def test_lcmv_raw(): """Test LCMV with raw data """ raw, _, _, _, noise_cov, label, forward, _, _, _ =\ _get_data(all_forward=False, epochs=False, data_cov=False) tmin, tmax = 0, 20 start, stop = raw.time_as_index([tmin, tmax]) # use only the left-temporal MEG channels for LCMV left_temporal_channels = mne.read_selection('Left-temporal') picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads', selection=left_temporal_channels) data_cov = mne.compute_raw_data_covariance(raw, tmin=tmin, tmax=tmax) stc = lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=label, start=start, stop=stop, picks=picks) assert_array_almost_equal(np.array([tmin, tmax]), np.array([stc.times[0], stc.times[-1]]), decimal=2) # make sure we get an stc with vertices only in the lh vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']] assert_true(len(stc.vertno[0]) == len(np.intersect1d(vertno[0], label.vertices))) assert_true(len(stc.vertno[1]) == 0)
def test_lcmv_raw(): """Test LCMV with raw data.""" raw, _, _, _, noise_cov, label, forward, _, _, _ =\ _get_data(all_forward=False, epochs=False, data_cov=False) tmin, tmax = 0, 20 start, stop = raw.time_as_index([tmin, tmax]) # use only the left-temporal MEG channels for LCMV data_cov = mne.compute_raw_covariance(raw, tmin=tmin, tmax=tmax) stc = lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=label, start=start, stop=stop, max_ori_out='signed') assert_array_almost_equal(np.array([tmin, tmax]), np.array([stc.times[0], stc.times[-1]]), decimal=2) # make sure we get an stc with vertices only in the lh vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']] assert_true( len(stc.vertices[0]) == len(np.intersect1d(vertno[0], label.vertices))) assert_true(len(stc.vertices[1]) == 0)
def test_lcmv_raw(): """Test LCMV with raw data """ tmin, tmax = 0, 20 # Setup for reading the raw data raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels # Set up pick list: EEG + MEG - bad channels (modify to your needs) left_temporal_channels = mne.read_selection('Left-temporal') picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, exclude='bads', selection=left_temporal_channels) noise_cov = mne.read_cov(fname_cov) noise_cov = mne.cov.regularize(noise_cov, raw.info, mag=0.05, grad=0.05, eeg=0.1, proj=True) start, stop = raw.time_as_index([tmin, tmax]) # use only the left-temporal MEG channels for LCMV picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads', selection=left_temporal_channels) data_cov = mne.compute_raw_data_covariance(raw, tmin=tmin, tmax=tmax) stc = lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=label, start=start, stop=stop, picks=picks) assert_array_almost_equal(np.array([tmin, tmax]), np.array([stc.times[0], stc.times[-1]]), decimal=2) # make sure we get an stc with vertices only in the lh vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']] assert_true( len(stc.vertno[0]) == len(np.intersect1d(vertno[0], label.vertices))) assert_true(len(stc.vertno[1]) == 0)
def test_lcmv_raw(): """Test LCMV with raw data """ forward = mne.read_forward_solution(fname_fwd) label = mne.read_label(fname_label) noise_cov = mne.read_cov(fname_cov) raw = mne.fiff.Raw(fname_raw, preload=False) tmin, tmax = 0, 20 # Setup for reading the raw data raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels # Set up pick list: EEG + MEG - bad channels (modify to your needs) left_temporal_channels = mne.read_selection('Left-temporal') picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, exclude='bads', selection=left_temporal_channels) noise_cov = mne.read_cov(fname_cov) noise_cov = mne.cov.regularize(noise_cov, raw.info, mag=0.05, grad=0.05, eeg=0.1, proj=True) start, stop = raw.time_as_index([tmin, tmax]) # use only the left-temporal MEG channels for LCMV picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads', selection=left_temporal_channels) data_cov = mne.compute_raw_data_covariance(raw, tmin=tmin, tmax=tmax) stc = lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=label, start=start, stop=stop, picks=picks) assert_array_almost_equal(np.array([tmin, tmax]), np.array([stc.times[0], stc.times[-1]]), decimal=2) # make sure we get an stc with vertices only in the lh vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']] assert_true(len(stc.vertno[0]) == len(np.intersect1d(vertno[0], label.vertices))) assert_true(len(stc.vertno[1]) == 0)
def test_lcmv_raw(): """Test LCMV with raw data.""" raw, _, _, _, noise_cov, label, forward, _, _, _ =\ _get_data(all_forward=False, epochs=False, data_cov=False) tmin, tmax = 0, 20 start, stop = raw.time_as_index([tmin, tmax]) # use only the left-temporal MEG channels for LCMV data_cov = mne.compute_raw_covariance(raw, tmin=tmin, tmax=tmax) stc = lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=label, start=start, stop=stop, max_ori_out='signed') assert_array_almost_equal(np.array([tmin, tmax]), np.array([stc.times[0], stc.times[-1]]), decimal=2) # make sure we get an stc with vertices only in the lh vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']] assert_true(len(stc.vertices[0]) == len(np.intersect1d(vertno[0], label.vertices))) assert_true(len(stc.vertices[1]) == 0)
def test_lcmv_raw(): """Test LCMV with raw data """ raw, _, _, _, noise_cov, label, forward, _, _, _ =\ _get_data(all_forward=False, epochs=False, data_cov=False) tmin, tmax = 0, 20 start, stop = raw.time_as_index([tmin, tmax]) # use only the left-temporal MEG channels for LCMV left_temporal_channels = mne.read_selection('Left-temporal') picks = mne.pick_types(raw.info, meg=True, exclude='bads', selection=left_temporal_channels) data_cov = mne.compute_raw_data_covariance(raw, tmin=tmin, tmax=tmax) stc = lcmv_raw(raw, forward, noise_cov, data_cov, reg=0.01, label=label, start=start, stop=stop, picks=picks) assert_array_almost_equal(np.array([tmin, tmax]), np.array([stc.times[0], stc.times[-1]]), decimal=2) # make sure we get an stc with vertices only in the lh vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']] assert_true( len(stc.vertno[0]) == len(np.intersect1d(vertno[0], label.vertices))) assert_true(len(stc.vertno[1]) == 0)