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:

# 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')

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'))

Out:

Successfully extracted to: [u'/home/ubuntu/mne_data/MNE-misc-data']
Opening raw data file /home/ubuntu/mne_data/MNE-misc-data/movement/simulated_movement_raw.fif...
    Range : 25800 ... 34208 =     42.956 ...    56.955 secs
Ready.
Current compensation grade : 0
Opening raw data file /home/ubuntu/mne_data/MNE-misc-data/movement/simulated_stationary_raw.fif...
    Range : 25800 ... 34208 =     42.956 ...    56.955 secs
Ready.
Current compensation grade : 0

Visualize the “subject” head movements (traces)

mne.viz.plot_head_positions(pos, mode='traces')
../../_images/sphx_glr_plot_movement_compensation_001.png

Out:

viridis is unavailable on matplotlib < 1.4, using "YlGnBu_r"

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', vmin=-500, vmax=500)

# 0. Take average of stationary data (bilateral auditory patterns)
evoked_stat = mne.Epochs(raw_stat, events, 1, -0.2, 0.8).average()
evoked_stat.plot_topomap(title='Stationary', **topo_kwargs)

# 1. Take a naive average (smears activity)
evoked = mne.Epochs(raw, events, 1, -0.2, 0.8).average()
evoked.plot_topomap(title='Moving: naive average', **topo_kwargs)

# 2. Use raw movement compensation (restores pattern)
raw_sss = maxwell_filter(raw, head_pos=pos)
evoked_raw_mc = mne.Epochs(raw_sss, events, 1, -0.2, 0.8).average()
evoked_raw_mc.plot_topomap(title='Moving: movement compensated', **topo_kwargs)
  • ../../_images/sphx_glr_plot_movement_compensation_002.png
  • ../../_images/sphx_glr_plot_movement_compensation_003.png
  • ../../_images/sphx_glr_plot_movement_compensation_004.png
  • ../../_images/sphx_glr_plot_movement_compensation_005.png

Out:

14 events found
Events id: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14]
14 matching events found
0 projection items activated
14 matching events found
0 projection items activated
Maxwell filtering raw data
    Appending head position result channels and loading raw data from disk
    No bad MEG channels
    Processing 203 gradiometers and 102 magnetometers
    Automatic origin fit: head of radius 91.0 mm
    Using origin -4.1, 16.0, 51.7 mm in the head frame
        Using 90/95 harmonic components for    0.000  (75/80 in, 15/15 out)
    Processing 1 data chunks of (at least) 10.0 sec
        Using 87/95 harmonic components for    0.000  (72/80 in, 15/15 out)
        Using 88/95 harmonic components for    1.001  (73/80 in, 15/15 out)
        Using 90/95 harmonic components for    2.000  (75/80 in, 15/15 out)
        Using 88/95 harmonic components for    3.000  (73/80 in, 15/15 out)
        Using 88/95 harmonic components for    3.999  (73/80 in, 15/15 out)
        Using 88/95 harmonic components for    5.000  (73/80 in, 15/15 out)
        Using 89/95 harmonic components for    6.001  (74/80 in, 15/15 out)
        Using 93/95 harmonic components for    6.999  (78/80 in, 15/15 out)
        Using 88/95 harmonic components for    8.000  (73/80 in, 15/15 out)
        Using 91/95 harmonic components for    9.001  (76/80 in, 15/15 out)
        Using 93/95 harmonic components for   10.000  (78/80 in, 15/15 out)
        Using 93/95 harmonic components for   11.000  (78/80 in, 15/15 out)
        Using 89/95 harmonic components for   11.999  (74/80 in, 15/15 out)
        Using 88/95 harmonic components for   13.000  (73/80 in, 15/15 out)
        Used  14 head positions for    0.000 -   13.999 sec (#1/1)
[done]
14 matching events found
0 projection items activated

Total running time of the script: ( 0 minutes 6.336 seconds)

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