Prof. Chong Li's research team focuses on developing novel neurotechnology to address critical challenges in motor function recovery and human augmentation. His research includes biomechatronic engineering, brain-computer interfaces (BCI), and closed-loop neuromodulation to create next generation of rehabilitation technology and equipment. His team collaborate with stroke associations, hospitals and companies, facilitating the technology transfer of the research outputs from laboratory to rehabilitation industries. The following impact stories are the representative achievements.
Intelligent Rehabilitation Robotics
Prof. Li is pioneering an AI-driven, full-chain rehabilitation pathway that moves beyond conventional robotic assistance. By developing novel technologies for precise assessment, prognostic prediction, and intelligent prescription, his work enables personalized therapy for patients with neurological diseases. These innovations have led to award-winning systems at international exhibitions and successful translation.
Brain-Computer Interfaces
Prof. Li has innovated individualized BCI therapies tailored to patient-specific patterns of neural reorganization. This approach facilitates the reconstruction of impaired sensorimotor neural pathway. Clinical trials have confirmed that this personalized strategy leads to superior rehabilitation outcomes compared to conventional methods, an achievement recognized as the First Prize for Science and Technology Award by the Chinese Association of Rehabilitation Medicine.
Noninvasive Closed-Loop Neuromodulation
Prof. Li invents non-invasive close-loop neuromodulation techniques based on biological and behavioral characteristics. This innovation has been successfully applied to several clinical and daily scenarios from treating hemifacial spasms, mitigating motion sickness and enhancing sleep quality, improving patient’s quality of life and augmenting human performance.
1. [Qu, X., Wan, J., Zhao, H.,] Xu, S., Cheng, X., Yang, B., Li, Z., Ji, L., Wu, J.*, Li, Z.*, Cheng, J.* and Li, C*, 2026. Closed-loop wearable neurostimulation system with triboelectric sensing to alleviate hemifacial spasms. Nature Communications.
2. [Jia, T., Pan, F.,] Yang, X., Ji, L., Farina, D.* and Li, C*, 2025. Artificial Empathy in Therapy and Healthcare: Advancements in Interpersonal Interaction Technologies. Cyborg and Bionic Systems, 6, 0473.
3. [Wan, J., Xu, S., Lin, J.,] Ji, L., Cheng, J.*, Li, Z.*, Qu, X.* and Li, C.*, 2025. AI‐Enhanced Wearable Technology for Human Physiological Signal Detection: Challenges and Future Directions. Small, 21(43), p.e04078.
4. [Yang, B., Yang, L.,] Zhao, H., Pan, F., Cheng, X., Ji, L., Wang, X., Li, C.*, Li, W.*, Qu, X.* and Cheng, J.*, 2025. A visual-tactile synchronized stimulation ring system for sensory rehabilitation integrating triboelectric sensing and pneumatic feedback. Nano Energy, 135, p.110638.
5. [Jia, T., Sun, J.,] McGeady, C., Ji, L. and Li, C.*, 2024. Enhancing brain–computer interface performance by incorporating brain-to-brain coupling. Cyborg and Bionic Systems, 5, p.0116.
6. Yang, Y., Li, W., Chen, H., Wang, X., Ji, L., Zhou, B.* and Li, C.*, 2024. Closed-Loop Respiratory Intervention Enhances Sleep Ventilation and Oxygen Saturation in Healthy Participants With Rapid High-Altitude Exposure. IEEE Journal of Biomedical and Health Informatics.
7. [Jia, T., Mo, L.,] McGeady, C., Sun, J., Liu, A., Ji, L., Xi, J.* and Li, C.*, 2024. Cortical Activation Patterns Determine Effectiveness of rTMS-Induced Motor Imagery Decoding Enhancement in Stroke Patients. IEEE Transactions on Biomedical Engineering.
8. Sun, J., Jia, T., Lin, P.J., Li, Z., Ji, L. and Li, C.*, 2023. Multiscale canonical coherence for functional corticomuscular coupling analysis. IEEE Journal of Biomedical and Health Informatics, 28(2), pp.812-822.
9. [Jia, T., Li, C.*,] Mo, L., Qian, C., Li, W., Xu, Q., Pan, Y., Liu, A.* and Ji, L.*, 2023. Tailoring brain–machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients. Cerebral Cortex, 33(6), pp.3043-3052.
10. [Lin, P.J., Jia, T.,] Li, C.*, Li, T., Qian, C., Li, Z., Pan, Y. * and Ji, L., 2021. CNN-based prognosis of BCI rehabilitation using EEG from first session BCI training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, pp.1936-1943.
