mne_features.univariate.compute_wavelet_coef_energy¶

mne_features.univariate.compute_wavelet_coef_energy(data, wavelet_name='db4')¶

Energy of Wavelet decomposition coefficients (per channel).

Parameters

datandarray, shape (n_channels, n_times)

wavelet_namestr (default: db4)

Wavelet name (to be used with pywt.Wavelet). The full list of Wavelet names are given by: [name for family in pywt.families() for name in pywt.wavelist(family)].

Returns

outputndarray, shape (n_channels * levdec,)

The decomposition level (levdec) used for the DWT is either 6 or the maximum useful decomposition level (given the number of time points in the data and chosen wavelet ; see pywt.dwt_max_level).

Notes

Alias of the feature function: wavelet_coef_energy. See [1].

References

1

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.