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