Blind extraction of source signals with specified stochastic features

We present a neural-network approach which allows sequential extraction of source signals from a linear mixture of multiple sources in the order determined by absolute values of normalized kurtosis. To achieve this, we develop a non-linear Hebbian learning rule for extraction of a single signal. We discuss several techniques which enable extraction of signals not randomly but in the desired order. To prevent the same signals from being extracted several times, a robust deflation technique is used which eliminates from the mixture the already extracted signals. Extensive computer simulations confirm the validity and high performance of our method