上肢多关节复合运动中的皮质-肌肉耦合:多通道脑电图和肌电图研究

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yingying Hao;Tingting Shen;Juan Wang;Jinyuan Zhang;Gengsheng Mao;Ping Xie;Xiaoling Chen
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引用次数: 0

摘要

上肢运动控制涉及肩、肘、腕等多个关节的协调运动,涉及复杂的脑-肢神经相互作用。本研究从皮质-肌肉耦合的角度探讨了这些任务中大脑和肌肉之间的关系。采用多元全局同步指数,测量了健康受试者右上肢多关节复合运动不同阶段多通道脑电图(EEG)和肌电图(EMG)信号的耦合。我们的研究结果表明,在同侧任务中,不同肌肉和大脑之间的β带耦合存在显著差异。此外,与静态任务相比,在协调任务中动态力表现出更高的全局同步指数。此外,静态任务时大脑与近端和远端肌肉群的整体同步指数比动态任务时更强。这些发现加深了我们对多关节复合运动中脑-肢相互作用机制的理解,并为探索肌肉群相互作用的差异提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Corticomuscular Coupling During Upper Limb Multijoint Composite Movement: A Multichannel EEG and EMG Study
The upper limb movement control involves the coordinated motion of multiple joints, including the shoulder, elbow, and wrist, and encompasses complex brain–limb neural interactions. This study explored the relationship between the brain and muscles during such tasks from the perspective of corticomuscular coupling. Employing the multivariate global synchronization index, we measured the coupling of multichannel electroencephalogram (EEG) and electromyography (EMG) signals during different stages of a right upper limb multijoint composite motion in healthy subjects. Our findings reveal that significant differences in beta band coupling were observed between different muscles and the brain during the ipsilateral task. Additionally, dynamic forces during coordination tasks exhibited significantly higher global synchronization indices compared to static tasks. Furthermore, the ratio of global synchronization index between the brain and proximal and distal muscle groups is stronger during static tasks than that in dynamic tasks. These findings deepen our understanding of the brain–limb interaction mechanisms during multijoint composite motion and provide opportunities to explore differences in muscle group interactions.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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