mne_connectivity.seed_target_indices#

mne_connectivity.seed_target_indices(seeds, targets)[source]#

Generate indices parameter for bivariate seed-based connectivity.

Parameters:

seedsarray_like, shape (n_unique_seeds) | int: Indices of signals for which to compute connectivity from.
targetsarray_like, shape (n_unique_targets) | int: Indices of signals for which to compute connectivity to.

Returns:

indicestuple of length 2 of array, shape (n_cons,): A tuple of 2 arrays containing the seed and target indices, respectively, to use for connectivity computation.

Notes

seeds and targets should be array-likes or integers representing the indices of the channel pairs in the data for each connection. seeds and targets will be expanded such that connectivity will be computed between each seed and each target. E.g. the seeds and targets:

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

would be returned as:

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

where the indices have been expanded to have shape (2, n_cons), where n_cons = n_unique_seeds * n_unique_targets.

Examples using `mne_connectivity.seed_target_indices`#

Compute multivariate coherency/coherence

Comparison of coherency-based methods

Compute seed-based time-frequency connectivity in sensor space

Multivariate decomposition for efficient connectivity analysis

Compute multivariate measures of the imaginary part of coherency

Compute coherence in source space using a MNE inverse solution

Compute Phase Slope Index (PSI) in source space for a visual stimulus

mne_connectivity.seed_target_indices#

Examples using mne_connectivity.seed_target_indices#

Examples using `mne_connectivity.seed_target_indices`#