mne.time_frequency.tfr_multitaper(inst, freqs, n_cycles, time_bandwidth=4.0, use_fft=True, return_itc=True, decim=1, n_jobs=1, picks=None, verbose=None)

Compute Time-Frequency Representation (TFR) using DPSS 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. The time-window length is thus T = n_cycles / freq.

time_bandwidth : float, (optional)

Time x (Full) Bandwidth product. Should be >= 2.0. Choose this along with n_cycles to get desired frequency resolution. The number of good tapers (least leakage from far away frequencies) is chosen automatically based on this to floor(time_bandwidth - 1). Default is 4.0 (3 good tapers). E.g., With freq = 20 Hz and n_cycles = 10, we get time = 0.5 s. If time_bandwidth = 4., then frequency smoothing is (4 / time) = 8 Hz.

use_fft : bool

The fft based convolution or not. Defaults to True.

return_itc : bool

Return intertrial coherence (ITC) as well as averaged power. Defaults to True.

decim : int

The decimation factor on the time axis. To reduce memory usage. Note than this is brute force decimation, no anti-aliasing is done. Defaults to 1.

n_jobs : int

The number of jobs to run in parallel. Defaults to 1.

picks : array-like of int | None

The indices of the channels to plot. If None all available channels are displayed.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).


power : AverageTFR

The averaged power.

itc : AverageTFR

The intertrial coherence (ITC). Only returned if return_itc is True.


New in version 0.9.0.