New Applications of Sequential ICA Mixtures Models Compared with Dynamic Bayesian Networks for EEG Signal Processing

Independent Component Analysis (ICA) is a blind source separation method that has proven popular in many fields of application. ICA can be improved incorporating temporal dependencies creating dynamic ICA methods and defining subspaces with multiple ICAs. Such a dynamic ICA method is called Sequential Independent Component Analysis Mixture Model (SICAMM). This method is proposed for two new EEG signal processing applications: detection of arousals in apnea patients and brain hemisphere activity classification during a memory task. SICAMM is compared with a nondynamic ICAMM model and a Dynamic Bayesian Network (DBN). Results show that SICAMM obtains a better performance than the DBN and both dynamic methods achieve a higher classification rate than the stationary ICAMM model. Furthermore, the structure of the SICAMM parameters suggests it for extraction of significant clinical information.