mne.time_frequency.cwt_morlet

mne.time_frequency.cwt_morlet(X, sfreq, freqs, use_fft=True, n_cycles=7.0, zero_mean=False)

Compute time freq decomposition with Morlet wavelets

This function operates directly on numpy arrays. Consider using tfr_morlet to process Epochs or Evoked instances.

Parameters:

X : array of shape [n_signals, n_times]

signals (one per line)

sfreq : float

sampling Frequency

freqs : array

Array of frequencies of interest

use_fft : bool

Compute convolution with FFT or temoral convolution.

n_cycles: float | array of float :

Number of cycles. Fixed number or one per frequency.

zero_mean : bool

Make sure the wavelets are zero mean.

Returns:

tfr : 3D array

Time Frequency Decompositions (n_signals x n_frequencies x n_times)

See also

tfr.cwt
Compute time-frequency decomposition with user-provided wavelets