mne.chpi.compute_chpi_snr#
- mne.chpi.compute_chpi_snr(raw, t_step_min=0.01, t_window='auto', ext_order=1, tmin=0, tmax=None, verbose=None)[source]#
Compute time-varying estimates of cHPI SNR.
- Parameters:
- rawinstance of
Raw
Raw data with cHPI information.
- t_step_min
float
Minimum time step to use.
- t_window
float
Time window to use to estimate the amplitudes, default is 0.2 (200 ms).
- ext_order
int
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=1
by default, which should improve detection of true HPI signals.- tmin
float
Start time of the raw data to use in seconds (must be >= 0).
- tmax
float
End time of the raw data to use in seconds (cannot exceed data duration).
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- rawinstance of
- Returns:
- chpi_snrs
dict
The time-varying cHPI SNR estimates, with entries “times”, “freqs”, “snr_mag”, “power_mag”, and “resid_mag” (and/or “snr_grad”, “power_grad”, and “resid_grad”, depending on which channel types are present in
raw
).
- chpi_snrs
Notes
New in version 0.24.
Examples using mne.chpi.compute_chpi_snr
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Extracting and visualizing subject head movement