tedana.decomposition.tedica

tedica(n_components, dd, conv, fixed_seed, cost='logcosh')

Performs ICA on dd and returns mixing matrix

Parameters:

• n_components (int) – Number of components retained from PCA decomposition
• dd ((S x T) numpy.ndarray) – Dimensionally reduced optimally combined functional data, where S is samples and T is time
• conv (float) – Convergence limit for ICA
• cost ({'logcosh', 'exp', 'cube'} str, optional) – Cost function for ICA
• fixed_seed (int) – Seed for ensuring reproducibility of ICA results

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 sklearn implementation of FastICA for decomposition