Abstract

An important scientific and practical problem has been solved, which consists in the creation of methods and tools for nonlinear dynamic identification "input-output" of the human oculomotor system (OMS). Identification is carried out based on Volterra models in the form of multidimensional transient functions (n-dimensional integrals from Volterra kernels) using innovative eye tracking technology. As test signals, visual stimuli are used, which are displayed on the monitor screen at different distances from the starting position, which formally corresponds to the action of step signals with different amplitudes at the OMS input. Experimental studies of the OMS of individuals were carried out using the Tobii Pro TX300 eye tracker. The transient functions of the first and diagonal intersections of the transient functions of the second and third order were determined based on the data of oculographic studies. The models differ from the known ones in that they provide the possibility of modeling the OMS in each interval of input signals beyond the radius of convergence of the Volterra series. The obtained multidimensional functions are used as a source of primary data in the implementation of intelligent information technology for the diagnosis and monitoring of human psychophysiological states. A set of heuristic features are proposed, which are determined using integral and differential transformations of the obtained transient functions of the OMS. The informativeness of individual signs and all their possible combinations in pairs according to the indicator of the probability of correct recognition was studied. The research results were obtained using the construction of Bayesian classifiers in different spaces of the proposed features. Figs.: 2. Refs.: 15 titles.
 Keywords: psychophysiological state, diagnosis, monitoring, oculo-motor system, identification, Volterra model, multidimensional transient functions, test visual stimuli, eye-tracking technology

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