reject = None events = mne.find_events(raw, stim_channel='STI 014') epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=baseline, preload=True, reject=reject) # fit sources from epochs or from raw (both works for epochs) ica.decompose_epochs(epochs) # plot components for one epoch of interest # A distinct cardiac component should be visible ica.plot_sources_epochs(epochs, epoch_idx=13, n_components=25) ############################################################################### # Automatically find the ECG component using correlation with ECG signal # As we don't have an ECG channel we use one that correlates a lot with heart # beats: 'MEG 1531'. We can directly pass the name to the find_sources method. # In our example, the find_sources method returns and array of correlation # scores for each ICA source. ecg_scores = ica.find_sources_epochs(epochs, target='MEG 1531',
random_state=0) print ica # get epochs tmin, tmax, event_id = -0.2, 0.5, 1 # baseline = None baseline = (None, 0) reject = None events = mne.find_events(raw, stim_channel='STI 014') epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=baseline, preload=True, reject=reject) # fit sources from epochs or from raw (both works for epochs) ica.decompose_epochs(epochs) # plot components for one epoch of interest # A distinct cardiac component should be visible ica.plot_sources_epochs(epochs, epoch_idx=13, n_components=25) ############################################################################### # Automatically find the ECG component using correlation with ECG signal # As we don't have an ECG channel we use one that correlates a lot with heart # beats: 'MEG 1531'. We can directly pass the name to the find_sources method. # In our example, the find_sources method returns and array of correlation # scores for each ICA source. ecg_scores = ica.find_sources_epochs(epochs, target='MEG 1531', score_func='pearsonr')