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%matplotlib inline

Extracting and visualizing subject head movement

Continuous head movement can be encoded during MEG recordings by use of HPI coils that continuously emit sinusoidal signals. These signals can then be extracted from the recording and used to estimate head position as a function of time. Here we show an example of how to do this, and how to visualize the result.

HPI frequencies

First let's load a short bit of raw data where the subject intentionally moved their head during the recording. Its power spectral density shows five peaks (most clearly visible in the gradiometers) corresponding to the HPI coil frequencies, plus other peaks related to power line interference (60 Hz and harmonics).



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

from os import path as op

import mne

print(__doc__)

data_path = op.join(mne.datasets.testing.data_path(verbose=True), 'SSS')
fname_raw = op.join(data_path, 'test_move_anon_raw.fif')
raw = mne.io.read_raw_fif(fname_raw, allow_maxshield='yes').load_data()
raw.plot_psd()

Estimating continuous head position

First, let's extract the HPI coil amplitudes as a function of time:



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chpi_amplitudes = mne.chpi.compute_chpi_amplitudes(raw)

Second, let's compute time-varying HPI coil locations from these:



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chpi_locs = mne.chpi.compute_chpi_locs(raw.info, chpi_amplitudes)

Lastly, compute head positions from the coil locations:



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head_pos = mne.chpi.compute_head_pos(raw.info, chpi_locs, verbose=True)

Note that these can then be written to disk or read from disk with :func:mne.chpi.write_head_pos and :func:mne.chpi.read_head_pos, respectively.

Visualizing continuous head position

We can plot as traces, which is especially useful for long recordings:



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mne.viz.plot_head_positions(head_pos, mode='traces')

Or we can visualize them as a continuous field (with the vectors pointing in the head-upward direction):



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mne.viz.plot_head_positions(head_pos, mode='field')

These head positions can then be used with :func:mne.preprocessing.maxwell_filter to compensate for movement, or with :func:mne.preprocessing.annotate_movement to mark segments as bad that deviate too much from the average head position.