Estimation psychophysiological state via nonlinear dynamic integral models

Abstract

The method of experimental research “input-output” of the human oculo-motor system was developed and implemented using innovative eye-tracking technology for recording oculo-motor system responses to test visual stimuli. Stimuli are displayed on the monitor screen at different distances from the starting position. This formally corresponds to the action of step signals with different amplitudes at the input of the oculo-motor system. According to the empirical data of the “input-output” studies of the respondent's oculo-motor system obtained with the aid of the Tobii Pro TX300 eye tracker, the transient functions of the first and diagonal intersections of the transient functions of the second and third orders of the oculo-motor system were determined. Experimental studies of the respondent's oculo-motor system to identify the state of fatigue were carried out before the beginning (in the morning) and after the working day (in the evening). The obtained multidimensional transient functions are used as a source of primary data in the implementation of intelligent information technology for diagnosis and monitoring of the psychophysiological state of a person. Instrumental algorithmic and software tools for determining diagnostic features based on the identification data of the oculo-motor system in the form of multidimensional transient functions in the Python language have been developed. Training samples of data for two states of the respondent (“Normal” and “Fatigue”) were formed on the basis of the proposed heuristic features, which are determined using integral and differential transformations of the obtained multidimensional transient functions of the oculo-motor system. Training samples of data are used to build classifiers of psychophysiological states of an individual using machine learning tools. The informativeness of individual features and all their possible combinations in pairs according to the indicator of the probability of correct recognition was studied using the method of complete search. The research results were obtained by evaluating the quality of recognition of states built by Bayesian classifiers in different spaces of the proposed features. An analysis of the stability of the correct recognition informativeness indicator of different feature spaces under the influence of different levels of additive noise on the features was carried out. Two-dimensional feature spaces with the maximum and most stable value of the correct recognition indicator were found when solving the scientific and practical task of assessing the psychophysiological state (fatigue) of a person (0.9375). Thus, it seems appropriate to use the multidimensional transient functions obtained from eye-tracking data in diagnostic studies in the fields of neuroscience and experimental psychology