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


LEGACY: New code should use .compute_tfr(method=”morlet”).

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

Same computation as tfr_array_morlet, but operates on Epochs or Evoked objects instead of NumPy arrays.

instEpochs | Evoked

The epochs or evoked object.

freqsndarray, shape (n_freqs,)

The frequencies in Hz.

n_cyclesint | array of int, shape (n_freqs,)

Number of cycles in the wavelet, either a fixed number or one per frequency. The number of cycles n_cycles and the frequencies of interest freqs define the temporal window length. See notes for additional information about the relationship between those arguments and about time and frequency smoothing.

use_fftbool, default False

The fft based convolution or not.

return_itcbool, default True

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

decimint | slice

Decimation factor, applied after time-frequency decomposition.

  • if int, returns tfr[..., ::decim] (keep only every Nth sample along the time axis).

  • if slice, returns tfr[..., decim] (keep only the specified slice along the time axis).


Decimation is done after convolutions and may create aliasing artifacts.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

picksarray_like of int | None, default None

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

zero_meanbool, default True

Make sure the wavelet has a mean of zero.

New in v0.13.0.

averagebool, default True

If False return an EpochsTFR containing separate TFRs for each epoch. If True return an AverageTFR containing the average of all TFRs across epochs.


Using average=True is functionally equivalent to using average=False followed by EpochsTFR.average(), but is more memory efficient.

New in v0.13.0.


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

New in v0.15.0.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

powerAverageTFR | EpochsTFR

The averaged or single-trial power.

itcAverageTFR | EpochsTFR

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


The Morlet wavelets follow the formulation in Tallon-Baudry et al.[1].

In spectrotemporal analysis (as with traditional fourier methods), the temporal and spectral resolution are interrelated: longer temporal windows allow more precise frequency estimates; shorter temporal windows “smear” frequency estimates while providing more precise timing information.

Time-frequency representations are computed using a sliding temporal window. Either the temporal window has a fixed length independent of frequency, or the temporal window decreases in length with increased frequency.

Figure: Time and frequency smoothing. (a) For a fixed length temporal window the time and frequency smoothing remains fixed. (b) For temporal windows that decrease with frequency, the temporal smoothing decreases and the frequency smoothing increases with frequency. Source: FieldTrip tutorial: Time-frequency analysis using Hanning window, multitapers and wavelets.

In MNE-Python, the length of the Morlet wavelet is affected by the arguments freqs and n_cycles, which define the frequencies of interest and the number of cycles, respectively. For the time-frequency representation, the length of the wavelet is defined such that both tails of the wavelet extend five standard deviations from the midpoint of its Gaussian envelope and that there is a sample at time zero.

The length of the wavelet is thus \(10\times\mathtt{sfreq}\cdot\sigma-1\), which is equal to \(\frac{5}{\pi} \cdot \frac{\mathtt{n\_cycles} \cdot \mathtt{sfreq}}{\mathtt{freqs}} - 1\), where \(\sigma = \frac{\mathtt{n\_cycles}}{2\pi f}\) corresponds to the standard deviation of the wavelet’s Gaussian envelope. Note that the length of the wavelet must not exceed the length of your signal.

For more information on the Morlet wavelet, see mne.time_frequency.morlet().

See mne.time_frequency.morlet() for more information about the Morlet wavelet.


Examples using mne.time_frequency.tfr_morlet#

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)