MATLAB Implementation of FastICA Algorithm for Blind Source Separation

In this documentation, we present a complete MATLAB implementation of the FastICA algorithm with full source code. This package introduces blind source separation and Independent Component Analysis (ICA) methodology using the FastICA approach. The implementation includes core functions for signal whitening, non-Gaussianity maximization through negentropy approximation, and iterative optimization using fixed-point algorithms. Key components feature central limit theorem applications for independence measurement and efficient Newton-Raphson optimization for component extraction. The package contains detailed documentation explaining the mathematical foundations of ICA, including covariance matrix decomposition and statistical independence criteria. We provide comprehensive code annotations highlighting critical implementation aspects such as preprocessing steps, convergence criteria handling, and separation performance evaluation metrics. This resource serves as an excellent starting point for understanding and applying FastICA algorithms, with practical examples demonstrating real-world signal separation scenarios and performance benchmarking capabilities.