Cumulant matching for independent source extraction

In this work we show how one can make use of priors on signal statistics under the form of cumulant guesses to extract an independent source from an observed mixture. The advantage of using statistical priors on the signal lies in the fact that no specific knowledge is needed about its temporal behavior, neither about its spatial distribution. We show that these statistics can be obtained either by reasoning on the theoretical values of a supposed waveform, either by using a subset of the observations from which we know that their statistics are merely hindered by interferences. Results on an electro-cardiographic recording confirm the above assumptions.