This documentation is for development version 0.18.dev0.

mne.time_frequency.tfr_array_multitaper

mne.time_frequency.tfr_array_multitaper(epoch_data, sfreq, freqs, n_cycles=7.0, zero_mean=True, time_bandwidth=None, use_fft=True, decim=1, output='complex', n_jobs=1, verbose=None)[source]

Compute time-frequency transforms using wavelets and multitaper windows.

Uses Morlet wavelets windowed with multiple DPSS tapers.

Parameters:
epoch_data : array of shape (n_epochs, n_channels, n_times)

The epochs.

sfreq : float | int

Sampling frequency of the data.

freqs : array-like of float, shape (n_freqs,)

The frequencies.

n_cycles : float | array of float

Number of cycles in the Morlet wavelet. Fixed number or one per frequency. Defaults to 7.0.

zero_mean : bool

If True, make sure the wavelets have a mean of zero. Defaults to True.

time_bandwidth : float

If None, will be set to 4.0 (3 tapers). Time x (Full) Bandwidth product. The number of good tapers (low-bias) is chosen automatically based on this to equal floor(time_bandwidth - 1). Defaults to None

use_fft : bool

Use the FFT for convolutions or not. Defaults to True.

decim : int | slice

To reduce memory usage, decimation factor after time-frequency decomposition. Defaults to 1. If int, returns tfr[…, ::decim]. If slice, returns tfr[…, decim].

Note

Decimation may create aliasing artifacts, yet decimation is done after the convolutions.

output : str, default ‘complex’
  • ‘complex’ : single trial complex.
  • ‘power’ : single trial power.
  • ‘phase’ : single trial phase.
  • ‘avg_power’ : average of single trial power.
  • ‘itc’ : inter-trial coherence.
  • ‘avg_power_itc’ : average of single trial power and inter-trial coherence across trials.
n_jobs : int

The number of epochs to process at the same time. The parallelization is implemented across channels. Defaults to 1.

verbose : bool, str, int, or None

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

Returns:
out : array

Time frequency transform of epoch_data. If output is in [‘complex’, ‘phase’, ‘power’], then shape of out is (n_epochs, n_chans, n_freqs, n_times), else it is (n_chans, n_freqs, n_times). If output is ‘avg_power_itc’, the real values code for ‘avg_power’ and the imaginary values code for the ‘itc’: out = avg_power + i * itc

Notes

New in version 0.14.0.