Determinants of Electrooculography Measurements for Quiet Eye Duration in Archers via Penalty Regularizer

Year 2025, Volume: 14 Issue: 3, 1596 - 1609, 30.09.2025

Fatma Söğüt , İnci Kesilmiş , Evrim Ersin Kangal , Ülkü Çömelekoglu

https://doi.org/10.17798/bitlisfen.1683303

Abstract

Electrooculography (EOG) is an electrophysiological method used to assess eye movements and the functional status of the retinal pigment epithelium by measuring the potential differences generated during eyeball motion. In recent years, EOG has also been explored as a tool for determining quiet eye (QE) periods in athletes. The QE period refers to the duration an athlete maintains focused visual attention on a target, and it is considered to be closely linked with athletic performance.
The application of EOG in this context is emerging as a novel approach to evaluate and enhance athletes’ visual focus and concentration skills. In this study, the relationships between QE period and various free parameters of the athletes were analyzed using penalty approaches (Lasso and Ridge regression). Thanks to this approach, QE periods can be predicted with high accuracy based on the independent variables of individuals without any direct QE measurement for different athletes, and this will contribute to the development of preventive or supportive strategies for performance. The R2 value for the coefficients of the two regression methods was obtained more than 80% and the mean square deviation was less than 5%.

Keywords

Electrooculography , Quiet eye period , Archer , Regression , Data prediction

References

• E. Vendrame, V. Belluscio, L. Truppa, L. Rum, A. Lazich, E. Bergamini, and A. Mannini, “Performance assessment in archery: A systematic review,” Sports Biomech., vol. 23, no. 12, pp. 2444–2466, Dec. 2024, doi: 10.1080/14763141.2022.2049357.
• T. Taha et al., “Performance analysis of archery using a wearable system,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 319, p. 012017, 2018, doi: 10.1088/1757-899X/319/1/012017.
• M. Siekańska, R. Z. Bondár, S. di Fronso, J. Blecharz, and M. Bertollo, “Integrating technology in psychological skills training for performance optimization in elite athletes: A systematic review,” Psychol. Sport Exerc., vol. 57, p. 102008, 2021, doi: 10.1016/j.psychsport.2021.102008.
• N. Vasilis et al., “Injuries and persistent pain in elite adolescent archery athletes: A cross-sectional epidemiological study,” Sports, vol. 12, no. 4, p. 101, 2024, doi: 10.3390/sports12040101.
• J. N. Vickers, “Visual control when aiming at a far target,” J. Exp. Psychol. Hum. Percept. Perform., vol. 22, no. 2, pp. 342–354, 1996, doi: 10.1037/0096-1523.22.2.342.
• J. N. Vickers, Perception, Cognition, and Decision Training: The Quiet Eye in Action. Champaign, IL: Human Kinetics, 2007.
• J. Causer, P. S. Holmes, and A. M. Williams, “Quiet Eye training facilitates visuomotor coordination in children with developmental coordination disorder,” J. Motor Behav., vol. 42, no. 6, pp. 357–365, 2010, doi: 10.1080/00222895.2010.526456.
• S. J. Vine, L. J. Moore, and M. R. Wilson, “Quiet Eye training: Effects on learning and performance under pressure,” J. Appl. Sport Psychol., vol. 23, no. 2, pp. 243–257, 2011, doi: 10.1080/10413200.2010.546827.
• J. N. Vickers, “The Quiet Eye: Origins, Controversies, and Future Directions,” Kinesiology Rev., vol. 5, no. 2, pp. 119–128, 2016, doi: 10.1123/kr.2016-0005.
• C. C. Gonzalez, J. Causer, M. J. Grey, G. W. Humphreys, R. C. Miall, and A. M. Williams, “Exploring the quiet eye in archery using field- and laboratory-based tasks,” Exp. Brain Res., vol. 235, no. 9, pp. 2843–2855, 2017, doi: 10.1007/s00221-017-4988-2.
• F. J. J. Reis, R. K. Alaiti, C. S. Vallio, and L. Hespanhol, “Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives,” Braz. J. Phys. Ther., vol. 28, p. 101083, 2024, doi: 10.1016/j.bjpt.2024.101083.
• J. G. Claudino et al., “Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review,” Sports Med. Open, vol. 5, no. 1, p. 1, 2019, doi: 10.1186/s40798-019-0202-3.
• OpenAI, ChatGPT. (2025, Apr. 24). [Online]. Available: https://chat.openai.com/
• T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer, 2009, doi: 10.1007/978-0-387-84858-7.
• G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. New York, NY, USA: Springer, 2013, doi: 10.1007/978-1-4614-7138-7.
• H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” J. R. Stat. Soc. Ser. B (Statistical Methodology), vol. 67, no. 2, pp. 301–320, 2005, doi: 10.1111/j.1467-9868.2005.00503.x.
• A. Bustamante-Garrido, M. Izquierdo, B. Miarka, A. Cuartero-Navarrete, J. Pérez-Contreras, E. Aedo-Muñoz, and H. Cerda-Kohler, “Mechanical determinants of sprinting and change of direction in elite female field hockey players,” Sensors (Basel), vol. 23, no. 18, p. 7663, 2023, doi: 10.3390/s23187663.
• A. Damoulaki, I. Ntzoufras, and K. Pelechrinis, “Lasso multinomial performance indicators for in-play basketball data,” Comput. Stat., vol. 40, pp. 2157–2181, 2025, doi: 10.1007/s00180-025-01604-7.