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:
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')
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)
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
Computing regularization
Using 90/95 harmonic components for 0.000 (75/80 in, 15/15 out)
Processing 1 data chunks of (at least) 10.0 sec
Computing regularization
Using 87/95 harmonic components for 0.000 (72/80 in, 15/15 out)
Computing regularization
Using 88/95 harmonic components for 1.001 (73/80 in, 15/15 out)
Computing regularization
Using 90/95 harmonic components for 2.000 (75/80 in, 15/15 out)
Computing regularization
Using 88/95 harmonic components for 3.000 (73/80 in, 15/15 out)
Computing regularization
Using 88/95 harmonic components for 3.999 (73/80 in, 15/15 out)
Computing regularization
Using 88/95 harmonic components for 5.000 (73/80 in, 15/15 out)
Computing regularization
Using 89/95 harmonic components for 6.001 (74/80 in, 15/15 out)
Computing regularization
Using 93/95 harmonic components for 6.999 (78/80 in, 15/15 out)
Computing regularization
Using 88/95 harmonic components for 8.000 (73/80 in, 15/15 out)
Computing regularization
Using 91/95 harmonic components for 9.001 (76/80 in, 15/15 out)
Computing regularization
Using 93/95 harmonic components for 10.000 (78/80 in, 15/15 out)
Computing regularization
Using 93/95 harmonic components for 11.000 (78/80 in, 15/15 out)
Computing regularization
Using 89/95 harmonic components for 11.999 (74/80 in, 15/15 out)
Computing regularization
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 9.119 seconds)