Extension of EFICA algorithm for blind separation of piecewise stationary non Gaussian sources

We propose an extension of EFICA algorithm for piecewise stationary and non Gaussian signals. The proposed method is able to profit from varying distribution of the original signals and also from their varying variance, which is demonstrated by simulations with real-world signals. We show that in case of constant-variance signals, the accuracy of the method may achieve the corresponding Cramer-Rao bound, if score functions of the original signals are known in all blocks.