Independent Component Analysis (ICA) is a computational technique used to separate a multivariate signal into additive, independent components, often used in signal processing and data analysis. It assumes that the observed data are linear mixtures of unknown latent variables and aims to reveal these latent variables by maximizing the statistical independence of the estimated components.