mne_connectivity.spectral_connectivity_time(data, names=None, method='coh', indices=None, sfreq=6.283185307179586, foi=None, sm_times=0.5, sm_freqs=1, sm_kernel='hanning', mode='cwt_morlet', mt_bandwidth=None, freqs=None, n_cycles=7, decim=1, block_size=None, n_jobs=1, verbose=None)[source]#

Compute frequency- and time-frequency-domain connectivity measures.

This method computes single-Epoch time-resolved spectral connectivity.

The connectivity method(s) are specified using the “method” parameter. All methods are based on estimates of the cross- and power spectral densities (CSD/PSD) Sxy and Sxx, Syy.


data : Epochs

The data from which to compute connectivity.

names : list | np.ndarray | None

The names of the nodes of the dataset used to compute connectivity. If ‘None’ (default), then names will be a list of integers from 0 to n_nodes. If a list of names, then it must be equal in length to n_nodes.

method : str | list of str

Connectivity measure(s) to compute. These can be ['coh', 'plv', 'sxy']. These are:

  • ‘coh’ : Coherence

  • ‘plv’ : Phase-Locking Value (PLV)

  • ‘sxy’ : Cross-spectrum

By default, the coherence is used.

indices : tuple of array | None

Two arrays with indices of connections for which to compute connectivity. I.e. it is a (n_pairs, 2) array essentially. If None, all connections are computed.

sfreq : float

The sampling frequency.

foi : array_like | None

Extract frequencies of interest. This parameters should be an array of shapes (n_foi, 2) defining where each band of interest start and finish.

sm_times : float

Amount of time to consider for the temporal smoothing in seconds. By default, 0.5 sec smoothing is used.

sm_freqs : int

Number of points for frequency smoothing. By default, 1 is used which is equivalent to no smoothing.

sm_kernel : {‘square’, ‘hanning’}

Kernel type to use. Choose either ‘square’ or ‘hanning’ (default).

mode : str, optional

Spectrum estimation mode can be either: ‘multitaper’, or ‘cwt_morlet’.

mt_bandwidth : float | None

The bandwidth of the multitaper windowing function in Hz. Only used in ‘multitaper’ mode.

freqs : array

Array of frequencies of interest for use in time-frequency decomposition method (specified by mode).

n_cycles : float | array of float

Number of cycles for use in time-frequency decomposition method (specified by mode). Fixed number or one per frequency.

decim : int | 1

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

block_size : int

How many connections to compute at once (higher numbers are faster but require more memory).

n_jobs : int

How many epochs to process in parallel.

verbose : bool, str, int, or None

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


con : array | instance of Connectivity

Computed connectivity measure(s). Either an instance of SpectralConnectivity or SpectroTemporalConnectivity. The shape of each connectivity dataset is either (n_signals ** 2, n_freqs) mode: ‘multitaper’ or ‘fourier’ (n_signals ** 2, n_freqs, n_times) mode: ‘cwt_morlet’ when “indices” is None, or (n_con, n_freqs) mode: ‘multitaper’ or ‘fourier’ (n_con, n_freqs, n_times) mode: ‘cwt_morlet’ when “indices” is specified and “n_con = len(indices[0])”.


This function was originally implemented in frites and was ported over.

New in version 0.3.