mne.chpi.compute_head_pos#

mne.chpi.compute_head_pos(info, chpi_locs, dist_limit=0.005, gof_limit=0.98, adjust_dig=False, *, weighted=None, verbose=None)[source]#

Compute time-varying head positions.

Parameters:

infomne.Info: The mne.Info object with information about the sensors and methods of measurement.
chpi_locsdict: The time-varying cHPI coils locations, with entries “times”, “rrs”, “moments”, and “gofs”. Typically obtained by compute_chpi_locs() or extract_chpi_locs_ctf().
dist_limitfloat: Minimum distance (m) to accept for coil position fitting.
gof_limitfloat: Minimum goodness of fit to accept for each coil.
adjust_digbool: If True, adjust the digitization locations used for fitting based on the positions localized at the start of the file.
weightedbool: If True, fit all coils that pass the gof_limit and dist_limit criteria simultaneously, weighting each coil by its goodness of fit and its inter-coil distance error. If False, subselect the three coils that yield the best fit. Weighting avoids discontinuous jumps in the estimated head position caused by coils switching in and out of the fit (see #11330). The default (False) will change to True in 1.14.

New in v1.13.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

quatsndarray, shape (n_pos, 10): MaxFilter-formatted head position parameters. The columns correspond to [t, q1, q2, q3, x, y, z, gof, err, v] for each time point.

Examples using `mne.chpi.compute_head_pos`#

Annotate movement artifacts and reestimate dev_head_t

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering

mne.chpi.compute_head_pos#

Examples using mne.chpi.compute_head_pos#

Examples using `mne.chpi.compute_head_pos`#