- mne.chpi.compute_chpi_amplitudes(raw, t_step_min=0.01, t_window='auto', ext_order=1, tmin=0, tmax=None, verbose=None)[source]#
Compute time-varying cHPI amplitudes.
- rawinstance of
Raw data with cHPI information.
Minimum time step to use.
Time window to use to estimate the amplitudes, default is 0.2 (200 ms).
The external order for SSS-like interfence suppression. The SSS bases are used as projection vectors during fitting.
Changed in version 0.20: Added
ext_order=1by default, which should improve detection of true HPI signals.
Start time of the raw data to use in seconds (must be >= 0).
End time of the raw data to use in seconds (cannot exceed data duration).
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.
- rawinstance of
The time-varying cHPI coil amplitudes, with entries “times”, “proj”, and “slopes”.
This function will:
Get HPI frequencies, HPI status channel, HPI status bits, and digitization order using
Window data using
t_window(half before and half after
Use a linear model (DC + linear slope + sin + cos terms) to fit sinusoidal amplitudes to MEG channels. It uses SVD to determine the phase/amplitude of the sinusoids.
In “auto” mode,
t_windowwill be set to the longer of:
- Five cycles of the lowest HPI or line frequency.
Ensures that the frequency estimate is stable.
- The reciprocal of the smallest difference between HPI and line freqs.
Ensures that neighboring frequencies can be disambiguated.
The output is meant to be used with
New in version 0.20.
Extracting and visualizing subject head movement