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"""
.. _ex-epochs-image:
=========================================
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 :footcite:`GramfortEtAl2010`.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
# %%
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
# %%
# Set parameters
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
event_fname = meg_path / "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),
)
# %%
# 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=1.0), 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, 0.2, len(epochs))
plt.close("all")
mne.viz.plot_epochs_image(
epochs,
[good_pick, bad_pick],
sigma=0.5,
order=order_func,
vmin=-250,
vmax=250,
overlay_times=plt_times,
show=True,
)
# %%
# References
# ----------
# .. footbibliography::
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