ICA Principles

Independent Component Analysis (ICA) is a computational method used to separate a complex set of signals into simpler, independent sources. Imagine being at a noisy party and trying to focus on only one conversation; ICA does something similar for data, distinguishing different underlying signals even when they are mixed together. It assumes the original sources are statistically independent and finds a way to untangle them by analyzing the patterns in the observed data. This process helps in applications like audio separation, brain imaging, and image processing, enabling clearer analysis of the original, hidden signals.