tedana.decomposition.tedica

tedica(data, n_components, fixed_seed, ica_method='fastica', n_robust_runs=30, maxit=500, maxrestart=10)[source]

Perform ICA on data with the user selected ica method and returns mixing matrix.

Parameters:

  • data ((S x T) numpy.ndarray) – Dimensionally reduced optimally combined functional data, where S is samples and T is time

  • n_components (int) – Number of components retained from PCA decomposition.

  • fixed_seed (int) – Seed for ensuring reproducibility of ICA results.

  • ica_method (:obj: str) – selected ICA method, can be fastica or robustica.

  • n_robust_runs (:obj: int) – selected number of robust runs when robustica is used. Default is 30.

  • maxit (int, optional) – Maximum number of iterations for ICA. Default is 500.

  • maxrestart (int, optional) – Maximum number of attempted decompositions to perform with different random seeds. ICA will stop running if there is convergence prior to reaching this limit. Default is 10.

Returns:

  • mixing ((T x C) numpy.ndarray) – Z-scored mixing matrix for converting input data to component space, where C is components and T is the same as in data

  • fixed_seed (int) – Random seed from final decomposition.