mne_features.univariate.compute_energy_freq_bands¶

mne_features.univariate.compute_energy_freq_bands(sfreq, data, freq_bands=array([0.5, 4., 8., 13., 30., 100.]), deriv_filt=True)¶

Band energy (per channel).

Parameters

sfreqfloat

Sampling rate of the data.

datandarray, shape (n_channels, n_times)

freq_bandsndarray or dict (default: np.array([.5, 4, 8, 13, 30, 100]))

The parameter freq_bands should be either a ndarray with shape (n_freq_bands + 1,) or (n_freq_bands, 2) or a dict. If ndarray with shape (n_freq_bands + 1,), the entries define contiguous frequency bands as follows: the i-th frequency band is defined as: [freq_bands[i], freq_bands[i + 1]] (0 <= i <= n_freq_bands - 1). If ndarray with shape (n_freq_bands, 2), the rows of freq_bands define non-contiguous frequency bands. If dict, the keys should be strings (names of the frequency bands) and the values, the corresponding bands (as ndarray with shape (2,) or list of length 2). When freq_bands is of type dict, the keys are used to generate the feature names (only used when features are extracted with return_as_df=True). The values of freq_bands should be between 0 and sfreq / 2 (the Nyquist frequency) as the function uses the one-sided PSD.

deriv_filtbool (default: False)

If True, a derivative filter is applied to the input data before filtering (see Notes).

Returns

outputndarray, shape (n_channels * (n_freqs - 1),)

Notes

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

References

1

Kharbouch, A. et al. (2011). An algorithm for seizure onset detection using intracranial EEG. Epilepsy & Behavior, 22, S29-S35.