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"""
=================================================
Compare evoked responses for different conditions
=================================================
In this example, an Epochs object for visual and auditory responses is created.
Both conditions are then accessed by their respective names to create a sensor
layout plot of the related evoked responses.
"""
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.viz import plot_evoked_topo
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 = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)
# Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
include = [] # or stim channels ['STI 014']
# bad channels in raw.info['bads'] will be automatically excluded
# Set up amplitude-peak rejection values for MEG channels
reject = dict(grad=4000e-13, mag=4e-12)
# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
# Create epochs including different events
event_id = {'audio/left': 1, 'audio/right': 2,
'visual/left': 3, 'visual/right': 4}
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
picks=picks, baseline=(None, 0), reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in ('left', 'right')]
###############################################################################
# Show topography for two different conditions
colors = 'blue', 'red'
title = 'MNE sample data\nleft vs right (A/V combined)'
plot_evoked_topo(evokeds, color=colors, title=title, background_color='w')
plt.show()
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