tedana.decomposition.ica.f_ica

f_ica(data, n_components, fixed_seed, maxit, maxrestart)[source]

Perform FastICA on data and returns mixing matrix.

Parameters:

  • data (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 restarted decompositions to perform with different random seeds if ICA fails to converge. 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.

Notes

Uses sklearn implementation of FastICA for decomposition