Applications of neural blind separation to signal and image processing

In blind source separation one tries to separate statistically independent unknown source signals from their linear mixtures without knowing the mixing coefficients. Such techniques are currently studied actively both in statistical signal processing and unsupervised neural learning. In this paper, we apply neural blind separation techniques developed in our laboratory to extraction of features from natural images and to separation of medical EEG signals. The new analysis method yields features that describe the underlying data better than for example classical principal component analysis. We briefly discuss difficulties related with real-world applications of blind signal processing, too.