Note
Click here to download the full example code
Computing various MNE solutions¶
This example shows example fixed- and free-orientation source localizations produced by MNE, dSPM, sLORETA, and eLORETA.
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
# Read data
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked = mne.read_evokeds(fname_evoked, condition='Left Auditory',
baseline=(None, 0))
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
fwd = mne.read_forward_solution(fname_fwd)
cov = mne.read_cov(fname_cov)
Out:
Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
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
Found the data of interest:
t = -199.80 ... 499.49 ms (Left Auditory)
0 CTF compensation matrices available
nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
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
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
Fixed orientation¶
First let’s create a fixed-orientation inverse, with the default weighting.
inv = make_inverse_operator(evoked.info, fwd, cov, loose=0., depth=0.8,
verbose=True)
Out:
Computing inverse operator with 364 channels.
364 out of 366 channels remain after picking
Selected 364 channels
Creating the depth weighting matrix...
203 planar channels
limit = 7262/7498 = 10.020866
scale = 2.58122e-08 exp = 0.8
Picked elements from a free-orientation depth-weighting prior into the fixed-orientation one
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
Whitening the forward solution.
Created an SSP operator (subspace dimension = 4)
Computing data rank from covariance with rank=None
Using tolerance 3.3e-13 (2.2e-16 eps * 305 dim * 4.8 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6 max singular value)
Estimated rank (eeg): 58
EEG: rank 58 computed from 59 data channels with 1 projector
Setting small MEG eigenvalues to zero (without PCA)
Setting small EEG 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 = 5.70263
scaling factor to adjust the trace = 1.18949e+19
Let’s look at the current estimates using MNE. We’ll take the absolute value of the source estimates to simplify the visualization.
snr = 3.0
lambda2 = 1.0 / snr ** 2
kwargs = dict(initial_time=0.08, hemi='both', subjects_dir=subjects_dir,
size=(600, 600))
stc = abs(apply_inverse(evoked, inv, lambda2, 'MNE', verbose=True))
brain = stc.plot(figure=1, **kwargs)
brain.add_text(0.1, 0.9, 'MNE', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 64.5% variance
[done]
Using control points [1.42705934e-10 1.68800290e-10 4.36316212e-10]
Next let’s use the default noise normalization, dSPM:
stc = abs(apply_inverse(evoked, inv, lambda2, 'dSPM', verbose=True))
brain = stc.plot(figure=2, **kwargs)
brain.add_text(0.1, 0.9, 'dSPM', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 64.5% variance
dSPM...
[done]
Using control points [ 4.0616553 4.70033906 14.3479461 ]
And sLORETA:
stc = abs(apply_inverse(evoked, inv, lambda2, 'sLORETA', verbose=True))
brain = stc.plot(figure=3, **kwargs)
brain.add_text(0.1, 0.9, 'sLORETA', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing noise-normalization factors (sLORETA)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 64.5% variance
sLORETA...
[done]
Using control points [1.57323355 1.82882269 4.94418718]
And finally eLORETA:
stc = abs(apply_inverse(evoked, inv, lambda2, 'eLORETA', verbose=True))
brain = stc.plot(figure=4, **kwargs)
brain.add_text(0.1, 0.9, 'eLORETA', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing noise-normalization factors (eLORETA)...
Fitting up to 20 iterations...
Converged on iteration 11 (5.7e-07 < 1e-06)
Assembling eLORETA kernel and modifying inverse
[done]
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained -2750563209363964559360.0% variance
[done]
Using control points [0.43137866 0.51856266 1.63279551]
Free orientation¶
Now let’s not constrain the orientation of the dipoles at all by creating a free-orientation inverse.
inv = make_inverse_operator(evoked.info, fwd, cov, loose=1., depth=0.8,
verbose=True)
Out:
Computing inverse operator with 364 channels.
364 out of 366 channels remain after picking
Selected 364 channels
Creating the depth weighting matrix...
203 planar channels
limit = 7262/7498 = 10.020866
scale = 2.58122e-08 exp = 0.8
Whitening the forward solution.
Created an SSP operator (subspace dimension = 4)
Computing data rank from covariance with rank=None
Using tolerance 3.3e-13 (2.2e-16 eps * 305 dim * 4.8 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6 max singular value)
Estimated rank (eeg): 58
EEG: rank 58 computed from 59 data channels with 1 projector
Setting small MEG eigenvalues to zero (without PCA)
Setting small EEG 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 = 5.2188
scaling factor to adjust the trace = 3.44205e+19
Let’s look at the current estimates using MNE. We’ll take the absolute value of the source estimates to simplify the visualization.
stc = apply_inverse(evoked, inv, lambda2, 'MNE', verbose=True)
brain = stc.plot(figure=5, **kwargs)
brain.add_text(0.1, 0.9, 'MNE', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 64.8% variance
Combining the current components...
[done]
Using control points [7.97082076e-11 9.23922488e-11 2.25445336e-10]
Next let’s use the default noise normalization, dSPM:
stc = apply_inverse(evoked, inv, lambda2, 'dSPM', verbose=True)
brain = stc.plot(figure=6, **kwargs)
brain.add_text(0.1, 0.9, 'dSPM', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 64.8% variance
Combining the current components...
dSPM...
[done]
Using control points [ 3.79973942 4.3837876 13.83479107]
And sLORETA:
stc = apply_inverse(evoked, inv, lambda2, 'sLORETA', verbose=True)
brain = stc.plot(figure=7, **kwargs)
brain.add_text(0.1, 0.9, 'sLORETA', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing noise-normalization factors (sLORETA)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 64.8% variance
Combining the current components...
sLORETA...
[done]
Using control points [1.44151368 1.65914944 4.62665486]
And finally eLORETA:
stc = apply_inverse(evoked, inv, lambda2, 'eLORETA', verbose=True)
brain = stc.plot(figure=8, **kwargs)
brain.add_text(0.1, 0.9, 'eLORETA', 'title', font_size=14)
Out:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 4)
Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing noise-normalization factors (eLORETA)...
Using independent orientation weights
Fitting up to 20 iterations (this make take a while)...
Converged on iteration 11 (4.6e-07 < 1e-06)
Assembling eLORETA kernel and modifying inverse
[done]
Applying inverse operator to "Left Auditory"...
Picked 364 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained -7502039560329552199680.0% variance
Combining the current components...
[done]
Using control points [0.41052351 0.4850979 1.37755651]
Total running time of the script: ( 2 minutes 8.220 seconds)
Estimated memory usage: 620 MB