Sport Psychology Studies

Sport Psychology Studies

A Pilot Study on the Evaluation of Brain Signal Features to Provide an Index Related to Visual Attention in Semi-Professional Table Tennis Players

Document Type : Original research

Authors
Mohammad Ali Khalilzadeh
Saleh Lashkari
mojtaba Salari
Health Technology Research Center, Imam Reza International University, Mashhad, Iran
10.22089/spsyj.2025.17625.2541
Abstract
For high-cognitive-load sports like table tennis, successful neurofeedback training needs accurate biomarkers for certain cognitive processes, e.g., visual attention. Yet, the majority of existing neurofeedback protocols are based on general EEG features that may not necessarily represent the required mental processes in these rapid sports. To bridge this gap, the current study explores EEG signal features related to visual attention. Therefore, in this research, EEG signals were registered in the Cz area in resting and decision-making conditions from 8 semi-professional male table tennis players (aged between 18 and 25). Features extracted were statistical parameters (mean and variance), Fourier transform mean amplitude and power, wavelet transform coefficients mean absolute value and standard deviation, and nonlinear features like entropy, fractal dimension, correlation dimension, and Lyapunov exponent. Then, correlations of these features with TOVA (Test of Variables of Attention) scores—reaction time and attention errors—were determined, and a t-test was conducted for observing pre- and post-test differences. It was observed that frequency and time-frequency features were most correlated, and their average correlation coefficients with reaction time scores were 0.84 and 0.89 respectively. In addition, frequency and time-frequency features also demonstrated the greatest changes pre- and post-test (P ≤ 0.02). These results indicate that these features can be used as good biomarkers for monitoring and improving attentional processes in sports individuals. Therefore, the use of these features in designing neurofeedback protocols for improving fast cognitive responses in sports individuals—particularly in sports such as table tennis—would not only be effective, but also feasible.
Keywords
Brain signal
visual attention
table tennis
cognitive assessment
frequency feature

Subjects

1.       Ahirwal, M. K., & Londhe, N. (2012). Power spectrum analysis of EEG signals for estimating visual attention. International Journal of Computer Applications, 42(15), 22–25. https://doi.org/10.5120/5773-8039
2.       Albaladejo-Garcia, C., Garcia-Aguilar, F., & Moreno, F. J. (2023). The role of inhibitory control in sport performance: Systematic review and meta-analysis in stop-signal paradigm. Neuroscience & Biobehavioral Reviews, 147, 105108. 
https://doi.org/10.1016/j.neubiorev.2023.105108
3.       Babiloni, C., Marzano, N., Infarinato, F., Iacoboni, M., Rizza, G., Aschieri, P., Del Percio, C. (2010). “Neural efficiency” of experts’ brain during judgment of actions: A high-resolution EEG study in elite and amateur karate athletes. Behavioural Brain Research, 207(2), 466–475. https://doi.org/10.1016/j.bbr.2009.10.034
4.       Badau, D., Baydil, B., & Badau, A. (2018). Differences among three measures of reaction time based on hand laterality in individual sports. Sports, 6(2), 45. https://doi.org/10.3390/sports6020045
5.       Bhattacharya, P., Chatterjee, S., & Mondal, S. (2022). Effect of karate on neurocognitive physiology: A focused review. Neurology India, 70(1), 11–18. 
6.       https://doi.org/10.4103/0028-3886.338722
7.       Calle-Jaramillo, G. A., Gonzalez-Palacio, E. V., Jaramillo, A. R., & Antonio, J. (2024). Differences between expert and novice players in execution time and decision-making in technical-tactical actions in football (passing and driving) performed under laboratory conditions. Retos, 52(2041), 402–409.
8.       Can, S., Kilit, B., Arslan, E., & Suveren, S. (2014). The comparison of reaction time of male tennis players, table tennis players and the ones who don’t exercise at all in 10 to 12 age groups. Beden Eğitimi ve Spor Bilimleri Dergisi, 8(2), 195–201.
9.       Cheng, M. Y., Yu, C. L., An, X., Wang, L., Tsai, C. L., Qi, F., & Wang, K. P. (2024). Evaluating EEG neurofeedback in sport psychology: A systematic review of RCT studies for insights into mechanisms and performance improvement. Frontiers in Psychology, 15, 1331997. 
https://doi.org/10.3389/fpsyg.2024.1331997
10.    Christie, A. W., & Patel, N. (2024). Ab. No. 84 Athlete Mindful Skills Associated With Reaction Time Among Recreational Football Players: An Analytical Study. Journal of Society of Indian Physiotherapists, 8(1), 65.
11.    Del Percio, C., Babiloni, C., Marzano, N., Iacoboni, M., Infarinato, F., Vecchio, F., ... & Eusebi, F. (2009). “Neural efficiency” of athletes’ brain for upright standing: A high-resolution EEG study. Brain Research Bulletin, 79(3–4), 193–200. 
https://doi.org/10.1016/j.brainresbull.2009.02.001
12.    Del Percio, C., Rossini, P. M., Marzano, N., Iacoboni, M., Infarinato, F., Aschieri, P., ... & Eusebi, F. (2008). Is there a “neural efficiency” in athletes? A high-resolution EEG study. NeuroImage, 42(4), 1544–1553. 
https://doi.org/10.1016/j.neuroimage.2008.05.062
13.    Demos, J. N. (2005). Getting started with neurofeedback. W. W. Norton & Company.
14.    Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64(1), 135–168. 
https://doi.org/10.1146/annurev-psych-113011-143750
15.    Duru, A. D., & Assem, M. (2018). Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG. Cognitive Neurodynamics, 12, 95–102. https://doi.org/10.1007/s11571-017-9459-8
16.    Egner, T., & Gruzelier, J. H. (2004). EEG biofeedback of low beta band components: Frequency-specific effects on variables of attention and event-related brain potentials. Clinical Neurophysiology, 115(1), 131–139. 
https://doi.org/10.1016/S1388-2457(03)00353-5
17.    Enriquez-Geppert, S., Huster, R. J., & Herrmann, C. S. (2017). EEG-neurofeedback as a tool to modulate cognition and behavior: A review tutorial. Frontiers in Human Neuroscience, 11, 51. https://doi.org/10.3389/fnhum.2017.00051
18.    Fang, Q. (2022). Impact of sport training on adaptations in neural.
19.    Heilmann, F. (2022). Self-report versus neuropsychological tests for examining executive functions in youth soccer athletes—A cross-sectional study. Behavioral Sciences, 12(9), 346. https://doi.org/10.3390/bs12090346
20.    Huang, H., Li, R., & Zhang, J. (2023). A review of visual sustained attention: Neural mechanisms and computational models. PeerJ, 11, e15351. https://doi.org/10.7717/peerj.15351
21.    Jain, A., Bansal, R., Kumar, A., & Singh, K. D. (2015). A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first-year students. International Journal of Applied and Basic Medical Research, 5(2), 124–127. https://doi.org/10.4103/2229-516X.157168
22.    Karimoi, R. Y., & Karimoi, A. Y. (2014). The effects of beta-I and fractal dimension neurofeedback on reaction time. International Journal of Intelligent Systems and Applications, 6(11), 42–48.
23.    Köster, M., & Gruber, T. (2022). Rhythms of human attention and memory: An embedded process perspective. Frontiers in Human Neuroscience, 16, 905837. 
https://doi.org/10.3389/fnhum.2022.905837
24.    Leark, R. A., Greenberg, L. M., Kindschi, C. L., Dupuy, T. R., & Hughes, S. J. (2007). Test of Variables of Attention Continuous Performance Test. The TOVA Company.
25.    Lempke, L. B., Howell, D. R., Eckner, J. T., & Lynall, R. C. (2020). Examination of reaction time deficits following concussion: A systematic review and meta-analysis. Sports Medicine, 50, 1341–1359. 
https://doi.org/10.1007/s40279-020-01281-0
26.    Mamaghani, J., & Javanmar, G. H. (2008). Standardization of a Brief Symptom Inventory (BSI) for diagnostic aims in consultant and therapeutic situations.
27.    Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017). Neurofeedback as supplementary training for optimizing athletes’ performance: A systematic review with implications for future research. Neuroscience & Biobehavioral Reviews, 75, 419–432. 
https://doi.org/10.1016/j.neubiorev.2017.02.005
28.    Nuri, L., Shadmehr, A., Ghotbi, N., & Attarbashi Moghadam, B. (2013). Reaction time and anticipatory skill of athletes in open and closed skill-dominated sport. European Journal of Sport Science, 13(5), 431–436. 
https://doi.org/10.1080/17461391.2012.738712
29.    Pal, S., Yadav, J., Kalra, S., & Sindhu, B. (2020). Different training approaches in karate—A review. London Journal of Research in Humanities and Social Sciences, 20, 33–44.
30.    Park, J. L., Fairweather, M. M., & Donaldson, D. I. (2015). Making the case for mobile cognition: EEG and sports performance. Neuroscience & Biobehavioral Reviews, 52, 117–130. 
https://doi.org/10.1016/j.neubiorev.2015.02.014
31.    Pineda-Hernández, S. (2022). Playing under pressure: EEG monitoring of activation in professional tennis players. Physiology & Behavior, 247, 113723. 
https://doi.org/10.1016/j.physbeh.2022.113723
32.    Quartiroli, A., Wagstaff, C. R., Martin, D. R., & Tod, D. (2024). A systematic review of professional identity in sport psychology. International Review of Sport and Exercise Psychology, 17(1), 264–290. 
https://doi.org/10.1080/1750984X.2023.2259044
33.    Rashid, M. M., & Ahmad, M. (2017, February). Epileptic seizure classification using statistical features of EEG signal. In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 308–312). IEEE. 
https://doi.org/10.1109/ECACE.2017.7912939
34.    Reigal, R. E., Barrero, S., Martín, I., Morales-Sánchez, V., Juárez-Ruiz de Mier, R., & Hernández-Mendo, A. (2019). Relationships between reaction time, selective attention, physical activity, and physical fitness in children. Frontiers in Psychology, 10, 2278. https://doi.org/10.3389/fpsyg.2019.02278
35.    Schiff, S. J., Aldroubi, A., Unser, M., & Sato, S. (1994). Fast wavelet transformation of EEG. Electroencephalography and Clinical Neurophysiology, 91(6), 442–455. 
https://doi.org/10.1016/0013-4694(94)90165-1
36.    Sun, Q. (2024). EEG-powered cerebral transformer for athletic performance. Frontiers in Neurorobotics, 18, 1499734. 
https://doi.org/10.3389/fnbot.2024.1499734
37.    Thompson, T., Steffert, T., Ros, T., Leach, J., & Gruzelier, J. (2008). EEG applications for sport and performance. Methods, 45(4), 279–288. https://doi.org/10.1016/j.ymeth.2008.07.006
38.    Toy, S., Ozsoy, S., Shafiei, S., Antonenko, P., & Schwengel, D. (2023). Using electroencephalography to explore neurocognitive correlates of procedural proficiency: A pilot study to compare experts and novices during simulated endotracheal intubation. Brain and Cognition, 165, 105938.
 https://doi.org/10.1016/j.bandc.2023.105938
39.    Trajkovic, J. (2023). Oscillatory mechanisms of conscious perception and attention.
40.    Tsai, Y. H., Wu, S. K., Yu, S. S., & Tsai, M. H. (2022). Analyzing brain waves of table tennis players with machine learning for stress classification. Applied Sciences, 12(16), 8052. https://doi.org/10.3390/app12168052
41.    Übeyli, E. D. (2009). Statistics over features: EEG signals analysis. Computers in Biology and Medicine, 39(8), 733–741. 
https://doi.org/10.1016/j.compbiomed.2009.06.001
42.    Visser, A., Büchel, D., Lehmann, T., & Baumeister, J. (2022). Continuous table tennis is associated with processing in frontal brain areas: An EEG approach. Experimental Brain Research, 240(6), 1899–1909. 
https://doi.org/10.1007/s00221-022-06388-6
43.    Wang, C., Verma, A. K., Guragain, B., Xiong, X., & Liu, C. (2024). Classification of bruxism based on time-frequency and nonlinear features of single channel EEG. BMC Oral Health, 24(1), 81. https://doi.org/10.1186/s12903-024-03958-8
44.    Wang, X., Liu, Z., Zhang, H., & Ji, C. (2023). Transfer effect of cognitive advantages in visual working memory capacity: Evidence from elite football players. Behavioral Sciences, 13(6), 464. https://doi.org/10.3390/bs13060464
45.    Wolf, S., Brölz, E., Keune, P. M., Wesa, B., Hautzinger, M., Birbaumer, N., & Strehl, U. (2015). Motor skill failure or flow-experience? Functional brain asymmetry and brain connectivity in elite and amateur table tennis players. Biological Psychology, 105, 95–105. 
https://doi.org/10.1016/j.biopsycho.2014.12.007
46.    Wolf, S., Brölz, E., Scholz, D., Ramos-Murguialday, A., Keune, P. M., Hautzinger, M., ... & Strehl, U. (2014). Winning the game: Brain processes in expert, young elite and amateur table tennis players. Frontiers in Behavioral Neuroscience, 8, 370. https://doi.org/10.3389/fnbeh.2014.00370
47.    Yao, Z. F., Sligte, I. G., & Ridderinkhof, R. (2024). Olympic team rowers and team swimmers show altered functional brain activation during working memory and action inhibition. Neuropsychologia, 203, 108974.
 https://doi.org/10.1016/j.neuropsychologia.2024.108974
48.    Zhu, Y., Wu, D., Sun, K., Chen, X., Wang, Y., He, Y., & Xiao, W. (2023). Alpha and theta oscillations are causally linked to interference inhibition: Evidence from high-definition transcranial alternating current stimulation. Brain Sciences, 13(7), 1026. https://doi.org/10.3390/brainsci13071026
Sport Psychology Studies
Volume 14, Issue 53
Summer 2025
Pages 100-117

  • Receive Date 01 January 2025
  • Revise Date 05 August 2025
  • Accept Date 28 August 2025