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"""
===================================================================
Compute LCMV inverse solution on evoked data in volume source space
===================================================================
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 lcmv
from nilearn.plotting import plot_stat_map
from nilearn.image import index_img
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-vol-7-fwd.fif'
###############################################################################
# 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, proj=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)
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
exclude='bads', selection=left_temporal_channels)
# 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()
forward = mne.read_forward_solution(fname_fwd)
# 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')
# Run free orientation (vector) beamformer. Source orientation can be
# restricted by setting pick_ori to 'max-power' (or 'normal' but only when
# using a surface-based source space)
stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01, pick_ori=None)
# 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 ######################################################
plot_stat_map(index_img(img, 61), t1_fname, threshold=0.8,
title='LCMV (t=%.1f s.)' % stc.times[61])
# plot source time courses with the maximum peak amplitudes
plt.figure()
plt.plot(stc.times, stc.data[np.argsort(np.max(stc.data, axis=1))[-40:]].T)
plt.xlabel('Time (ms)')
plt.ylabel('LCMV value')
plt.show()
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