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# -*- coding: utf-8 -*-
"""
.. _ex-movement-comp:
==============================================
Maxwell filter data with movement compensation
==============================================
Demonstrate movement compensation on simulated data. The simulated data
contains bilateral activation of auditory cortices, repeated over 14
different head rotations (head center held fixed). See the following for
details:
https://github.com/mne-tools/mne-misc-data/blob/master/movement/simulate.py
"""
# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
# %%
import mne
from mne.preprocessing import maxwell_filter
print(__doc__)
data_path = mne.datasets.misc.data_path(verbose=True) / 'movement'
head_pos = mne.chpi.read_head_pos(data_path / 'simulated_quats.pos')
raw = mne.io.read_raw_fif(data_path / 'simulated_movement_raw.fif')
raw_stat = mne.io.read_raw_fif(data_path / 'simulated_stationary_raw.fif')
# %%
# Visualize the "subject" head movements. By providing the measurement
# information, the distance to the nearest sensor in each direction
# (e.g., left/right for the X direction, forward/backward for Y) can
# be shown in blue, and the destination (if given) shown in red.
mne.viz.plot_head_positions(
head_pos, mode='traces', destination=raw.info['dev_head_t'], info=raw.info)
# %%
# This can also be visualized using a quiver.
mne.viz.plot_head_positions(
head_pos, mode='field', destination=raw.info['dev_head_t'], info=raw.info)
# %%
# Process our simulated raw data (taking into account head movements).
# extract our resulting events
events = mne.find_events(raw, stim_channel='STI 014')
events[:, 2] = 1
raw.plot(events=events)
topo_kwargs = dict(times=[0, 0.1, 0.2], ch_type='mag', vlim=(-500, 500))
# %%
# First, take the average of stationary data (bilateral auditory patterns).
evoked_stat = mne.Epochs(raw_stat, events, 1, -0.2, 0.8).average()
fig = evoked_stat.plot_topomap(**topo_kwargs)
fig.suptitle('Stationary')
# %%
# Second, take a naive average, which averages across epochs that have been
# simulated to have different head positions and orientations, thereby
# spatially smearing the activity.
epochs = mne.Epochs(raw, events, 1, -0.2, 0.8)
evoked = epochs.average()
fig = evoked.plot_topomap(**topo_kwargs)
fig.suptitle('Moving: naive average')
# %%
# Third, use raw movement compensation (restores pattern).
raw_sss = maxwell_filter(raw, head_pos=head_pos)
evoked_raw_mc = mne.Epochs(raw_sss, events, 1, -0.2, 0.8).average()
fig = evoked_raw_mc.plot_topomap(**topo_kwargs)
fig.suptitle('Moving: movement compensated (raw)')
# %%
# Fourth, use evoked movement compensation. For these data, which contain
# very large rotations, it does not as cleanly restore the pattern.
evoked_evo_mc = mne.epochs.average_movements(epochs, head_pos=head_pos)
fig = evoked_evo_mc.plot_topomap(**topo_kwargs)
fig.suptitle('Moving: movement compensated (evoked)')
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