API

mne_connectivity:

Connectivity for MEG, EEG and iEEG data.

This is the application programming interface (API) reference for classes (CamelCase names) and functions (underscore_case names) of MNE-Connectivity, grouped thematically by analysis stage. The data structure classes contain different types of connectivity data and are described below.

Most-used classes

Connectivity(data, n_nodes[, names, …])	Connectivity class without frequency or time component.
TemporalConnectivity(data, times, n_nodes[, …])	Temporal connectivity class.
SpectralConnectivity(data, freqs, n_nodes[, …])	Spectral connectivity class.
SpectroTemporalConnectivity(data, freqs, …)	Spectrotemporal connectivity class.
EpochConnectivity(data, n_nodes[, names, …])	Epoch connectivity class.
EpochTemporalConnectivity(data, times, n_nodes)	Temporal connectivity class over Epochs.
EpochSpectralConnectivity(data, freqs, n_nodes)	Spectral connectivity class over Epochs.
EpochSpectroTemporalConnectivity(data, …)	Spectrotemporal connectivity class over Epochs.

Connectivity functions

These functions compute connectivity and return one of the Connectivity data structure classes listed above.

envelope_correlation(data[, names, …])	Compute the envelope correlation.
phase_slope_index(data[, indices, names, …])	Compute the Phase Slope Index (PSI) connectivity measure.
spectral_connectivity(data[, names, method, …])	Compute frequency- and time-frequency-domain connectivity measures.
vector_auto_regression(data[, times, names, …])	Compute vector auto-regresssive (VAR) model.

Reading functions

read_connectivity(fname)

Read connectivity data from netCDF file.

Post-processing on connectivity

degree(connectivity[, threshold_prop])	Compute the undirected degree of a connectivity matrix.
seed_target_indices(seeds, targets)	Generate indices parameter for seed based connectivity analysis.
check_indices(indices)	Check indices parameter.

Installation mne_connectivity.Connectivity