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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)
print(__doc__)
import matplotlib.pyplot as plt
import mne
from mne.io import Raw
from mne.viz import plot_topo
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 = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = Raw(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
epochs = mne.Epochs(raw, events, dict(audio_l=1, visual_r=3), tmin, tmax,
picks=picks, baseline=(None, 0), reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in 'audio_l', 'visual_r']
###############################################################################
# Show topography for two different conditions
colors = 'yellow', 'green'
title = 'MNE sample data - left auditory and visual'
plot_topo(evokeds, color=colors, title=title)
conditions = [e.comment for e in evokeds]
for cond, col, pos in zip(conditions, colors, (0.025, 0.07)):
plt.figtext(0.775, pos, cond, color=col, fontsize=12)
plt.show()
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