A new method for spatiotemporal identification of event-related potential subcomponents

In this study a novel method for tracking and separation of event-related potential (ERP) subcomponents from trial to trial is considered. The sources of ERP subcomponents are assumed to be electric current dipoles (ECD). The shape of each ERP subcomponent is also supposed to be monophasic wave and modeled using a Gaussian waveform. We are interested in the estimation and tracking of ERP subcomponent locations and parameters (amplitude, latency and width of each Gaussian waveform). Estimation of ECD locations, which have nonlinear relation to the measurement, is performed by particle filtering, and estimation of the amplitude is optimally estimated by a maximum likelihood approach, and finally estimation of latency and width of the Gaussian functions are given by Newton-Raphson technique. New recursive methods are introduced for both maximum likelihood and Newton-Raphson approaches to prevent the divergence of the filtering in the presence of very low signal to noise ratio (SNR). The proposed method was assessed using both simulated and real data and the results verified a successful deployment of the method in ERP analysis.