{"title":"适应握力动态变化的神经肌肉反应。","authors":"Bowen Xiao;Limeng Liu;Lin Chen;Xing Wang;Xin Zhang;Xiaoyu Liu;Wensheng Hou;Xiaoying Wu","doi":"10.1109/TNSRE.2024.3447062","DOIUrl":null,"url":null,"abstract":"Precise control of strength is of significant importance in upper limb functional rehabilitation. Understanding the neuro-muscular response in strength regulation can help optimize the rehabilitation prescriptions and facilitate the relative training process for recovery control. This study aimed to investigate the inherent characteristics of neural-muscular activity during dynamic hand strength adjustment. Four dynamic grip force tracking modes were set by manipulating different magnitude and speed of force variations, and thirteen healthy young individuals took participation in the experiment. Electroencephalography were recorded in the contralateral sensorimotor cortex area, as well as the electromyography from the first dorsal interosseous muscle were collected synchronously. The metrics of the Event-related desynchronization, the electromyography stability index, and the force variation, were used to represent the corresponding cortical neural responses, muscle contraction activities, and the level of strength regulation, respectively; and further neuro-muscular coupling between the sensorimotor cortex and the first dorsal interosseous muscle was investigated by transfer entropy analysis. The results indicated a strong relationship that the increase of force regulation demand would result in a force variation increase as well as a stability reduction in muscle motor unit output. Meanwhile, the intensity of neural response increased in both the \n<inline-formula> <tex-math>$\\alpha $ </tex-math></inline-formula>\n and \n<inline-formula> <tex-math>$\\beta $ </tex-math></inline-formula>\n frequency bands. As the force regulation demand increased, the strength of bidirectional transfer entropy showed a clear shift from \n<inline-formula> <tex-math>$\\beta $ </tex-math></inline-formula>\n to the \n<inline-formula> <tex-math>$\\gamma $ </tex-math></inline-formula>\n frequency band, which facilitate rapid integration of dynamic strength compensation to adapt to motor task changes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3189-3198"},"PeriodicalIF":4.8000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643225","citationCount":"0","resultStr":"{\"title\":\"Neuro-Muscular Responses Adaptation to Dynamic Changes in Grip Strength\",\"authors\":\"Bowen Xiao;Limeng Liu;Lin Chen;Xing Wang;Xin Zhang;Xiaoyu Liu;Wensheng Hou;Xiaoying Wu\",\"doi\":\"10.1109/TNSRE.2024.3447062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Precise control of strength is of significant importance in upper limb functional rehabilitation. Understanding the neuro-muscular response in strength regulation can help optimize the rehabilitation prescriptions and facilitate the relative training process for recovery control. This study aimed to investigate the inherent characteristics of neural-muscular activity during dynamic hand strength adjustment. Four dynamic grip force tracking modes were set by manipulating different magnitude and speed of force variations, and thirteen healthy young individuals took participation in the experiment. Electroencephalography were recorded in the contralateral sensorimotor cortex area, as well as the electromyography from the first dorsal interosseous muscle were collected synchronously. The metrics of the Event-related desynchronization, the electromyography stability index, and the force variation, were used to represent the corresponding cortical neural responses, muscle contraction activities, and the level of strength regulation, respectively; and further neuro-muscular coupling between the sensorimotor cortex and the first dorsal interosseous muscle was investigated by transfer entropy analysis. The results indicated a strong relationship that the increase of force regulation demand would result in a force variation increase as well as a stability reduction in muscle motor unit output. Meanwhile, the intensity of neural response increased in both the \\n<inline-formula> <tex-math>$\\\\alpha $ </tex-math></inline-formula>\\n and \\n<inline-formula> <tex-math>$\\\\beta $ </tex-math></inline-formula>\\n frequency bands. As the force regulation demand increased, the strength of bidirectional transfer entropy showed a clear shift from \\n<inline-formula> <tex-math>$\\\\beta $ </tex-math></inline-formula>\\n to the \\n<inline-formula> <tex-math>$\\\\gamma $ </tex-math></inline-formula>\\n frequency band, which facilitate rapid integration of dynamic strength compensation to adapt to motor task changes.\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"32 \",\"pages\":\"3189-3198\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643225\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10643225/\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10643225/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Neuro-Muscular Responses Adaptation to Dynamic Changes in Grip Strength
Precise control of strength is of significant importance in upper limb functional rehabilitation. Understanding the neuro-muscular response in strength regulation can help optimize the rehabilitation prescriptions and facilitate the relative training process for recovery control. This study aimed to investigate the inherent characteristics of neural-muscular activity during dynamic hand strength adjustment. Four dynamic grip force tracking modes were set by manipulating different magnitude and speed of force variations, and thirteen healthy young individuals took participation in the experiment. Electroencephalography were recorded in the contralateral sensorimotor cortex area, as well as the electromyography from the first dorsal interosseous muscle were collected synchronously. The metrics of the Event-related desynchronization, the electromyography stability index, and the force variation, were used to represent the corresponding cortical neural responses, muscle contraction activities, and the level of strength regulation, respectively; and further neuro-muscular coupling between the sensorimotor cortex and the first dorsal interosseous muscle was investigated by transfer entropy analysis. The results indicated a strong relationship that the increase of force regulation demand would result in a force variation increase as well as a stability reduction in muscle motor unit output. Meanwhile, the intensity of neural response increased in both the
$\alpha $
and
$\beta $
frequency bands. As the force regulation demand increased, the strength of bidirectional transfer entropy showed a clear shift from
$\beta $
to the
$\gamma $
frequency band, which facilitate rapid integration of dynamic strength compensation to adapt to motor task changes.
期刊介绍:
Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.