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"""
========================================================
Extract epochs, average and save evoked response to disk
========================================================
This script shows how to read the epochs from a raw file given
a list of events. The epochs are averaged to produce evoked
data and then saved to disk.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
print(__doc__)
import mne
from mne import io
from mne.datasets import sample
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.Raw(raw_fname)
events = mne.read_events(event_fname)
# Plot raw data
fig = raw.plot(events=events)
# Set up pick list: EEG + STI 014 - bad channels (modify to your needs)
include = [] # or stim channels ['STI 014']
raw.info['bads'] += ['EEG 053'] # bads + 1 more
# pick EEG channels
picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=True,
include=include, exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(eeg=80e-6, eog=150e-6),
preload=True)
epochs.plot()
evoked = epochs.average() # average epochs and get an Evoked dataset.
evoked.save('sample_audvis_eeg-ave.fif') # save evoked data to disk
###############################################################################
# View evoked response
times = 1e3 * epochs.times # time in miliseconds
ch_max_name, latency = evoked.get_peak(mode='neg')
import matplotlib.pyplot as plt
evoked.plot()
plt.xlim([times[0], times[-1]])
plt.xlabel('time (ms)')
plt.ylabel('Potential (uV)')
plt.title('EEG evoked potential')
plt.axvline(latency * 1e3, color='red',
label=ch_max_name, linewidth=2,
linestyle='--')
plt.legend(loc='best')
plt.show()
# Look at channels that caused dropped events, showing that the subject's
# blinks were likely to blame for most epochs being dropped
epochs.drop_bad_epochs()
epochs.plot_drop_log(subject='sample')
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