tedana.decomposition.tedica

tedica(data, n_components, fixed_seed, maxit=500, maxrestart=10)[source]

Perform ICA on data 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
  • 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:

mmix – Z-scored mixing matrix for converting input data to component space, where C is components and T is the same as in data

Return type:

(T x C) numpy.ndarray

Notes

Uses sklearn implementation of FastICA for decomposition