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
) – Value passed tomdp.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
- n_components (