mne.time_frequency.tfr.morlet(sfreq, freqs, n_cycles=7, sigma=None, zero_mean=False)

Compute Wavelets for the given frequency range


sfreq : float

Sampling Frequency

freqs : array

frequency range of interest (1 x Frequencies)

n_cycles: float | array of float :

Number of cycles. Fixed number or one per frequency.

sigma : float, (optional)

It controls the width of the wavelet ie its temporal resolution. If sigma is None the temporal resolution is adapted with the frequency like for all wavelet transform. The higher the frequency the shorter is the wavelet. If sigma is fixed the temporal resolution is fixed like for the short time Fourier transform and the number of oscillations increases with the frequency.

zero_mean : bool

Make sure the wavelet is zero mean


Ws : list of array

Wavelets time series

See also

Compute time-frequency decomposition with Morlet wavelets