mne_features.univariate.compute_hjorth_complexity_spect¶

mne_features.univariate.compute_hjorth_complexity_spect(sfreq, data, normalize=False, psd_method='welch', psd_params=None)¶

Hjorth complexity (per channel).

Hjorth complexity parameter computed from the Power Spectrum of the data.

Parameters

sfreqfloat

Sampling rate of the data.

datandarray, shape (n_channels, n_times)

normalizebool (default: False)

Normalize the result by the total power.

psd_methodstr (default: ‘welch’)

Method used for the estimation of the Power Spectral Density (PSD). Valid methods are: 'welch', 'multitaper' or 'fft'.

psd_paramsdict or None (default: None)

If not None, dict with optional parameters (welch_n_fft, welch_n_per_seg, welch_n_overlap) to be passed to mne_features.utils.power_spectrum(). If None, default parameters are used (see doc for mne_features.utils.power_spectrum()).

Returns

outputndarray, shape (n_channels,)

Notes

Alias of the feature function: hjorth_complexity_spect. See [1] and [2].

References

1

Mormann, F. et al. (2006). Seizure prediction: the long and winding road. Brain, 130(2), 314-333.

2

Teixeira, C. A. et al. (2011). EPILAB: A software package for studies on the prediction of epileptic seizures. Journal of Neuroscience Methods, 200(2), 257-271.