Virtual Reality-Enhanced Neurofeedback: Training Mechanisms, System Components, and Prospects for Cognitive and Neural Rehabilitation
Keywords:
Socio-emotional Education, School Conflict, Verbal Aggression.Abstract
The integration of virtual reality (VR) with electroencephalography (EEG)-based neurofeedback represents a significant advancement in cognitive training and neural rehabilitation. This article examines the training mechanisms of neurofeedback across key EEG frequency bands, slow cortical potentials (SCP), theta (4–7 Hz), alpha (8–13 Hz), sensorimotor rhythm (SMR, 12–15 Hz), and beta (14–30 Hz) detailing their physiological origins, electrode locations, targeted therapeutic effects, and applications in conditions such as epilepsy, attention-deficit/hyperactivity disorder (ADHD), anxiety, depression, and mild cognitive impairment. It is demonstrated how VR overcomes the limitations of conventional neurofeedback environments, monotony, low adherence, and limited adaptability, by providing immersive, interactive, and multi-sensory settings that enhance motivation, brain self-regulation, and neuroplasticity. The typical architecture of VR-supported neurofeedback systems is described, including EEG acquisition and processing, VR control module, and real-time feedback delivery. Reviewed evidence indicates that VR augments specific cortical activation, accelerates neuroplastic changes, and improves outcomes in psychological and neurological rehabilitation, while enabling application in clinical, educational, and home-based contexts. Nevertheless, technical challenges persist, including hardware latency, algorithmic precision, and cybersickness. In conclusion, the VR–neurofeedback synergy constitutes a promising evolution in brain–computer interface applications, offering greater efficacy, accessibility, and personalization. Longitudinal studies and technical refinements are required to fully realize its therapeutic potential.
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Afrash, S., Saemi, E., Gong, A., & Doustan, M. (2023). Neurofeedback training and motor learning: the enhanced sensorimotor rhythm protocol is better or the suppressed alpha or the suppressed mu?. BMC Sports Science, Medicine and Rehabilitation, 15(1), 93. https://doi.org/10.1186/s13102-023-00706-3
Antal, A., Luber, B., Brem, A. K., Bikson, M., Brunoni, A. R., Kadosh, R. C., ... & Paulus, W. (2022). Non-invasive brain stimulation and neuroenhancement. Clinical neurophysiology practice, 7, 146-165. https://doi.org/10.1016/j.cnp.2022.05.002
Berman, D. E., Cowansage, K. P., Bellanti, D. M., Nair, R., Boyd, C. C., Beech, E. H., ... & Kelber, M. S. (2025). Systematic review and meta-analysis of neurofeedback training efficacy and neural mechanisms in the treatment of posttraumatic stress disorder. Frontiers in Neuroscience, 19, 1658652. https://doi.org/10.3389/fnins.2025.1658652
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
Gao, Y., Liu, Y., Cheng, D., & Wang, Y. (2016). A review on development of head mounted display. Journal of computer-aided design & computer graphics, 28(6), 896-904. https://www.jcad.cn/en/article/id/da3252af-7922-4f7b-81f8-d52071030fe8
Gkintoni, E., Vassilopoulos, S. P., Nikolaou, G., & Vantarakis, A. (2025). Neurotechnological approaches to cognitive rehabilitation in mild cognitive impairment: A systematic review of neuromodulation, EEG, virtual reality, and emerging AI applications. Brain Sciences, 15(6), 582. https://doi.org/10.3390/brainsci15060582
Halarnkar, P., Shah, S., Shah, H., Shah, H., & Shah, A. (2012). A review on virtual reality. International Journal of Computer Science Issues (IJCSI), 9(6), 325. https://www.proquest.com/openview/164bd36d4d0797497984d7827a9c9cde/1?pq-origsite=gscholar&cbl=55228
Hao, Z., He, C., Ziqian, Y., Haotian, L., & Xiaoli, L. (2022). Neurofeedback training for children with ADHD using individual beta rhythm. Cognitive Neurodynamics, 16(6), 1323-1333. https://doi.org/10.1007/s11571-022-09798-y
Jensen, O., & Bonnefond, M. (2026). The alpha rhythm: from physiology to behaviour. Physiological Reviews. DOI: 10.1152/physrev.00001.2025
Khanghah, A. B., Fernie, G., & Fekr, A. R. (2023). A novel approach to tele-rehabilitation: implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications, 14, 100499. https://doi.org/10.1016/j.mlwa.2023.100499
Lalanza, J. F., Lorente, S., Bullich, R., García, C., Losilla, J. M., & Capdevila, L. (2023). Methods for heart rate variability biofeedback (HRVB): A systematic review and guidelines. Applied Psychophysiology and Biofeedback, 48(3), 275-297. https://doi.org/10.1007/s10484-023-09582-6
Liao, W., Li, J., Zhang, X., & Li, C. (2023). Motor imagery brain–computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study. Frontiers in Human Neuroscience, 17, 1117670. https://doi.org/10.3389/fnhum.2023.1117670
Liwei, Z. (2024). Biofeedback Training. In: Kan, Z. (eds) The ECPH Encyclopedia of Psychology. Springer, Singapore. https://doi.org/10.1007/978-981-97-7874-4_707
Lombardo, C. (2024). An Innovative and Hypothetical Model for Personalizing Physical Training through Neurofeedback and Biofeedback. Journal of Biology and Health Science. https://doi.org/10.56147/jbhs.1.2.7
Lüddecke, R., & Felnhofer, A. (2022). Virtual reality biofeedback in health: a scoping review. Applied psychophysiology and biofeedback, 47(1), 1-15. https://doi.org/10.1007/s10484-021-09529-9
Orban, M., Elsamanty, M., Guo, K., Zhang, S., & Yang, H. (2022). A review of brain activity and EEG-based brain–computer interfaces for rehabilitation application. Bioengineering, 9(12), 768. https://doi.org/10.3390/bioengineering9120768
Patil, A. U., Madathil, D., Fan, Y. T., Tzeng, O. J., Huang, C. M., & Huang, H. W. (2022). Neurofeedback for the education of children with ADHD and specific learning disorders: A review. Brain sciences, 12(9), 1238. https://doi.org/10.3390/brainsci12091238
Pourbehbahani, Z., Saemi, E., Cheng, M. Y., & Dehghan, M. R. (2023). Both sensorimotor rhythm neurofeedback and self-controlled practice enhance motor learning and performance in novice golfers. Behavioral Sciences, 13(1), 65. https://doi.org/10.3390/bs13010065
Rajaby, E., & Sayedi, S. M. (2022). A structured review of sparse fast Fourier transform algorithms. Digital Signal Processing, 123, 103403. https://doi.org/10.1016/j.dsp.2022.103403
Ribeiro, T. F., Carriello, M. A., de Paula Jr, E. P., Garcia, A. C., Rocha, G. L. D., & Teive, H. A. G. (2023). Clinical applications of neurofeedback based on sensorimotor rhythm: A systematic review and meta-analysis. Frontiers in neuroscience, 17, 1195066. https://doi.org/10.3389/fnins.2023.1195066
Suhaimi, N. S., Mountstephens, J., & Teo, J. (2022). A dataset for emotion recognition using virtual reality and EEG (DER-VREEG): Emotional state classification using low-cost wearable VR-EEG headsets. Big Data and Cognitive Computing, 6(1), 16. https://doi.org/10.3390/bdcc6010016
Tan, W., Xu, Y., Liu, P., Liu, C., Li, Y., Du, Y., ... & Zhang, Y. (2020). A method of VR-EEG scene cognitive rehabilitation training. Health information science and systems, 9(1), 4. https://doi.org/10.1007/s13755-020-00137-1
Tosti, B., Corrado, S., Mancone, S., Di Libero, T., Rodio, A., Andrade, A., & Diotaiuti, P. (2024). Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: A narrative review. Frontiers in Neuroscience, 18, 1358481. https://doi.org/10.3389/fnins.2024.1358481
Zaferiou, A., Hirsch, Z., Bacani, T., & Dahl, L. (2025). A review of concurrent sonified biofeedback in balance and gait training. Journal of NeuroEngineering and Rehabilitation, 22(1), 38. https://doi.org/10.1186/s12984-025-01565-4
Zoefel, B., Huster, R. J., & Herrmann, C. S. (2011). Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage, 54(2), 1427-1431. https://doi.org/10.1016/j.neuroimage.2010.08.078.
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