Estimation of trial to trial variability of P300 subcomponents by coupled Rao-blackwellised particle filtering

Abstract

In this paper a new method based on Rao-blackwellised particle filtering for tracking variability of event related-potential (ERP) subcomponents in different trials is presented. The latency, amplitude, and width of each subcomponent is formulated in the state space model. Then, the observation is modeled as a linear function of amplitude and a nonlinear function of latency and width. The Rao-blackwellised particle filtering is then applied for recursive estimation of the state of the system in different trials. To prevent generation of some invalid particles and also to have a reliable estimation in every situation, using some prior knowledge about some ERP subcomponents, a coupled Rao-blackwellised particle filter is designed to detect variability of the desired ERP subcomponents. The method is applied to both simulated and real P300 data. The algorithm has the ability of tracking the variability of P300 subcomponents i.e. P3a and P3b, in single trials even in the low signal-to-noise ratio situations.