tedana.decomposition.tedica

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

Perform ICA on data and return 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 ((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.

Notes

Uses sklearn implementation of FastICA for decomposition