neurodsp.aperiodic.irasa.compute_irasa

neurodsp.aperiodic.irasa.compute_irasa(sig, fs, f_range=None, hset=None, thresh=None, **spectrum_kwargs)

Separate aperiodic and periodic components using IRASA.

Parameters

sig1d array

Time series.

fsfloat

The sampling frequency of sig.

f_rangetuple, optional

Frequency range to restrict the analysis to.

hset1d array, optional

Resampling factors used in IRASA calculation. If not provided, defaults to values from 1.1 to 1.9 with an increment of 0.05.

threshfloat, optional

A relative threshold to apply when separating out periodic components. The threshold is defined in terms of standard deviations of the original spectrum.

spectrum_kwargsdict

Optional keywords arguments that are passed to compute_spectrum.

Returns

freqs1d array

Frequency vector.

psd_aperiodic1d array

The aperiodic component of the power spectrum.

psd_periodic1d array

The periodic component of the power spectrum.

Notes

Irregular-Resampling Auto-Spectral Analysis (IRASA) is an algorithm ([1]) that aims to separate 1/f and periodic components by resampling time series and computing power spectra, averaging away any activity that is frequency specific to isolate the aperiodic component.

References

1

Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal. Brain Topography, 29(1), 13–26. DOI: https://doi.org/10.1007/s10548-015-0448-0

Examples

Apply IRASA to a simulated combined time series:

>>> from neurodsp.sim import sim_combined
>>> sig = sim_combined(n_seconds=10, fs=500,
...                    components={'sim_powerlaw': {}, 'sim_oscillation': {'freq': 10}})
>>> freqs, psd_aperiodic, psd_periodic = compute_irasa(sig, fs=500, f_range=[3, 50])