EFFICIENCY OF MACHINE LEARNING OF PSYCHOPHYSIOLOGICAL STATE CLASSIFIERS BASED ON EYE TRACKING DATA IN ORTHOGONAL DIRECTIONS
DOI:
https://doi.org/10.30890/2709-2313.2025-42-04-027Ключові слова:
The problem of assessing human psychophysiological states was studied using machine learning methods within diagnostic feature spaces derived from second-order Volterra polynomial models of the eye movement system (EMS). Experimental “input–output” data fMetrics
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Ştefănescu E., Chelaru V.F., Chira D., Mureşanu D. (2024). Eye Tracking Assessment of Parkinson's Disease: a Clinical Retrospective Analysis. J. Med. Life, vol. 17, no. 3, p. 360.
DOI: https://doi.org/10.25122/jml-2024-0270.
Jansson D., Rosén O., Medvedev A. (2015). Parametric and nonparametric analysis of eye-tracking data by anomaly detection. IEEE Trans. Control Syst. Technol., vol. 23, pp. 1578–1586.
DOI: https://doi.org/10.1109/TCST.2014.2364958.
Keehn B., Monahan P., Enneking B., et al. (2024). Eye-Tracking Biomarkers and Autism Diagnosis in Primary Care. JAMA Netw Open, vol. 7, no. 5, e2411190, pp. 1–14.
DOI: https://doi.org/10.1001/jamanetworkopen.2024.11190.
Dostálová N., Pátková Daňsová P., Ježek S., Vojtechovska M., Šašinka Č. (2024). Towards the Intervention of Dyslexia: a Complex Concept of Dyslexia Intervention System using Eye-Tracking. Proc. of the 2024 Symp. on Eye Tracking Research and Applications (ETRA '24), Article 84, pp. 1-6.
DOI: https://doi.org/10.1145/3649902.3656490.
Sun W., Wang Y., Hu B., Wang Q. (2024). Exploring the Connection between Eye Movement Parameters and Eye Fatigue. J. Phys.: Conf. Ser., vol. 2722.
DOI: https://doi.org/10.1088/1742-6596/2722/1/012013 .
Sevcenko N., Appel T., Ninaus M., et al. (2023). Theory-based approach for assessing cognitive load during time-critical resource-managing human–computer interactions: an eye-tracking study. J. Multimodal User Interfaces, vol. 17, pp. 1-19.
DOI: https://doi.org/10.1007/s12193-022-00398-y
Mézière D.C., Yu L., Reichle E.D., Von Der Malsburg T., McArthur G. (2023). Using eye-tracking measures to predict reading comprehension. Reading Research Quarterly, vol. 58, no. 3, pp. 425-449.
DOI: https://doi.org/10.1002/rrq.498
Li S., Duffy M.C., Lajoie S.P., Zheng J., Lachapelle K. (2023). Using eye tracking to examine expert–novice differences during simulated surgical training: a case study. Computers in Human Behavior, vol. 144, 107720.
DOI: https://doi.org/10.1016/j.chb.2023.107720
Sun J., Liu Y., Wu H., Jing P., Ji Y. (2022). A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data. Front. Hum. Neurosci., vol. 16, Article 972773.
DOI: https://doi.org/10.3389/fnhum.2022.972773.
Kalla B.V., Mandava N., Surya C. (2024). Eye Disease Detection Using Deep Learning Models with Transfer Learning Techniques. ICST Trans. on Scalable Inf. Syst., vol. 11, 2024.
DOI: https://doi.org/10.4108/eetsis.5971.
Solodusha S., Kokonova Y., Dudareva O. (2023). Integral Models in the Form of Volterra Polynomials and Continued Fractions in the Problem of Identifying Input Signals. Mathematics, vol. 11, no. 23, 4724.
DOI: https://doi.org/10.3390/math11234724.
Bro V., Medvedev A. (2023). Continuous and Discrete Volterra-Laguerre Models with Delay for Modeling of Smooth Pursuit Eye Movements. IEEE Trans. Biomed. Eng., vol. 70, no. 1, pp. 97-104.
Doyle F.J., Pearson R.K., Ogunnaike B.A. (2001). Identification and Control Using Volterra Models. Communications and Control Engineering, Springer, London.
Weiss K., Kolbe M., Lohmeyer Q., Meboldt M. (2023). Measuring teamwork for training in healthcare using eye tracking and pose estimation. Front. Psychol., vol. 14, Article 1169940.
DOI: https://doi.org/10.3389/fpsyg.2023.1169940.
Yin J., Sun J., Li J., Liu K. (2022). An Effective Gaze-Based Authentication Method with the Spatiotemporal Feature of Eye Movement. Sensors, vol. 22, Article 3002, pp. 1-18.
DOI: https://doi.org/10.3390/s22083002.
Lanata L., Sebastian L., Di Gruttola F., et al. (2020). Nonlinear Analysis of Eye-Tracking Information for Motor Imagery Assessments. Front. Neurosci., vol. 13, Article 1431.
DOI: https://doi.org/10.3389/fnins.2019.01431.
Porras M.M., Campen C.A.N. Kv., González-Rosa J.J., et al. (2024). Eye Tracking Study in Children to Assess Mental Calculation and Eye Movements. Sci. Rep., vol. 14, Article 18901.
DOI: https://doi.org/10.1038/s41598-024-69800-x.
Chandran P., Huang Y., Munsell J., et al. (2024). Characterizing learners' complex attentional states during online multimedia learning using eye-tracking, egocentric camera, webcam, and retrospective recalls. Proc. of the 2024 Symp. on Eye Tracking Research and Applications (ETRA '24).
DOI: https://doi.org/10.1145/3649902.3653939.
Pardhu T., Deevi N. (2023). EEG Artifact Removal Strategies for BCI Applications: A Survey. Int. J. Electr. Eng. Comput. Sci., vol. 5, pp. 57-72.
DOI: https://doi.org/10.37394/232027.2023.5.8
Tablatin C.L.S. (2023). Visual Attention Patterns in Finding Source Code Defects. WSEAS Trans. on Inf. Sci. and Appl., vol. 20, pp. 375-389.
DOI: https://doi.org/10.37394/23209.2023.20.40.
Hämäläinen R., De Wever B., Sipiläinen K., Zemblys R. (2024). Using eye tracking to support professional learning in vision-intensive professions: a case of aviation pilots. Educ. Inf. Technol., 2024.
DOI: https://doi.org/10.1007/s10639-024-12814-9.
Pavlenko V., Lukashuk D. (2025). Machine Learning Effectiveness of a Psychophysiological State Classification System based on Eye Tracking Technology. WSEAS Trans. on Systems, vol. 24, pp. 424-437.
DOI: https://doi.org/10.37394/23202.2025.24.37
Pavlenko V., Pavlenko S. (2018). Deterministic Identification Methods for Nonlinear Dynamical Systems Based on the Volterra Model. Applied Aspects of Information Technology, vol. 1, no. 1, pp. 9-29.
DOI: https://doi.org/10.15276/aait.01.2018.1.
Pavlenko V., Milosz M., Dzienkowski M. (2020). Identification of the Oculo-Motor System Based on the Volterra Model Using Eye Tracking Technology. J. Phys.: Conf. Ser., vol. 1603, pp. 1-8.
DOI: https://doi.org/10.1088/1742-6596/1603/1/012011.
Pavlenko V., Shamanina T., Chori V. (2024). Nonlinear dynamic model of the oculo-motor system human based on the Volterra series. In: Awrejcewicz J. (ed) Perspectives in Dynamical Systems II — Numerical and Analytical Approaches. Springer Proc. in Mathematics & Statistics, vol. 454, Cham: Springer, pp. 1-12.
DOI: https://doi.org/10.1007/978-3-031-56496-3_27.
Sundararajan D. (2016). Discrete Wavelet Transform: A Signal Processing Approach. John Wiley & Sons, 344 p.
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