ICA (Independent Component Analysis)

Independent Component Analysis (ICA) is a computational method used to separate a mixed set of signals into their original, independent sources. Imagine hearing several people talking simultaneously and wanting to isolate each person's voice. ICA analyzes the combined signals—like overlapping audio or images—and finds a way to disentangle them into individual, independent components. It relies on the idea that these sources are statistically independent from one another. This technique is widely used in fields such as neuroscience, audio processing, and image analysis to uncover hidden, distinct signals hidden within complex data.