mne.time_frequency.tfr_array_morlet¶

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

Compute time-frequency transform using Morlet wavelets.

Convolves epoch data with selected Morlet wavelets.

Parameters:

Parameters:	epoch_data : array of shape (n_epochs, n_channels, n_times) The epochs. sfreq : float \| int Sampling frequency of the data. frequencies : array-like of floats, shape (n_freqs) The frequencies. n_cycles : float \| array of float, defaults to 7.0 Number of cycles in the Morlet wavelet. Fixed number or one per frequency. zero_mean : bool \| False If True, make sure the wavelets have a mean of zero. Defaults to False. 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, defaults to ‘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, defaults to 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

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

The epochs.

sfreq : float | int

Sampling frequency of the data.

frequencies : array-like of floats, shape (n_freqs)

The frequencies.

n_cycles : float | array of float, defaults to 7.0

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

zero_mean : bool | False

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

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, defaults to ‘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, defaults to 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