mne_connectivity.seed_target_multivariate_indices

mne_connectivity.seed_target_multivariate_indices(seeds, targets)

Generate indices parameter for multivariate seed-based connectivity.

Parameters:

seedsarray_like

Indices of signals for which to compute connectivity from. Has n_unique_seeds array-like entries containing the channel indices for each seed. The number of channels within each seed can vary.

targetsarray_like

Indices of signals for which to compute connectivity to. Has n_unique_targets array-like entries containing the channel indices for each target. The number of channels within each target can vary.

Returns:

indicestuple of length 2 of array

A tuple of two numpy object arrays containing the seed and target indices, respectively, to use for connectivity computation. Each array has n_cons array entries containing the channel indices for each connection, where n_cons = n_unique_seeds * n_unique_targets. The number of channels within each connection can vary.

Notes

seeds and targets should be array-likes representing the indices of the channel sets in the data for each connection. The indices for each connection should be an array-like of integers representing the individual channels in the data. The length of indices for each connection do not need to be equal. Furthermore, all indices within a connection must be unique.

Because the number of channels per connection can vary, the indices are stored as numpy arrays with dtype=object. E.g. seeds and targets:

seeds   = [[0]]
targets = [[1, 2], [3, 4, 5]]

would be returned as:

indices = (np.array([[0   ], [0      ]], dtype=object),  # seeds
           np.array([[1, 2], [3, 4, 5]], dtype=object))  # targets

Even if the number of channels does not vary, the indices will still be stored as object arrays for compatibility.

More information on working with multivariate indices and handling connections where the number of seeds and targets are not equal can be found in the Working with ragged indices for multivariate connectivity example.