Note

Click here to download the full example code

# Compute LCMV inverse solution in volume source space¶

Compute LCMV beamformer on an auditory evoked dataset in a volume source space.

```
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 3
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, apply_lcmv
print(__doc__)
```

Out:

```
```

Data preprocessing:

```
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
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-vol-7-fwd.fif'
# Get epochs
event_id, tmin, tmax = [1, 2], -0.2, 0.5
# Read forward model
forward = mne.read_forward_solution(fname_fwd)
# 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)
# Pick the channels of interest
raw.pick(['meg', 'eog'])
# Read epochs
proj = False # already applied
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
baseline=(None, 0), preload=True, proj=proj,
reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
evoked = epochs.average()
# Visualize sensor space data
evoked.plot_joint()
```

Out:

```
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-vol-7-fwd.fif...
Reading a source space...
[done]
1 source spaces read
Desired named matrix (kind = 3523) not available
Read MEG forward solution (3757 sources, 306 channels, free orientations)
Source spaces transformed to the forward solution coordinate frame
Opening raw data file /home/circleci/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...
145 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
Created an SSP operator (subspace dimension = 3)
Loading data for 145 events and 421 original time points ...
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on MAG : ['MEG 0111', 'MEG 1411', 'MEG 1421']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on MAG : ['MEG 1131', 'MEG 1411', 'MEG 1421']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on MAG : ['MEG 1711']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on MAG : ['MEG 1711']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on MAG : ['MEG 1421']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
31 bad epochs dropped
```

Compute covariance matrices, fit and apply spatial filter.

```
# Read regularized noise covariance and compute regularized data covariance
noise_cov = mne.compute_covariance(epochs, tmin=tmin, tmax=0, method='shrunk',
rank=None)
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15,
method='shrunk', rank=None)
# Compute weights of free orientation (vector) beamformer with weight
# normalization (neural activity index, NAI). Providing a noise covariance
# matrix enables whitening of the data and forward solution. Source orientation
# is optimized by setting pick_ori to 'max-power'.
# weight_norm can also be set to 'unit-noise-gain'. Source orientation can also
# be 'normal' (but only when using a surface-based source space) or None,
# which computes a vector beamfomer. Note, however, that not all combinations
# of orientation selection and weight normalization are implemented yet.
filters = make_lcmv(evoked.info, forward, data_cov, reg=0.05,
noise_cov=noise_cov, pick_ori='max-power',
weight_norm='nai', rank=None)
print(filters)
# You can save these with:
# filters.save('filters-lcmv.h5')
# Apply this spatial filter to the evoked data.
stc = apply_lcmv(evoked, filters, max_ori_out='signed')
```

Out:

```
Computing data rank from raw with rank=None
Using tolerance 9.3e-09 (2.2e-16 eps * 305 dim * 1.4e+05 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Created an SSP operator (subspace dimension = 3)
Setting small MEG eigenvalues to zero (without PCA)
Reducing data rank from 305 -> 302
Estimating covariance using SHRUNK
Done.
Number of samples used : 13794
[done]
Computing data rank from raw with rank=None
Using tolerance 7.7e-09 (2.2e-16 eps * 305 dim * 1.1e+05 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Created an SSP operator (subspace dimension = 3)
Setting small MEG eigenvalues to zero (without PCA)
Reducing data rank from 305 -> 302
Estimating covariance using SHRUNK
Done.
Number of samples used : 7638
[done]
Computing data rank from covariance with rank=None
Using tolerance 1.1e-12 (2.2e-16 eps * 305 dim * 16 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Computing data rank from covariance with rank=None
Using tolerance 4.1e-13 (2.2e-16 eps * 305 dim * 6.1 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Making LCMV beamformer with rank {'meg': 302}
Computing inverse operator with 305 channels.
305 out of 306 channels remain after picking
Selected 305 channels
Whitening the forward solution.
Created an SSP operator (subspace dimension = 3)
Computing data rank from covariance with rank={'meg': 302}
Setting small MEG eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
<Beamformer | LCMV, subject unknown, 3757 vert, 305 ch, max-power ori, nai norm, rank 302>
```

Plot source space activity:

```
# You can save result in stc files with:
# stc.save('lcmv-vol')
clim = dict(kind='value', pos_lims=[0.3, 0.6, 0.9])
stc.plot(src=forward['src'], subject='sample', subjects_dir=subjects_dir,
clim=clim)
```

We can also visualize the activity on a “glass brain” (shown here with absolute values):

```
clim = dict(kind='value', lims=[0.3, 0.6, 0.9])
abs(stc).plot(src=forward['src'], subject='sample', subjects_dir=subjects_dir,
mode='glass_brain', clim=clim)
```

**Total running time of the script:** ( 0 minutes 26.004 seconds)

**Estimated memory usage:** 961 MB