
    Computing CSD matrix for epoch 88
    Computing CSD matrix for epoch 89
    Computing CSD matrix for epoch 90
    Computing CSD matrix for epoch 91
    Computing CSD matrix for epoch 92
    Computing CSD matrix for epoch 93
    Computing CSD matrix for epoch 94
    Computing CSD matrix for epoch 95
    Computing CSD matrix for epoch 96
    Computing CSD matrix for epoch 97
    Computing CSD matrix for epoch 98
    Computing CSD matrix for epoch 99
    Computing CSD matrix for epoch 100
    Computing CSD matrix for epoch 101
    Computing CSD matrix for epoch 102
    Computing CSD matrix for epoch 103
    Computing CSD matrix for epoch 104
    Computing CSD matrix for epoch 105
    Computing CSD matrix for epoch 106
    Computing CSD matrix for epoch 107
    Computing CSD matrix for epoch 108
    Computing CSD matrix for epoch 109
    Computing CSD matrix for epoch 110
    Computing CSD matrix for epoch 111
[done]
Identifying common channels ...
Dropped the following channels:
['MEG 2141', 'MEG 0241', 'MEG 0921', 'MEG 0741', 'MEG 0431', 'MEG 2021', 'MEG 2641', 'MEG 2041', 'MEG 2121', 'MEG 1321', 'MEG 2621', 'MEG 0721', 'MEG 1921', 'MEG 1731', 'MEG 2011', 'MEG 1821', 'MEG 2631', 'MEG 0611', 'MEG 1111', 'MEG 0531', 'MEG 0621', 'MEG 1041', 'MEG 0321', 'MEG 1311', 'MEG 2111', 'MEG 2221', 'MEG 1441', 'MEG 1221', 'MEG 2241', 'MEG 1241', 'MEG 0811', 'MEG 1131', 'MEG 0141', 'MEG 2521', 'MEG 0641', 'MEG 2611', 'MEG 1331', 'MEG 0631', 'MEG 1541', 'MEG 0131', 'MEG 1911', 'MEG 1531', 'MEG 1021', 'MEG 2131', 'MEG 1631', 'MEG 1931', 'MEG 2231', 'MEG 1011', 'MEG 1811', 'MEG 1031', 'MEG 0941', 'MEG 1231', 'MEG 1831', 'MEG 0411', 'MEG 1421', 'MEG 0421', 'MEG 0821', 'MEG 1411', 'MEG 0221', 'MEG 1431', 'MEG 2531', 'MEG 0231', 'MEG 0711', 'MEG 2431', 'MEG 1121', 'MEG 2541', 'MEG 2031', 'MEG 2421', 'MEG 0931', 'MEG 1521', 'MEG 2331', 'MEG 0521', 'MEG 2411', 'MEG 1621', 'MEG 2211', 'MEG 2341', 'MEG 1841', 'MEG 0111', 'MEG 1141', 'MEG 0441', 'MEG 1211', 'MEG 1941', 'MEG 0121', 'MEG 0511', 'MEG 0211', 'MEG 0311', 'MEG 1641', 'MEG 2511', 'MEG 1511', 'MEG 1341', 'MEG 2441', 'MEG 0731', 'MEG 0341', 'MEG 2321', 'MEG 0541', 'MEG 0911', 'MEG 0331', 'MEG 1611', 'MEG 1721', 'MEG 1741', 'MEG 1711', 'MEG 2311']
Computing inverse operator with 204 channels.
    204 out of 306 channels remain after picking
Selected 204 channels
Creating the depth weighting matrix...
Whitening the forward solution.
Computing rank from covariance with rank=None
    Using tolerance 4.5e+08 (2.2e-16 eps * 204 dim * 1e+22  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
    Setting small GRAD eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing DICS spatial filters...
Computing beamformer filters for 8155 sources
Filter computation complete
Computing DICS source power...
[done]
Computing DICS source power...
[done]
Computing rank from covariance with rank='info'
    GRAD: rank 204 after 0 projectors applied to 204 channels
Computing rank from covariance with rank='info'
    GRAD: rank 204 after 0 projectors applied to 204 channels
Making LCMV beamformer with rank {'grad': 204}
Computing inverse operator with 204 channels.
    204 out of 306 channels remain after picking
Selected 204 channels
Whitening the forward solution.
Computing rank from covariance with rank={'grad': 204}
    Setting small GRAD eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing beamformer filters for 8155 sources
Filter computation complete
Converting forward solution to surface orientation
    No patch info available. The standard source space normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
Computing inverse operator with 204 channels.
    204 out of 306 channels remain after picking
Selected 204 channels
Creating the depth weighting matrix...
    204 planar channels
    limit = 7615/8155 = 10.004172
    scale = 5.17919e-08 exp = 0.8
Applying loose dipole orientations to surface source spaces: 0.2
Whitening the forward solution.
Computing rank from covariance with rank=None
    Using tolerance 2.6e-13 (2.2e-16 eps * 204 dim * 5.7  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
    Setting small GRAD eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
    largest singular value = 4.31582
    scaling factor to adjust the trace = 2.53127e+21
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 1
    Created the regularized inverter
    The projection vectors do not apply to these channels.
    Created the whitener using a noise covariance matrix with rank 204 (0 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "cov"...
    Picked 204 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Computing residual...
    Explained  56.5% variance
    dSPM...
    Combining the current components...
[done]
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 1
    Created the regularized inverter
    The projection vectors do not apply to these channels.
    Created the whitener using a noise covariance matrix with rank 204 (0 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "cov"...
    Picked 204 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Computing residual...
    Explained  56.5% variance
    dSPM...
    Combining the current components...
[done]

Plot source estimates

for method, stc in zip(['DICS', 'LCMV', 'dSPM'],
                       [stc_dics, stc_lcmv, stc_dspm]):
    title = '%s source power in the 12-30 Hz frequency band' % method
    brain = stc.plot(hemi='rh', subjects_dir=subjects_dir,
                     subject=subject, time_label=title)
plot evoked ers source power
  • plot evoked ers source power
  • plot evoked ers source power

Out:

Using control points [1.39376309 1.43246371 1.57854484]
Using control points [1.51924176 1.56913499 1.96717505]
Using control points [1.32360332 1.35265114 1.51367404]

Total running time of the script: ( 1 minutes 7.881 seconds)

Estimated memory usage: 1185 MB

Gallery generated by Sphinx-Gallery