def test_compute_minimum_norm(): """Test MNE inverse computation """ setno = 0 noise_cov = mne.Covariance(fname_cov) forward = mne.read_forward_solution(fname_fwd) evoked = mne.fiff.Evoked(fname_data, setno=setno, baseline=(None, 0)) whitener = noise_cov.get_whitener(evoked.info, mag_reg=0.1, grad_reg=0.1, eeg_reg=0.1, pca=True) stc, K, W = mne.minimum_norm(evoked, forward, whitener, orientation='loose', method='dspm', snr=3, loose=0.2)
fname_evoked = data_path + "/MEG/sample/sample_audvis-ave.fif" setno = 0 snr = 3.0 lambda2 = 1.0 / snr ** 2 dSPM = True # Load data evoked = Evoked(fname_evoked, setno=setno, baseline=(None, 0)) forward = mne.read_forward_solution(fname_fwd) noise_cov = mne.Covariance(fname_cov) # Compute whitener from noise covariance matrix whitener = noise_cov.get_whitener(evoked.info, mag_reg=0.1, grad_reg=0.1, eeg_reg=0.1, pca=True) # Compute inverse solution stc, K, W = mne.minimum_norm(evoked, forward, whitener, orientation="loose", method="dspm", snr=3, loose=0.2) # Save result in stc files lh_vertices = stc["inv"]["src"][0]["vertno"] rh_vertices = stc["inv"]["src"][1]["vertno"] lh_data = stc["sol"][: len(lh_vertices)] rh_data = stc["sol"][-len(rh_vertices) :] mne.write_stc("mne_dSPM_inverse-lh.stc", tmin=stc["tmin"], tstep=stc["tstep"], vertices=lh_vertices, data=lh_data) mne.write_stc("mne_dSPM_inverse-rh.stc", tmin=stc["tmin"], tstep=stc["tstep"], vertices=rh_vertices, data=rh_data) ############################################################################### # View activation time-series times = stc["tmin"] + stc["tstep"] * np.arange(stc["sol"].shape[1]) pl.close("all") pl.plot(1e3 * times, stc["sol"][::100, :].T)