Compute Time-Frequency Representation (TFR) using Morlet wavelets.
Same computation as tfr_array_morlet, but
operates on Epochs objects instead of
NumPy arrays.
Epochs | EvokedThe epochs or evoked object.
ndarray, shape (n_freqs,)The frequencies in Hz.
float | ndarray, shape (n_freqs,)The number of cycles globally or for each frequency.
FalseThe fft based convolution or not.
TrueReturn inter-trial coherence (ITC) as well as averaged power.
Must be False for evoked data.
int | slice, default 1To 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.
int | NoneThe 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.
int | None, default NoneThe indices of the channels to decompose. If None, all available good data channels are decomposed.
TrueMake sure the wavelet has a mean of zero.
New in version 0.13.0.
TrueIf 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.
strCan be “power” (default) or “complex”. If “complex”, then average must be False.
New in version 0.15.0.
str | int | NoneControl 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.
AverageTFR | EpochsTFRThe averaged or single-trial power.
AverageTFR | EpochsTFRThe inter-trial coherence (ITC). Only returned if return_itc is True.
mne.time_frequency.tfr_morlet#Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power
Mass-univariate twoway repeated measures ANOVA on single trial power
Spatiotemporal permutation F-test on full sensor data
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)