Visualize channel over epochs as an image¶

This will produce what is sometimes called an event related potential / field (ERP/ERF) image.

2 images are produced. One with a good channel and one with a channel that does not see any evoked field.

It is also demonstrated how to reorder the epochs using a 1d spectral embedding as described in:

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://hal.inria.fr/inria-00497023

# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne import io
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()

Set parameters

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, 0.5

# 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']
picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=True, eog=True,
                       exclude='bads')

# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                    picks=picks, baseline=(None, 0), preload=True,
                    reject=dict(grad=4000e-13, eog=150e-6))

Out:

Opening raw data file /home/ubuntu/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
4 projection items activated
Loading data for 72 events and 106 original time points ...
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
    Rejecting  epoch based on EOG : [u'EOG 061']
16 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.cluster.spectral 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=0.5, vmin=-100,
                          vmax=250, colorbar=True, order=order_func,
                          overlay_times=plt_times, show=True)

Total running time of the script: ( 0 minutes 1.453 seconds)

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