基于脑电图的自适应闭环脑机接口在神经康复中的应用:综述。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2024-09-20 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1431815
Wenjie Jin, XinXin Zhu, Lifeng Qian, Cunshu Wu, Fan Yang, Daowei Zhan, Zhaoyin Kang, Kaitao Luo, Dianhuai Meng, Guangxu Xu
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引用次数: 0

摘要

脑机接口(BCI)是一种突破性的方法,它可以绕过传统的神经和肌肉通路,让有严重运动障碍的人直接进行交流。在种类繁多的 BCI 技术中,基于脑电图(EEG)的系统因其非侵入性、操作简便和成本效益高而备受青睐。最近的进步促进了自适应双向闭环生物识别(BCI)技术的发展,该技术可根据用户的大脑活动进行动态调整,从而提高神经康复的响应速度和疗效。这些系统支持实时调节和持续反馈,可根据用户的神经和行为反应进行个性化治疗干预。通过结合机器学习算法,这些 BCI 可优化用户互动,并通过依赖活动的神经可塑性机制促进康复效果。本文回顾了基于脑电图的自适应双向闭环 BCI 目前的发展状况,研究了它们在运动和感觉功能恢复方面的应用,以及在实际应用中遇到的挑战。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交往方面的潜力,同时也指出了未来研究的关键领域,旨在提高系统的适应性和性能。随着人工智能的不断进步,先进的生物识别(BCI)系统有望改变神经康复的现状,并扩大在各个领域的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.

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.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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