mne.time_frequency.tfr_array_morlet¶

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

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

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

Parameters

epoch_dataarray of shape (n_epochs, n_channels, n_times)

The epochs.

sfreqfloat | int

Sampling frequency of the data.

freqsarray_like of float, shape (n_freqs,)

The frequencies.

n_cyclesfloat | array of float, default 7.0

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

zero_meanbool | False

If True, make sure the wavelets have a mean of zero. default False.

use_fftbool

Use the FFT for convolutions or not. default True.

decimint | slice

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

Note

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

outputstr, 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_jobsint

The number of jobs to run in parallel (default 1). Requires the joblib package. The number of epochs to process at the same time. The parallelization is implemented across channels. Default 1.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

outarray: 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.

Examples using `mne.time_frequency.tfr_array_morlet`¶

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

mne.time_frequency.psd_array_welch mne.time_frequency.tfr_array_multitaper

mne.time_frequency.tfr_array_morlet¶

Examples using mne.time_frequency.tfr_array_morlet¶

Examples using `mne.time_frequency.tfr_array_morlet`¶