mne.time_frequency.tfr.morlet(sfreq, freqs, n_cycles=7.0, sigma=None, zero_mean=False)

Compute Morlet wavelets for the given frequency range.


sfreq : float

The sampling Frequency.

freqs : array

frequency range of interest (1 x Frequencies)

n_cycles: float | array of float, defaults to 7.0

Number of cycles. Fixed number or one per frequency.

sigma : float, defaults to None

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, defaults to False

Make sure the wavelet has a mean of zero.


Ws : list of array

The wavelets time series.