Visual stimulus design in parameter estimation of the human smooth pursuit system from eye-tracking data

The dynamical properties of the human smooth pursuit system (SPS) are studied. Linear black-box and nonlinear Wiener models of the SPS are identified from eye-tracking data in view of their potential applications in diagnosing and staging various clinical conditions. A novel approach to visual stimuli design is suggested and evaluated. Accurate estimation of the linear dynamics requires sufficient input frequency excitation, while the identification of the nonlinear part is dependent upon the signal amplitude distribution. Both aspects of input design are taken into account. Visual stimuli generated using the presented method are shown to yield favorable identification results compared to existing stimuli design techniques in terms of reduced variance of parameter estimates and smaller spread of the parameter clouds pertaining to different individuals. The nonlinear Wiener models of the SPS appear to outperform the linear ones provided the visual stimuli are properly designed.