Unsupervised adaptive separation of impulse signals applied to EEG analysis
Alexandre Rouxel, Daniel Le Guennec, Odile Macchi · 2002
The theoretical properties of a novel self adaptive source separation algorithm are studied. It is a normalized version of a modified relative gradient. It is shown that its stability domain in terms of the normalized kurtosises of sources is complementary of the unmodified gradient algorithm. So it can separate a source with a very high kurtosis from other sources having positive kurtosis. The algorithm is then used to analyze EEG signals because they often have positive kurtosises especially for patients suffering from epilepsy. The good behavior of this novel algorithm is illustrated via simulated data and then demonstrated with real signals in an EEG analysis to separate an epileptic source from other brain signals.