neurodsp.aperiodic.conversions.convert_hurst_exponent

neurodsp.aperiodic.conversions.convert_hurst_exponent(hurst, fractional_class)

Convert a Hurst exponent value to the expected powerlaw exponent.

Parameters

hurstfloat

Hurst exponent value.

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

exponentfloat

Predicted aperiodic exponent value, representing a 1/f distribution.

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 Hurst exponent value to the expected powerlaw exponent, for fractional Gaussian noise:

>>> convert_hurst_exponent(1, 'gaussian')
-1.0

Convert a Hurst exponent value to the expected powerlaw exponent, for fractional Brownian noise:

>>> convert_hurst_exponent(0, 'brownian')
-1.0