A question about ICA and non-gaussianity

1. Why does ICA require non Gaussian?
2. What is ICA used for?
3. What is non Gaussian signals?
4. What does non Gaussian mean in statistics?

Why does ICA require non Gaussian?

ICA uses the idea of non-Gaussianity to uncover independent components. Non-Gaussianity quantifies how far the distribution of a random variable is from being Gaussian. Example measures of non-Gaussianity are kurtosis and negentropy. Why such a measure is helpful follows from the Central Limit Theorem.

What is ICA used for?

Independent Component Analysis (ICA) is a technique that allows the separation of a mixture of signals into their different sources, by assuming non Gaussian signal distribution (Yao et al., 2012). The ICA extracts the sources by exploring the independence underlying the measured data.

What is non Gaussian signals?

All signal processing techniques exploit signal structure; when the signals are random, we want to understand the probabilistic structure of irregular, ill-formed signals. Such signals can be either be bothersome (noise) or information-bearing (discharges of single neurons).

What does non Gaussian mean in statistics?

What is non-Gaussian data? Data not drawn from a population of values having a Gaussian distribution. more information can be contained in the data distribution than in the covariance matrix.