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 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 parameter.