tedana.decomposition
.tedica¶
-
tedica
(data, n_components, fixed_seed, maxit=500, maxrestart=10)[source]¶ Perform ICA on data and returns 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 – Z-scored mixing matrix for converting input data to component space, where C is components and T is the same as in data
Return type: (T x C)
numpy.ndarray
Notes
Uses sklearn implementation of FastICA for decomposition
- data ((S x T)