Abstract
This paper deals with the identification of Volterra models that capture the dynamics of smooth pursuit eye movements recorded by an eye tracker. The framework is motivated by neurological applications but can also be useful in biometrics. In healthy subjects, ocular dynamics are predominantly linear, while neurological conditions inflict nonlinearity on smooth pursuit eye movements. Besides overparameterization, Volterra models may also exhibit functional dependence among the model coefficients. A combination of sparse estimation and Principal Component Analysis is shown to be instrumental in estimating parsimonious Volterra models from eye-tracking data. The efficacy of the approach is demonstrated on experimental data collected from Parkinsonian patients as well as healthy controls.
Published Version
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