mne.chpi.compute_chpi_locs

mne.chpi.compute_chpi_locs(info, chpi_amplitudes, t_step_max=1.0, too_close='raise', adjust_dig=False, verbose=None)

Compute locations of each cHPI coils over time.

Parameters

infoinstance of Info

The measurement information.

chpi_amplitudesdict

The time-varying cHPI coil amplitudes, with entries “times”, “proj”, and “slopes”. Typically obtained by mne.chpi.compute_chpi_amplitudes().

t_step_maxfloat

Maximum time step to use.

too_closestr

How to handle HPI positions too close to the sensors, can be ‘raise’ (default), ‘warning’, or ‘info’.

adjust_digbool

If True, adjust the digitization locations used for fitting based on the positions localized at the start of the file.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

chpi_locsdict

The time-varying cHPI coils locations, with entries “times”, “rrs”, “moments”, and “gofs”.

See also

compute_chpi_amplitudes

compute_head_pos

read_head_pos

write_head_pos

extract_chpi_locs_ctf

Notes

This function is designed to take the output of mne.chpi.compute_chpi_amplitudes() and:

1. Get HPI coil locations (as digitized in info['dig']) in head coords.

2. If the amplitudes are 98% correlated with last position (and Δt < t_step_max), skip fitting.

3. Fit magnetic dipoles using the amplitudes for each coil frequency.

The number of fitted points n_pos will depend on the velocity of head movements as well as t_step_max (and t_step_min from mne.chpi.compute_chpi_amplitudes()).

New in version 0.20.

Examples using mne.chpi.compute_chpi_locs

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering