Event-related potentials Source Separation based on a weak exclusion principle

Currently, the standard event-related potentials (ERP) technique consists in averaging many on-going electroencephalogram (EEG) trials using the same stimuli. Key questions are how to extract the ERP from on-going EEG with fewer average times and how to further decompose ERP into basic components related to cognitive process. In this paper we introduce a novel Blind Source Separation (BSS) approach based on a weak exclusion principle (WEP) to solve these problems. The superior aspect of this algorithm is that it is based on a deterministic principle, which is more appropriate to analyze non-stationary EEG signals than most other BSS methods based on statistical hypotheses. The results show that our BSS algorithm can quickly and effectively extract ERPs using fewer average times than the traditional averaging methods. We show that, via BSS, we can isolate two main ERP components, which are respectively related to an exogenous process and a cognitive process, and can discriminate between the occipital lobe and the frontal lobe responses from the brain, agreeing with the classical component modeling in ERPs. Single-trial ERP separation results have demonstrated the consistency of these two main ERP components. Thus, BSS based on WEP can provide a window to better understand ERP, not only in averaging behavior, but in the complexities of moment-to-moment dynamics as well.