mne.time_frequency.csd_array_morlet(X, sfreq, frequencies, t0=0, tmin=None, tmax=None, ch_names=None, n_cycles=7, use_fft=True, decim=1, projs=None, n_jobs=1, verbose=None)[source]

Estimate cross-spectral density from an array using Morlet wavelets.

X : array-like, shape (n_epochs, n_channels, n_times)

The time series data consisting of n_epochs separate observations of signals with n_channels time-series of length n_times.

sfreq : float

Sampling frequency of observations.

frequencies : list of float

The frequencies of interest, in Hertz.

t0 : float

Time of the first sample relative to the onset of the epoch, in seconds. Defaults to 0.

tmin : float | None

Minimum time instant to consider, in seconds. If None start at first sample.

tmax : float | None

Maximum time instant to consider, in seconds. If None end at last sample.

ch_names : list of str | None

A name for each time series. If None (the default), the series will be named ‘SERIES###’.

n_cycles: float | list of float | None

Number of cycles to use when constructing Morlet wavelets. Fixed number or one per frequency. Defaults to 7.

use_fft : bool

Whether to use FFT-based convolution to compute the wavelet transform. Defaults to True.

decim : int | slice

To reduce memory usage, decimation factor during time-frequency decomposition. Defaults to 1 (no decimation).

If int, uses tfr[…, ::decim]. If slice, uses tfr[…, decim].

projs : list of Projection | None

List of projectors to store in the CSD object. Defaults to None, which means the projectors defined in the Epochs object will be copied.

n_jobs : int

Number of jobs to run in parallel. Defaults to 1.

verbose : bool | str | int | None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

csd : instance of CrossSpectralDensity

The computed cross-spectral density.