mne.time_frequency.tfr_morlet#

mne.time_frequency.tfr_morlet(inst, freqs, n_cycles, use_fft=False, return_itc=True, decim=1, n_jobs=None, picks=None, zero_mean=True, average=True, output='power', verbose=None)[source]#

Compute Time-Frequency Representation (TFR) using Morlet wavelets.

Same computation as tfr_array_morlet, 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.

use_fftbool, default False

The fft based convolution or not.

return_itcbool, default True

Return inter-trial coherence (ITC) as well as averaged power. Must be False for evoked data.

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.

picksarray-like of int | None, default None

The indices of the channels to decompose. If None, all available good data channels are decomposed.

zero_meanbool, default True

Make sure the wavelet has a mean of zero.

New in version 0.13.0.

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.

outputstr

Can be “power” (default) or “complex”. If “complex”, then average must be False.

New in version 0.15.0.

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.

Examples using mne.time_frequency.tfr_morlet#

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python
Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

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Non-parametric 1 sample cluster statistic on single trial power

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Non-parametric between conditions cluster statistic on single trial power

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Spatiotemporal permutation F-test on full sensor data

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Mass-univariate twoway repeated measures ANOVA on single trial power

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

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

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