Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.

IF 2.8 3区 医学 Q3 NEUROSCIENCES
Robert Finnis, Adeel Mehmood, Henning Holle, Jamshed Iqbal
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Abstract

Brain-Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function Near-Infrared Spectroscopy (fNIRS) in decoding Motor Imagery (MI) movements for both offline and online BCI systems. EEG has been the dominant non-invasive neuroimaging modality due to its high temporal resolution and accessibility; however, it is limited by high susceptibility to electrical noise and motion artifacts, particularly in real-world settings. fNIRS offers improved robustness to electrical and motion noise, making it increasingly viable in prosthetic control tasks; however, it has an inherent physiological delay. The review categorizes experimental approaches based on modality, paradigm, and study type, highlighting the methods used for signal acquisition, feature extraction, and classification. Results show that while offline studies achieve higher classification accuracy due to fewer time constraints and richer data processing, recent advancements in machine learning-particularly deep learning-have improved the feasibility of online MI decoding. Hybrid EEG-fNIRS systems further enhance performance by combining the temporal precision of EEG with the spatial specificity of fNIRS. Overall, the review finds that predicting online imagined movement is feasible, though still less reliable than motor execution, and continued improvements in neuroimaging integration and classification methods are essential for real-world BCI applications. Broader dissemination of recent advancements in MI-based BCI research is expected to stimulate further interdisciplinary collaboration among roboticists, neuroscientists, and clinicians, accelerating progress toward practical and transformative neuroprosthetic technologies.

脑机接口控制中想象运动的探索:近红外光谱和脑电图综述。
脑机接口(bci)为恢复运动功能提供了一种非侵入性途径,特别是对于肢体丧失的个体。本综述探讨了脑电图(EEG)和功能近红外光谱(fNIRS)在离线和在线BCI系统中解码运动图像(MI)运动的有效性。脑电图由于其高时间分辨率和可及性而成为主要的非侵入性神经成像方式;然而,它受到电噪声和运动伪影的高易感性的限制,特别是在现实环境中。fNIRS提高了对电气和运动噪声的鲁棒性,使其在假肢控制任务中越来越可行;然而,它有一个内在的生理延迟。本文根据模态、范式和研究类型对实验方法进行了分类,重点介绍了用于信号采集、特征提取和分类的方法。结果表明,虽然离线研究由于更少的时间限制和更丰富的数据处理而实现了更高的分类精度,但机器学习(特别是深度学习)的最新进展提高了在线MI解码的可行性。脑电-近红外光谱混合系统将脑电的时间精度与近红外光谱的空间特异性相结合,进一步提高了系统的性能。总体而言,该综述发现预测在线想象运动是可行的,尽管仍然不如运动执行可靠,并且神经成像整合和分类方法的持续改进对于现实世界的脑机接口应用至关重要。基于脑机接口的脑机接口研究最新进展的广泛传播有望刺激机器人专家、神经科学家和临床医生之间进一步的跨学科合作,加速向实用和变革性神经假肢技术的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Brain Sciences
Brain Sciences Neuroscience-General Neuroscience
CiteScore
4.80
自引率
9.10%
发文量
1472
审稿时长
18.71 days
期刊介绍: Brain Sciences (ISSN 2076-3425) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes and short communications in the areas of cognitive neuroscience, developmental neuroscience, molecular and cellular neuroscience, neural engineering, neuroimaging, neurolinguistics, neuropathy, systems neuroscience, and theoretical and computational neuroscience. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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