Jin Wenjie, Zhu XinXin, Qian Lifeng, Wu Cunshu, Yang Fan, Zhan Daowei, Kang Zhaoyin, Luo Kaitao, Meng Dianhuai, Xu Guangxu
Department of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China.
Rehabilitation Medicine Center, Zhejiang Chinese Medical University Affiliated Jiaxing TCM Hospital, Jiaxing, China.
Front Comput Neurosci. 2024 Sep 20;18:1431815. doi: 10.3389/fncom.2024.1431815. eCollection 2024.
Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.
脑机接口（BCIs）代表了一种开创性的方法，可为严重运动障碍患者实现直接通信，绕过传统的神经和肌肉通路。在各种各样的脑机接口技术中，基于脑电图（EEG）的系统因其非侵入性、用户友好的操作和成本效益而特别受到青睐。最近的进展推动了自适应双向闭环脑机接口的发展，这种接口可动态适应用户的大脑活动，从而提高神经康复中的反应能力和疗效。这些系统支持实时调制和持续反馈，促进与用户神经和行为反应相匹配的个性化治疗干预。通过纳入机器学习算法，这些脑机接口通过活动依赖性神经可塑性机制优化用户交互并促进恢复结果。本文综述了基于脑电图的自适应双向闭环脑机接口的当前状况，研究了它们在运动和感觉功能恢复中的应用以及实际实施中遇到的挑战。研究结果强调了这些技术在显著提高患者生活质量和社会互动方面的潜力，同时也确定了未来旨在提高系统适应性和性能的研究关键领域。随着人工智能的不断进步，复杂脑机接口系统的发展有望改变神经康复并扩展其在各个领域的应用。
