mne.time_frequency.tfr_multitaper(inst, freqs, n_cycles, time_bandwidth=4.0, use_fft=True, return_itc=True, decim=1, n_jobs=None, picks=None, average=True, *, verbose=None)[source]#


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

Compute Time-Frequency Representation (TFR) using DPSS tapers.

Same computation as tfr_array_multitaper(), 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.

time_bandwidthfloat 2.0

Product between the temporal window length (in seconds) and the full frequency bandwidth (in Hz). This product can be seen as the surface of the window on the time/frequency plane and controls the frequency bandwidth (thus the frequency resolution) and the number of good tapers. See notes for additional information.

use_fftbool, default True

The fft based convolution or not.

return_itcbool, default True

Return inter-trial coherence (ITC) as well as averaged (or single-trial) power.

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.

picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

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.

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.


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 multitaper temporal window length is defined by the arguments freqs and n_cycles, respectively defining the frequencies of interest and the number of cycles: \(T = \frac{\mathtt{n\_cycles}}{\mathtt{freqs}}\)

A fixed number of cycles for all frequencies will yield a temporal window which decreases with frequency. For example, freqs=np.arange(1, 6, 2) and n_cycles=2 yields T=array([2., 0.7, 0.4]).

To use a temporal window with fixed length, the number of cycles has to be defined based on the frequency. For example, freqs=np.arange(1, 6, 2) and n_cycles=freqs / 2 yields T=array([0.5, 0.5, 0.5]).

In MNE-Python’s multitaper functions, the frequency bandwidth is additionally affected by the parameter time_bandwidth. The n_cycles parameter determines the temporal window length based on the frequencies of interest: \(T = \frac{\mathtt{n\_cycles}}{\mathtt{freqs}}\). The time_bandwidth parameter defines the “time-bandwidth product”, which is the product of the temporal window length (in seconds) and the frequency bandwidth (in Hz). Thus once n_cycles has been set, frequency bandwidth is determined by \(\frac{\mathrm{time~bandwidth}}{\mathrm{time~window}}\), and thus passing a larger time_bandwidth value will increase the frequency bandwidth (thereby decreasing the frequency resolution).

The increased frequency bandwidth is reached by averaging spectral estimates obtained from multiple tapers. Thus, time_bandwidth also determines the number of tapers used. MNE-Python uses only “good” tapers (tapers with minimal leakage from far-away frequencies); the number of good tapers is floor(time_bandwidth - 1). This means there is another trade-off at play, between frequency resolution and the variance reduction that multitaper analysis provides. Striving for finer frequency resolution (by setting time_bandwidth low) means fewer tapers will be used, which undermines what is unique about multitaper methods — namely their ability to improve accuracy / reduce noise in the power estimates by using several (orthogonal) tapers.


In tfr_array_multitaper and tfr_multitaper, time_bandwidth defines the product of the temporal window length with the full frequency bandwidth For example, a full bandwidth of 4 Hz at a frequency of interest of 10 Hz will “smear” the frequency estimate between 8 Hz and 12 Hz.

This is not the case for psd_array_multitaper where the argument bandwidth defines the half frequency bandwidth. In the example above, the half-frequency bandwidth is 2 Hz.

New in v0.9.0.

Examples using mne.time_frequency.tfr_multitaper#

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

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