Visualize channel over epochs as an image¶
This will produce what is sometimes called an event related potential / field (ERP/ERF) image.
Two images are produced, one with a good channel and one with a channel that does not show any evoked field.
It is also demonstrated how to reorder the epochs using a 1D spectral embedding as described in 1.
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' event_id, tmin, tmax = 1, -0.2, 0.4 # Setup for reading the raw data raw = io.read_raw_fif(raw_fname) events = mne.read_events(event_fname) # Set up pick list: EEG + MEG - bad channels (modify to your needs) raw.info['bads'] = ['MEG 2443', 'EEG 053'] # Create epochs, here for gradiometers + EOG only for simplicity epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=('grad', 'eog'), baseline=(None, 0), preload=True, reject=dict(grad=4000e-13, eog=150e-6))
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Range : 6450 ... 48149 = 42.956 ... 320.665 secs Ready. Current compensation grade : 0 72 matching events found Applying baseline correction (mode: mean) Not setting metadata 4 projection items activated Loading data for 72 events and 91 original time points ... Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] 9 bad epochs dropped
Show event-related fields images
# and order with spectral reordering # If you don't have scikit-learn installed set order_func to None from sklearn.manifold import spectral_embedding # noqa from sklearn.metrics.pairwise import rbf_kernel # noqa def order_func(times, data): this_data = data[:, (times > 0.0) & (times < 0.350)] this_data /= np.sqrt(np.sum(this_data ** 2, axis=1))[:, np.newaxis] return np.argsort(spectral_embedding(rbf_kernel(this_data, gamma=1.), n_components=1, random_state=0).ravel()) good_pick = 97 # channel with a clear evoked response bad_pick = 98 # channel with no evoked response # We'll also plot a sample time onset for each trial plt_times = np.linspace(0, .2, len(epochs)) plt.close('all') mne.viz.plot_epochs_image(epochs, [good_pick, bad_pick], sigma=.5, order=order_func, vmin=-250, vmax=250, overlay_times=plt_times, show=True)
63 matching events found No baseline correction applied Not setting metadata 0 projection items activated 0 bad epochs dropped 63 matching events found No baseline correction applied Not setting metadata 0 projection items activated 0 bad epochs dropped
Graph-based variability estimation in single-trial event-related neural responses. A. Gramfort, R. Keriven, M. Clerc, 2010, Biomedical Engineering, IEEE Trans. on, vol. 57 (5), 1051-1061 https://ieeexplore.ieee.org/document/5406156
Total running time of the script: ( 0 minutes 1.954 seconds)
Estimated memory usage: 11 MB