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REVIEW article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1431815
This article is part of the Research Topic Passive Brain-Computer Interfaces: Toward an “Out of the Lab” Employment View all articles

Electroencephalogram-based adaptive bidirectional closed-loop braincomputer interface in neurorehabilitation: a review

Provisionally accepted
JIN WENJIE JIN WENJIE 1,2,3*Xinxin Zhu Xinxin Zhu 2,3*Lifeng Qian Lifeng Qian 2,3*Shu Wang Shu Wang 1Fan Yang Fan Yang 2,3*Daowei Zhan Daowei Zhan 2,3*Zhaoyin Kang Zhaoyin Kang 2,3*Kaitao Luo Kaitao Luo 2,3*Dianhuai Meng Dianhuai Meng 1,4*Guangxu Xu Guangxu Xu 1,4*
  • 1 Nanjing Medical University, Nanjing, China
  • 2 Jiaxing Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
  • 3 Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, Zhejiang, China
  • 4 Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: Neurorehabilitation, Brain-computer interface, BCI, Electroencephalography, bidirectional, closed-loop, Intelligence

    Received: 13 May 2024; Accepted: 10 Sep 2024.

    Copyright: © 2024 WENJIE, Zhu, Qian, Wang, Yang, Zhan, Kang, Luo, Meng and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    JIN WENJIE, Nanjing Medical University, Nanjing, China
    Xinxin Zhu, Jiaxing Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
    Lifeng Qian, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, 314000, Zhejiang, China
    Fan Yang, Jiaxing Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
    Daowei Zhan, Jiaxing Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
    Zhaoyin Kang, Jiaxing Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
    Kaitao Luo, Jiaxing Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
    Dianhuai Meng, Nanjing Medical University, Nanjing, China
    Guangxu Xu, Nanjing Medical University, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.