mne.time_frequency.tfr_multitaper#

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]#

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

Same computation as tfr_array_multitaper, but operates on Epochs objects instead of NumPy arrays.

Parameters:

instEpochs | Evoked: The epochs or evoked object.
freqsndarray, shape (n_freqs,): The frequencies in Hz.
n_cyclesfloat | ndarray, shape (n_freqs,): The number of cycles globally or for each frequency. The time-window length is thus T = n_cycles / freq.
time_bandwidthfloat, (optional), default 4.0 (n_tapers=3): 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). 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_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, default 1: To reduce memory usage, decimation factor after time-frequency decomposition. If int, returns tfr[…, ::decim]. If slice, returns tfr[…, decim].

Note

Decimation 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_backend() 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.

Note

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

New in version 0.13.0.
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.

Returns:

powerAverageTFR | EpochsTFR: The averaged or single-trial power.
itcAverageTFR | EpochsTFR: The inter-trial coherence (ITC). Only returned if return_itc is True.