mne_features.univariate.compute_spect_edge_freq¶

mne_features.univariate.compute_spect_edge_freq(sfreq, data, ref_freq=None, edge=None, psd_method='welch', psd_params=None)¶

Spectal Edge Frequency (per channel).

Parameters

sfreqfloat

Sampling rate of the data.

datandarray, shape (n_channels, n_times)

ref_freqfloat or None (default: None)

If not None, reference frequency for the computation of the spectral edge frequency. If None, ref_freq = sfreq / 2 is used.

edgelist of float or None (default: None)

If not None, edge is expected to be a list of values between 0 and 1. If None, edge = [0.5] is used.

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 * n_edge,)

With: n_edge = 1 if edge is None or n_edge = len(edge) otherwise.

Notes

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

References

1

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