Applying PCA neural models for the blind separation of signals

Principal component analysis is often thought of as a preprocessing step for blind source separation (BSS). Although second order methods have been proposed for BSS in the past, these approaches cannot be easily implemented by neural models. In this paper we demonstrate that PCA is more than a preprocessing step and, in fact, it can be used directly for solving the BSS problem in combination with very simple temporal filtering process. We also demonstrate that a PCA extension called oriented PCA (OPCA) can be also used for the same purpose without prewhitening the observed data. Both approaches can be implemented using efficient neural models that are shown to successfully extract the hidden sources.