MATLAB Source Code for Independent Component Analysis (ICA) Implementation

This repository provides MATLAB source code for implementing Independent Component Analysis (ICA), featuring algorithms such as FastICA or Infomax that can separate mixed signals into statistically independent components. The code includes functions for preprocessing (whitening, centering), optimization routines, and component visualization, making it ideal for signal processing and feature extraction tasks in EEG, fMRI, or image processing applications. For advanced implementation techniques and theoretical foundations, we recommend consulting academic papers on signal processing and feature extraction methodologies. Additionally, you may explore complementary open-source tools like Python (using libraries such as Scikit-learn or MNE) and R (with packages like ica) for enhanced data analysis capabilities and sophisticated visualization outputs. Key functions implemented include: - Data normalization and whitening procedures - Iterative separation algorithms with convergence criteria - Component sorting based on variance contribution - Visualization tools for source signal reconstruction