Oriented Pca and Blind Signal Separation

Oriented PCA (OPCA) extends standard Principal Component Analysis by maximizing the power ratio of a pair of signals rather than the power of a single signal. We show that OPCA in combination with almost arbitrary temporal filtering can be used for the blind separation of linear instantaneous mixtures. Although the method works for almost any filter, the design of the optimal temporal filter is also discussed for filters of length two. Compared to other Second Order Statistics (SOS) methods this approach avoids the spatial prewhitening step. Further, it is a fast converging iterative approach which achieves better performance by combining several time lags for the estimation of the mixing parameters.