Tracking single-trial evoked potential changes with Kalman filtering and smoothing

A mathematical way to describe trial-to-trial variations in evoked potentials (EPs) is given by state-space modeling. Linear estimators optimal in the mean square sense can then be obtained through Kalman filter and smoother algorithms. Of importance are the parametrization of the problem and the selection of an observation model for estimation. In this paper, we introduce a general way for designing a model for dynamical estimation of EPs. The observation model is constructed based on a finite impulse response (FIR) filter and can be used for different kind of EPs. We also demonstrate that for batch processing the use of the smoother algorithm is preferable. The method is demonstrated with measurements obtained from an experiment with visual stimulation.