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%matplotlib inline

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



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# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

from os import path as op

import mne
from mne.preprocessing import maxwell_filter

print(__doc__)

data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement')

head_pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_quats.pos'))
raw = mne.io.read_raw_fif(op.join(data_path, 'simulated_movement_raw.fif'))
raw_stat = mne.io.read_raw_fif(op.join(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.



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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.



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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).



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# 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', vmin=-500, vmax=500,
                   time_unit='s')

First, take the average of stationary data (bilateral auditory patterns).



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evoked_stat = mne.Epochs(raw_stat, events, 1, -0.2, 0.8).average()
evoked_stat.plot_topomap(title='Stationary', **topo_kwargs)

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.



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epochs = mne.Epochs(raw, events, 1, -0.2, 0.8)
evoked = epochs.average()
evoked.plot_topomap(title='Moving: naive average', **topo_kwargs)

Third, use raw movement compensation (restores pattern).



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raw_sss = maxwell_filter(raw, head_pos=head_pos)
evoked_raw_mc = mne.Epochs(raw_sss, events, 1, -0.2, 0.8).average()
evoked_raw_mc.plot_topomap(title='Moving: movement compensated (raw)',
                           **topo_kwargs)

Fourth, use evoked movement compensation. For these data, which contain very large rotations, it does not as cleanly restore the pattern.



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evoked_evo_mc = mne.epochs.average_movements(epochs, head_pos=head_pos)
evoked_evo_mc.plot_topomap(title='Moving: movement compensated (evoked)',
                           **topo_kwargs)