mne.time_frequency.tfr_morlet(inst, freqs, n_cycles, use_fft=False, return_itc=True, decim=1, n_jobs=1, picks=None, zero_mean=True, average=True, output='power', verbose=None)[source]

Compute Time-Frequency Representation (TFR) using Morlet wavelets.

inst : Epochs | Evoked

The epochs or evoked object.

freqs : ndarray, shape (n_freqs,)

The frequencies in Hz.

n_cycles : float | ndarray, shape (n_freqs,)

The number of cycles globally or for each frequency.

use_fft : bool, defaults to False

The fft based convolution or not.

return_itc : bool, defaults to True

Return inter-trial coherence (ITC) as well as averaged power. Must be False for evoked data.

decim : int | slice, defaults to 1

To reduce memory usage, decimation factor after time-frequency decomposition. If int, returns tfr[…, ::decim]. If slice, returns tfr[…, decim].


Decimation may create aliasing artifacts.

n_jobs : int, defaults to 1

The number of jobs to run in parallel.

picks : array-like of int | None, defaults to None

The indices of the channels to decompose. If None, all available good data channels are decomposed.

zero_mean : bool, defaults to True

Make sure the wavelet has a mean of zero.

New in version 0.13.0.

average : bool, defaults to True

If True average across Epochs.

New in version 0.13.0.

output : str

Can be “power” (default) or “complex”. If “complex”, then average must be False.

New in version 0.15.0.

verbose : bool, str, int, or None, defaults to None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

power : AverageTFR | EpochsTFR

The averaged or single-trial power.

itc : AverageTFR | EpochsTFR

The inter-trial coherence (ITC). Only returned if return_itc is True.