A hardware and software system for implementing a full cycle of neurorehabilitation based on a neurointerface with biofeedback

The study presents the development of a hardware-software system for neurorehabilitation based on motor imagery, implementing the full cycle of brain-computer interface (BCI) operation – from EEG acquisition and preprocessing to classification, multimodal feedback control, and generation of clinically oriented reports.

Materials and Methods: The system architecture includes patient and scenario databases, a data stream synchronization module, automatic removal of oculomotor artifacts using Independent Component Analysis (ICA), a classifier based on Riemannian geometry of covariance matrices, and an adaptive feature alignment mechanism to reduce inter-individual variability.

Results: The developed system ensures reliable real-time performance and generates reports including neurophysiological parameters (spectral power in delta, theta, alpha, and beta bands, ERD, lateralization index) and behavioral characteristics (motor imagery formation speed, number of successful imagery attempts). The system provides objective monitoring of patient’s condition and recovery dynamics, including assessment of changes in background rhythmic activity and functional responses of the sensorimotor cortex.

Conclusions: The proposed solution combines state-of-the-art algorithmic methods with clinical applicability, making it a promising tool for neurorehabilitation and cognitive-motor training.

1. Feigin VL, et al. World Stroke Organization: Global Stroke Fact Sheet 2025. International Journal of Stroke. 2025; 20(2): 132-144. doi: 10.1177/17474930241308142.

2. [Mokienko OA, Suponeva GA, Aziatskaya GA. Insul't u vzroslyh: central'nyj parez verhnej konechnosti. Klinicheskie rekomendacii. O.A. Mokienko, N.A. Suponevа, ed. M.: MEDpress-Inform. 2018. 222. (In Russ.)]

3. Joundi RA, et al. Magnitude and time-course of dementia risk in stroke survivors: a population-wide matched cohort study. Neurology. 2025; 104(1): e210131. doi: 10.1212/WNL.0000000000210131.

4. [Piradov MA, Hernikova NA, Suponeva AS, et. al. Perspektivy razvitiya robotizirovannyh ustrojstv dlya vosstanovleniya dvizhenij ruki. Roboticheskie tekhnologii v medicine. 2016: 122-130. (In Russ.)]

5. Khorev V, Kurkin S, Badarin A, et al. Review on the use of brain computer interface rehabilitation methods for treating mental and neurological conditions. Journal of Integrative Neuroscience. 2024; 23(7): 125. doi: 10.31083/j.jin2307125.

6. Hramov AE, Maksimenko VA, Pisarchik AN. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021; 918: 1-133. doi: 10.1016/j.physrep.2021.03.002.

7. [Frolov AA, Mokienko OA, Lyukmanov RH, et al. Predvaritel'nye rezul'taty kontroliruemogo issledovaniya effektivnosti tekhnologii IMK-ekzoskelet pri postinsul'tnom pareze ruki. Vestnik Rossijskogo gosudarstvennogo medicinskogo universiteta. 2016; 2: 17-2. (In Russ.)]

8. [Frolov AA, Chernikova LA, Lyukmanov RH, et. al. Ispol'zovanie medicinskoj tekhnologii «Neinvazivnyj interfejs mozg — komp'yuter — ekzoskelet kisti». Metodicheskie rekomendacii. 2016. (In Russ.)]

9. [Frolov AA, Aziatskaya GA, Bobrov PD, et al. Elektrofiziologicheskaya aktivnost' mozga pri upravlenii interfejsom mozg-komp'yuter, osnovannym na voobrazhenii dvizheniya. Fiziologiya cheloveka. 2017; 43(5): 17-25. (In Russ.)]

10. Alonso-Valerdi LM, Salido-Ruiz RA, Ramirez-Mendoza RA. Motor imagery based brain–computer interfaces: an emerging technology to rehabilitate motor deficits. Neuropsychologia. 2015; 79: 354-363. doi: 10.1016/j.neuropsychologia.2015.09.012.

11. Almufareh MF, Alsumayt A, Alageely AS, et al. Leveraging motor imagery rehabilitation for individuals with disabilities: a review. Healthcare. 2023; 11(19): 2681. doi: 10.3390/healthcare11192653.

12. [Antipov VM, Badarin AA, Kurkin SA, et al. Program-apparatus complex for rehabilitation of patients with cognitive and motor disorders. Vrach i informatsionnye tekhnologii. 2024; 4: 38-47. (In Russ.)] doi: 10.25881/18110193_2024_4_38.

13. [Antipov VM, Smirnov NM, Badarin AA, et al. Using brain–computer interfaces for personalized neurorehabilitation: the role of subjective perception and neurophysiological indicators. Vrach i informatsionnye tekhnologii. 2025; 2: 84-97. doi: 10.25881/18110193_2025_2_84. (In Russ.)]

14. Kothe C, Shirazi SY, Stenner T, et al. The lab streaming layer for synchronized multimodal recording. Imaging Neuroscience. 2025; 3: IMAG-a. doi: 10.1162/IMAG.a.136.

15. Yger F, Berar M, Lotte F. Riemannian approaches in brain-computer interfaces: a review. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2016; 25(10): 1753-1762. doi: 10.1109/TNSRE.2016.2627016.

16. Brokaw K, Tishler W, Manceor S, et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory. 2016; 130: 17-25. doi: 10.1016/j.nlm.2016.01.008.

17. Wan W, Gao Z, Zhang Q, et al. Resting state EEG complexity as a predictor of cognitive performance // Physica A: Statistical Mechanics and its Applications. 2023; 624: 128952. doi: 10.1016/j.physa.2023.128952.

18. Nam CS, Jeon Y, Kim YJ, Lee., Park K. Movement imagery-related lateralization of event-related (de) synchronization (ERD/ERS): motor-imagery duration effects. Clinical Neurophysiology. 2011; 122(3): 567-577. doi: 10.1016/j.clinph.2010.08.002.

19. Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Annals of Neurology. 2013; 74(1): 100-108. doi: 10.1002/ana.23879.

20. Cervera MA, Soekadar SR, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of Clinical and Translational Neurology. 2018; 5(5): 651-663. doi: 10.1002/acn3.544.

21. Yang W, Zhang X, Li Z, et al. The effect of brain-computer interface training on rehabilitation of upper limb dysfunction after stroke: a meta-analysis of randomized controlled trials. Frontiers in Neuroscience. 2022; 15: 766879. doi: 10.3389/fnins.2021.766879.

22. Hu Y, Li Y, Leung AY, et al. A scoping review on motor imagery-based rehabilitation: Potential working mechanisms and clinical application for cognitive function and depression. Clinical Rehabilitation. 2025; 39(4): 504-523. doi: 10.1177/02692155241303259.

23. Gu J, Wang X, Liu C, et al. Cognitive and neural mechanisms of mental imagery supporting creative cognition. Communications Biology. 2025; 8: 1386. doi: 10.1038/s42003-025-08513-x.