mne_features.univariate.compute_spect_entropy¶

mne_features.univariate.compute_spect_entropy(sfreq, data, psd_method='welch', psd_params=None)¶

Spectral Entropy (per channel).

Spectral Entropy is defined to be the Shannon Entropy of the Power Spectrum of the data.

Parameters

sfreqfloat

Sampling rate of the data

datandarray, shape (n_channels, n_times)

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: spect_entropy. See [1].

References

1

Inouye, T. et al. (1991). Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalography and clinical neurophysiology, 79(3), 204-210.