Implementation of Independent Component Analysis: A Collection of MATLAB Programs

This article introduces a highly practical toolkit containing several MATLAB programs specifically designed for implementing Independent Component Analysis (ICA). The toolkit includes implementations of key ICA algorithms such as FastICA (using fixed-point iteration for non-Gaussianity maximization) and JADE (Joint Approximate Diagonalization of Eigenmatrices) for blind source separation. These programs facilitate efficient data preprocessing, component extraction, and result visualization through functions like ica_main() for core processing and visualize_components() for output analysis. By utilizing this toolkit, researchers can significantly streamline their ICA workflow, gaining deeper insights into data-model relationships through practical code implementations that handle real-world signal processing challenges. The package not only enhances research productivity but also provides comprehensive understanding of ICA methodologies through commented code and algorithm explanations.