Note

Click here to download the full example code

Compute LCMV inverse solution on an auditory evoked dataset in a volume source space. It stores the solution in a nifti file for visualisation, e.g. with Freeview.

```
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, apply_lcmv
from nilearn.plotting import plot_stat_map
from nilearn.image import index_img
print(__doc__)
# sphinx_gallery_thumbnail_number = 3
```

Data preprocessing:

```
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-vol-7-fwd.fif'
# Get epochs
event_id, tmin, tmax = [1, 2], -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: gradiometers and magnetometers, excluding bad channels
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
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(ts_args=dict(time_unit='s'),
topomap_args=dict(time_unit='s'))
```

Out:

```
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')
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15,
method='shrunk')
# Read forward model
forward = mne.read_forward_solution(fname_fwd)
# 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')
# Apply this spatial filter to the evoked data.
stc = apply_lcmv(evoked, filters, max_ori_out='signed')
```

Out:

```
Estimating covariance using SHRUNK
Done.
Number of samples used : 13794
[done]
Estimating covariance using SHRUNK
Done.
Number of samples used : 7638
[done]
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
305 out of 306 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
```

Plot source space activity:

```
# take absolute values for plotting
stc.data[:, :] = np.abs(stc.data)
# Save result in stc files
stc.save('lcmv-vol')
stc.crop(0.0, 0.2)
# Save result in a 4D nifti file
img = mne.save_stc_as_volume('lcmv_inverse.nii.gz', stc,
forward['src'], mri_resolution=False)
t1_fname = data_path + '/subjects/sample/mri/T1.mgz'
# Plotting with nilearn ######################################################
# Based on the visualization of the sensor space data (gradiometers), plot
# activity at 88 ms
idx = stc.time_as_index(0.088)
plot_stat_map(index_img(img, idx), t1_fname, threshold=0.45,
title='LCMV (t=%.3f s.)' % stc.times[idx])
# plot source time courses with the maximum peak amplitudes at 88 ms
plt.figure()
plt.plot(stc.times, stc.data[np.argsort(np.max(stc.data[:, idx],
axis=1))[-40:]].T)
plt.xlabel('Time (ms)')
plt.ylabel('LCMV value')
plt.show()
```

Out:

```
Writing STC to disk...
[done]
```

**Total running time of the script:** ( 0 minutes 55.979 seconds)