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ExtendedInfomax

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Python equivalent

mne.preprocessing.ICA (infomax) in MNE-Python.

#include <dsp/extended_infomax.h>

class UTILSLIB::ExtendedInfomax

Extended Infomax ICA (Lee et al., 1999).

Performs Independent Component Analysis using the extended infomax algorithm, which can separate both super-Gaussian and sub-Gaussian sources.

Static Methods​

compute(matData, nComponents, maxIterations, learningRate, tolerance, extendedMode, seed)​

Compute ICA decomposition using the extended infomax algorithm.

Parameters:

  • matData : const Eigen::MatrixXd & Input data matrix (n_channels x n_times), should be mean-removed.

  • nComponents : int Number of components to extract (-1 for n_channels).

  • maxIterations : int Maximum number of iterations.

  • learningRate : double Learning rate for weight updates.

  • tolerance : double Convergence tolerance.

  • extendedMode : bool If true, use extended mode (sub- and super-Gaussian).

  • seed : unsigned int Random seed (0 for no seeding).

Returns:

  • InfomaxResult — InfomaxResult containing unmixing/mixing matrices and sources.

Authors of this file​