An Improved Method for Blind Source Separation

Abstract

In practical applications especially in biomedical signal processing, a large number of sensors is available but only one or a very few are desired. The simultaneous blind source separation (BSS) technique always introduces large computational load. A contrast function is formulated associated with normalized kurtosis. Furthermore, an improved learning algorithm is derived based on the standard gradient descent rule. In contrast to simultaneous BSS, the proposed method can provide more flexibility and has some potential advantages in terms of computational load. Computer simulations illustrate its performance.