Table 5.

Inverse modeling using the LCMV beamformer.

import mne # load raw data and create epochs and evoked objects as in Table 1, but picking # only MEG channels using mne.fiff.pick_types(raw.info, meg=True, eeg=False) # compute noise and data covariance noise_cov = mne.compute_covariance(epochs, tmax=0.0) noise_cov = mne.cov.regularize (noise_cov, evoked.info, mag=0.05, grad=0.05, eeg=0.1, proj=True) data_cov = mne.compute_covariance (epochs, tmin=0.04, tmax=0.15) # compute LCMV inverse solution fwd_fname = 'sample_audvis-meg-vol-7-fwd.fif' fwd = mne.read_forward_solution (fwd_fname, surf_ori=True) stc = mne.beamformer.lcmv(evoked, fwd, noise_cov, data_cov, reg=0.1, pick_ori='max-power') # save result in 4D nifti file for plotting with Freesurfer stc.save_as_volume('lcmv.nii.gz', fwd['src'], mri_resolution=False)