Information technology of the diagnostics of neurophysiological states of personality based on the eye-tracking data

Makarov, V.; Broska, D.; Chorniy, V.

Information technology of the diagnostics of neurophysiological states of personality based on the eye-tracking data

Файли

Black_Sea_Sci_2021_Makarov.pdf(5.26 MB)

Дата

2021

Автори

Makarov, V.

Broska, D.

Chorniy, V.

Анотація

Instrumental algorithmic and software tools for constructing a nonparametric dynamic model of the human oculo-motor system (OMS) based on its inertial and nonlinear properties are developed in the paper on the basis of the experimental studies data of «input-output» in the form of the Volterra model. Taking into account the specificity of the object investigated, test multistage signals (visual stimulus) were used for identification. Based on the experimental data obtained using the developed computational algorithms and data processing software, a nonparametric dynamic model of OMS in the form of a transient function and transient functions of the 2nd and 3rd orders is constructed. Verification of the constructed model showed its adequacy to the object investigated – a practical coincidence (within the acceptable error) responses of the object and model for the same test signal. Experimental studies of the OMS made of the same subject before and after the working day. Based on the data using the eye-tracker, multidimensional transient functions of the OMS has obtained. The revealed variability of the transient functions of the 2nd and 3rd orders for different neurophysiological states of the respondent (level of fatigue) has observed. Thus, they can be used in diagnostic studies in the field of the neuroscience and psychology.

Опис

Makarov V. Information technology of the diagnostics of neurophysiological states of personality based on the eye-tracking data / V. Makarov, D. Broska, V. Chorniy ; advisor : V. Pavlenko // Black Sea Science 2021 : proc. of the Intern. Competition of Student Scientific Works / Odessa National Academy of Food Technologies ; eds. B. Yegorov, M. Mardar [et al.]. – Odessa: ONAFT, 2021. – P. 419–434 : tabl., fig. – Ref.: 27 tit.