MODELING AND ESTIMATION OF SINGLE EVOKED BRAIN POTENTIAL COMPONENTS

In this paper, we present a novel approach to solving the single-trial evoked-potential estimation problem. Recognizing that different compo nents of an evoked potential complex may originate from different func tional brain sites and can be distinguished according to their respect ive latencies and amplitudes, we propose an estimation approach based on identification of evoked potential components on a single-trial bas is. The estimation process is performed in two stages: first, an avera ge evoked potential is calculated and decomposed into a set of compone nts, with each component serving as a subtemplate for the next stage; then, the single measurement is parametrically modeled by a superposit ion of an emulated ongoing electroencephalographic activity and a line ar combination of latency and amplitude-corrected component templates. Once optimized, the model provides the two assumed signal contributio ns, namely the ongoing brain activity and the single evoked brain resp onse. The estimator's performance is analyzed analytically and via sim ulation, verifying its capability to extract single components at low signal-to-noise ratios typical of evoked potential data. Finally, two applications are presented, demonstrating the improved analysis capabi lities gained by using the proposed approach. The first application de als with movement related brain potentials, where a change of the sing le evoked response due to external loading is detected. The second app lication involves cognitive event-related brain potentials, where a dy namic change of two overlapping components throughout the experimental session is detected and tracked.