Estimation of single-evoked cerebral potentials by means of parametric modeling and Kalman filtering

A method is presented for the investigation of stimulus evoked cerebral potentials (EPs) in single-trial EEG recordings which separates the measured activity into its evoked and spontaneous parts. A compound state-space model trying to incorporate the observable properties of both parts is formulated on the basis of additivity of the two components. Within this model, spontaneous activity is described as an autoregressive process, while the EP is modeled by an impulse response of a parametrically described system. Based on the state-space representation of the model, a Kalman filter for the observation of the system´s state can be designed which yields optimal estimates for both activities. The properties of the proposed method are tested by application to stimulated data, in which preset EPs are added to measured spontaneous EEG segments. The relative mean squared error and the bias are used to judge the accuracy of the EPs retrieved by the filter. Finally, the method is applied to data where rapid drug-induced effects can be monitored with high time-resolution by means of estimated somatosensory evoked potentials.