Independent Component Analysis (ICA)

Independent Component Analysis (ICA) is a computational method used to separate a mixture of signals into their original, independent sources. Imagine hearing overlapping conversations in a room; ICA can help isolate each person's voice. It works by identifying patterns that are statistically independent from each other, such as different sounds or signals. This technique is widely used in fields like neuroscience to analyze brain activity or in audio processing to separate mixed sounds. Essentially, ICA uncovers hidden, independent components from complex data, enabling clearer analysis of the underlying signals.