Blind separation of skewed signals in instantaneous mixtures

The problem of source separation of instantaneous mixtures has been addressed thoroughly in literature in the past. The assumption of statistical independence between the source signals, led to the introduction of independent component analysis (ICA). A number of methods, based on the ICA framework, can identify nonGaussian sources in instantaneous mixtures with robust convergence and performance. However, in several biomedical applications, there is a need to identify and separate signals that, apart from being nonGaussian, are not symmetric. In this article, the authors present a method for blind identification and separation of skewed (non-symmetric) signals in a linear instantaneous mixture.