{"title":"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","authors":"Yingying Hao;Tingting Shen;Juan Wang;Jinyuan Zhang;Gengsheng Mao;Ping Xie;Xiaoling Chen","doi":"10.1109/JSEN.2025.3538008","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 6","pages":"10046-10054"},"PeriodicalIF":4.3000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Corticomuscular Coupling During Upper Limb Multijoint Composite Movement: A Multichannel EEG and EMG Study\",\"authors\":\"Yingying Hao;Tingting Shen;Juan Wang;Jinyuan Zhang;Gengsheng Mao;Ping Xie;Xiaoling Chen\",\"doi\":\"10.1109/JSEN.2025.3538008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 6\",\"pages\":\"10046-10054\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10879418/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10879418/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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