The Block EFICA Algorithm. This is an extension of algorithm EFICA, called Block EFICA, for piecewise stationary and non Gaussian signals. In contrast to EFICA and other classical ICA algorithms, the Block EFICA is able to profit from varying distribution of the original signals and also from their varying variance. Therefore, the method is more flexible which is useful when separating real-world signals that usually possess various features not comprised by standard ICA models. Block EFICA theoretically achieves Cramér-Rao bound for the case of constant-variance signals, i.e. signals whose shape of distribution is changing, but the variance remains the same. The only condition is that the score functions of the original signals are known in advance or should be consistently estimated.