1. X. Yu and X. Yu, “Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control,” IEEE Trans. Cybern., vol. 55, no. 9, Sep. 2025. https://doi.org/10.1109/TCYB.2025.3580726.
2. M. Du et al., “A Method for Improving Online Active Engagement During Lower Limb Rehabilitation Training Based on EEG Signals,” IEEE Trans. Human Mach. Syst., vol. 55, no. 4, Aug. 2025. https://doi.org/10.1109/THMS.2025.3569194.
3. L. Ding et al., “Resting-state EEG associated with clinical measures to predict upper limb motor recovery of subacute stroke,” Front. Neurol., Aug. 2025. https://doi.org/10.3389/fneur.2025.1577393.
4. Y. Huang, L. Cao, Y. Chen, and T. Wang, “Optimization of Dynamic SSVEP Paradigms for Practical Application: Low-Fatigue Design with Coordinated Trajectory and Speed Modulation and Gaming Validation,” Sensors, vol. 25, no. 15, Jul. 2025. https://doi.org/10.3390/s25154727.
5. X. Tian et al., “The clinical efficacy and mechanism of gamma frequency electroacupuncture stimulation on the rehabilitation of upper limb motor function in stroke patients: study protocol of a randomized clinical trial,” Front. Neurol., May 2025. https://doi.org/10.3389/fneur.2025.1603522.
6. J. Zhang et al., “Effect of transcranial magnetic stimulation combined with transcutaneous auricular vagus nerve stimulation on mild cognitive impairment: a study protocol for a randomized controlled trial,” Front. Aging Neurosci., vol. 17, May 2025. https://doi.org/10.3389/fnagi.2025.1600921.
7. X. Zhang et al., “Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation,” J. NeuroEngineering Rehabil., vol. 22, p. 97, Apr. 2025. https://doi.org/10.1186/s12984-025-01627-7.
8. Z. Yin et al., “A Wearable Multisensor Fusion System for Neuroprosthetic Hand,” IEEE Sens. J., vol. 25, no. 8, pp. 12547-12558, Apr. 2025. https://doi.org/10.1109/JSEN.2025.3546214.
9. S. Zhao, Z. Bao, and X. Shou, “Investigation of Proprioceptive Responses to Passive Lower Limb Movements Based on Corticokinematic Coherence,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 33, 2025. https://doi.org/10.1109/TNSRE.2025.3583668.
10. X. Cai et al., “A Novel Brain-Computer Interface Application: Precise Decoding of Urination and Defecation Motor Attempts in Spinal Cord Injury Patients,” IEEE Trans. Neural Syst. Rehabil. Eng., 2025. https://doi.org/10.1109/TNSRE.2025.3637066.
11. Y. Zhong, L. Yao, and Y. Wang, “Enhanced BCI Performance using Diffusion Model for EEG Generation,” in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, Jul. 2024. https://doi.org/10.1109/EMBC53108.2024.10782900.
12. M. Li et al., “Electroacupuncture alters brain network functional connectivity in subacute stroke: A randomized crossover trial,” Medicine, vol. 103, no. 14, p. e37686, Apr. 2024. https://doi.org/10.1097/MD.0000000000037686.
13. Z. Wang, B. Geng, X. Duan, Y. Mao, and Q. Yang, “A Genetic Algorithm-Based Multi-Parameter Method for Asynchronous Working State Classification Under SSVEP,” IEEE Access, vol. 12, pp. 53828-53837, 2024. https://doi.org/10.1109/ACCESS.2024.3384378.
14. Y. Zhu et al., “A flexible, stable, semi-dry electrode with low impedance for electroencephalography recording,” RSC Adv., vol. 14, no. 46, pp. 34415-34427, 2024. https://doi.org/10.1039/D4RA05560H.
15. M. Li, S. Zheng, W. Zou, H. Li, C. Wang, and L. Peng, “Electroencephalography-based parietofrontal connectivity modulated by electroacupuncture for predicting upper limb motor recovery in subacute stroke,” Medicine, vol. 102, no. 36, Sep. 2023. https://doi.org/10.1097/MD.0000000000034886.
16. J. Fu et al., “Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study,” Front. Neurosci., vol. 17, May 2023. https://doi.org/10.3389/fnins.2023.1146146.
17. J. Fu, Z. Jiang, X. Shu, S. Chen, and J. Jia, “Correlation between the ERD in grasp open tasks of BCIs and hand function of stroke patients: a cross-sectional study,” BioMed Eng. OnLine, vol. 22, p. 36, Apr. 2023. https://doi.org/10.1186/s12938-023-01091-1.
18. M. Ji, S. Shi, M. Zhu, S. Li, and J. Meng, “A Brain-Controlled Spherical Robot Based on Augmented Reality (AR),” in Intelligent Robotics and Applications, Singapore: Springer, 2023, pp. 343-352. https://doi.org/10.1007/978-981-99-6486-4_30.
19. Z. Yin et al., “A Wearable Ultrasound Interface for Prosthetic Hand Control,” IEEE J. Biomed. Health Inform., vol. 26, no. 11, Nov. 2022. https://doi.org/10.1109/JBHI.2022.3203084.
20. Z. Yin et al., “Wearable Ultrasound Interface for Prosthetic Hand Manipulation,” in Intelligent Robotics and Applications, Cham: Springer, 2022, pp. 3-12. https://doi.org/10.1007/978-3-031-13835-5_1.
21. J. Duan, S. Li, L. Ling, N. Zhang, and J. Meng, “Exploring the effects of head movements and accompanying gaze fixation switch on steady-state visual evoked potential,” Front. Hum. Neurosci., Sep. 2022. https://doi.org/10.3389/fnhum.2022.943070.
22. Z. Qin, Y. Xu, X. Shu, L. Hua, X. Sheng, and X. Zhu, “eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation,” in 2019 9th International IEEE EMBS Conference on Neural Engineering (NER), Mar. 2019, pp. 734-737. https://doi.org/10.1109/NER.2019.8716940.
23. Zhonghao Yao, ULMEE Dataset. IEEE DataPort. https://doi.org/10.21227/EFJN-D211.