tedana.decomposition.tedica

tedica(n_components, dd, conv, fixed_seed, cost, final_cost, verbose=False)[source]

Performs ICA on dd and returns mixing matrix

Parameters:
  • n_components (int) – Number of components retained from PCA decomposition
  • dd ((S x E x T) numpy.ndarray) – Dimensionally-reduced functional data, where S is samples, E is echos, and T is time
  • conv (float) – Convergence limit for ICA
  • fixed_seed (int) – Seed for ensuring reproducibility of ICA results
  • initcost ({'tanh', 'pow3', 'gaus', 'skew'} str, optional) – Initial cost function for ICA
  • finalcost ({'tanh', 'pow3', 'gaus', 'skew'} str, optional) – Final cost function for ICA
  • verbose (bool, optional) – Whether to print messages regarding convergence process. Default: False
Returns:

mmix – Mixing matrix for converting input data to component space, where C is components and T is the same as in dd
Return type:

(C x T) numpy.ndarray

Notes

Uses mdp implementation of FastICA for decomposition