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. All these functions work with MNE-Python’s Epochs class, which is the recommended input to these functions. However, they also work on numpy array inputs.

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

Decoding classes

These classes fit filters which decompose data into discrete sources of connectivity, amplifying the signal-to-noise ratio of these interactions.

CoherencyDecomposition(info, method, indices)

Decompose connectivity sources using multivariate coherency-based methods.

Reading functions

read_connectivity(fname)

Read connectivity data from netCDF file.

Pre-processing on connectivity

symmetric_orth(data, *[, n_iter, tol, verbose])

Perform symmetric orthogonalization.

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 bivariate seed-based connectivity.
seed_target_multivariate_indices(seeds, targets)	Generate indices parameter for multivariate seed-based connectivity.
check_indices(indices)	Check indices parameter for bivariate connectivity.
select_order(X[, maxlags])	Compute lag order selections based on information criterion.

Visualization functions

plot_sensors_connectivity(info, con[, ...])	Visualize the sensor connectivity in 3D.
plot_connectivity_circle(con, node_names[, ...])	Visualize connectivity as a circular graph.

Dataset functions

make_signals_in_freq_bands(n_seeds, ...[, ...])	Simulate signals interacting in a given frequency band.
make_surrogate_data(data[, n_shuffles, ...])	Create surrogate data for a null hypothesis of connectivity.