tedana.decomposition.tedica

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

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) – Value passed to mdp.numx_rand.seed(). Set to an integer value for reproducible ICA results; otherwise, set to -1 for varying results across calls.
• cost ({'tanh', 'pow3', 'gaus', 'skew'}) – Initial cost function for ICA.
• final_cost ({'tanh', 'pow3', 'gaus', 'skew'}) – 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