neurodsp.aperiodic.conversions.convert_exponent_hurst¶
- neurodsp.aperiodic.conversions.convert_exponent_hurst(exponent, fractional_class)[source]¶
Convert a powerlaw exponent to the expected Hurst exponent value.
- Parameters
- exponentfloat
Aperiodic exponent value, representing a 1/f distribution.
- fractional_class{‘gaussian’, ‘brownian’}
The class of input data that the given exponent value relates to. This can be either ‘fractional Gaussian noise’ or ‘fractional Brownian motion.’ This is required as the conversion differs between the two classes.
- Returns
- hurstfloat
Predicted Hurst exponent for the given exponent value.
References
- 1
Schaefer, A., Brach, J. S., Perera, S., & Sejdić, E. (2014). A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series. Journal of Neuroscience Methods, 222, 118–130. https://doi.org/10.1016/j.jneumeth.2013.10.017
Examples
Convert a powerlaw exponent to the expected hurst exponent, for fractional Gaussian noise:
>>> convert_exponent_hurst(-1, 'gaussian') 1.0
Convert a powerlaw exponent to the expected hurst exponent, for fractional Brownian motion:
>>> convert_exponent_hurst(-1, 'brownian') 0.0