Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review

作者：Wenjie Jin, Xinxin Zhu, Lifeng Qian, Cunshu Wu, Fan Yang, D Zhan, Zhaoyin Kang, Kaitao Luo, Dianhuai Meng, Guangxu Xu · 发表于：Frontiers in Computational Neuroscience · 年份：2024 · DOI：10.3389/fncom.2024.1431815 · 被引用次数：44 · 研究领域：EEG and Brain-Computer Interfaces、Neuroscience and Neural Engineering、Gaze Tracking and Assistive Technology

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...