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基于感觉运动节律的脑机接口在中风后手上肢康复运动任务中的应用:一项系统综述。

Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review.

作者信息

Fu Jianghong, Chen Shugeng, Jia Jie

机构信息

Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China.

National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China.

出版信息

Brain Sci. 2022 Dec 28;13(1):56. doi: 10.3390/brainsci13010056.

Abstract

Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.

摘要

脑机接口(BCIs)在神经康复领域越来越受欢迎,感觉运动节律(SMR)是一种脑振荡节律,可在脑机接口中被捕获和分析。以往的综述证实了脑机接口的有效性,但很少详细讨论脑机接口实验中采用的运动任务,以及反馈是否适合这些任务。我们聚焦于基于感觉运动节律的脑机接口所采用的运动任务及其相应反馈,在PubMed、Embase、Cochrane图书馆、科学网和Scopus中检索文章,共找到442篇。经过一系列筛选,有15项随机对照研究符合分析条件。我们发现运动想象(MI)或运动尝试(MA)是基于脑电图的脑机接口试验中常见的实验范式。想象/尝试抓握和伸展手指是最常见的,还有包括手腕、肘部和肩部的多关节运动。在抓握和伸展手部的运动想象或运动尝试任务中有各种类型的反馈。本体感觉以多种形式被更频繁地使用。矫形器、机器人、外骨骼和功能性电刺激可辅助瘫痪肢体运动,视觉反馈可作为主要反馈或组合形式。然而,在恢复过程中,手部恢复存在许多瓶颈问题,如弛缓性麻痹或手指张开。在实践中,我们应主要关注患者的困难,在机器人、功能性电刺激或其他组合反馈的辅助下,为患者设计一个或多个运动任务,以帮助他们完成抓握、手指伸展、拇指对掌或其他动作。未来的研究应关注神经生理变化和功能改善,并进一步阐述运动功能恢复过程中的神经生理变化。

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EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application.
Front Med. 2021 Oct;15(5):740-749. doi: 10.1007/s11684-020-0794-5. Epub 2021 Jun 22.
4
Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review.
J Neuroeng Rehabil. 2021 Jan 23;18(1):15. doi: 10.1186/s12984-021-00820-8.
6
BCI-Based Rehabilitation on the Stroke in Sequela Stage.
Neural Plast. 2020 Dec 13;2020:8882764. doi: 10.1155/2020/8882764. eCollection 2020.
9
Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke.
IEEE Trans Biomed Eng. 2020 Dec;67(12):3339-3351. doi: 10.1109/TBME.2020.2984003. Epub 2020 Nov 19.
10
Action Observation of Own Hand Movement Enhances Event-Related Desynchronization.
IEEE Trans Neural Syst Rehabil Eng. 2019 Jul;27(7):1407-1415. doi: 10.1109/TNSRE.2019.2919194. Epub 2019 May 27.

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