Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis

Hui Chang; Ruikai Cao; Yi Xuan Sheng; Zhiyong Wang; Pu Wang; Rong Xin; Xian Xian Yu; Honghai Liu

doi:10.1007/978-981-99-6483-3_23

Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis

Hui Chang
, Ruikai Cao
, Yi Xuan Sheng
, Zhiyong Wang
, Pu Wang
, Rong Xin
, Xian Xian Yu
, Honghai Liu^*

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Sun Yat-Sen University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The interaction of information between the brain and muscles is widely present in human activities. Brain-muscle coupling has been proven to quantify the amount of information transmission between the cortex and muscles. Motor dysfunction is a typical sequela of stroke. Accurate quantitative assessment of motor function is the basis for formulating rehabilitation strategies. The application of brain-muscle coupling analysis to stroke patients’ motor evaluation has received extensive attention from researchers. In this study, brain and muscle data were collected synchronously from 6 healthy subjects and 2 stroke patients during an upper limb iso-velocity motion paradigm. Linear brain-muscle coupling results were obtained based on the method of wavelet coherence. The results revealed that CMC during the dynamic force output process predominantly manifested in the gamma band. The CMC strength of stroke patients was significantly lower than that of healthy individuals. Furthermore, stroke patients exhibited a shift of CMC towards the lower frequency band (beta band). This study proves the effectiveness of brain-muscle coupling in quantifying stroke patients’ motor function and has certain value in promoting motor rehabilitation.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings
Editors	Huayong Yang, Jun Zou, Geng Yang, Xiaoping Ouyang, Honghai Liu, Zhiyong Wang, Zhouping Yin, Lianqing Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	256-267
Number of pages	12
ISBN (Print)	9789819964826
DOIs	https://doi.org/10.1007/978-981-99-6483-3_23
State	Published - 2023
Externally published	Yes
Event	16th International Conference on Intelligent Robotics and Applications, ICIRA 2023 - Hangzhou, China Duration: 5 Jul 2023 → 7 Jul 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14267 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Intelligent Robotics and Applications, ICIRA 2023
Country/Territory	China
City	Hangzhou
Period	5/07/23 → 7/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cortico-muscular coupling
Motor Rehabilitation
Stroke

Access to Document

10.1007/978-981-99-6483-3_23

Cite this

Chang, H., Cao, R., Sheng, Y. X., Wang, Z., Wang, P., Xin, R., Yu, X. X., & Liu, H. (2023). Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis. In H. Yang, J. Zou, G. Yang, X. Ouyang, H. Liu, Z. Wang, Z. Yin, & L. Liu (Eds.), Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings (pp. 256-267). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14267 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-6483-3_23

Chang, Hui ; Cao, Ruikai ; Sheng, Yi Xuan et al. / Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis. Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. editor / Huayong Yang ; Jun Zou ; Geng Yang ; Xiaoping Ouyang ; Honghai Liu ; Zhiyong Wang ; Zhouping Yin ; Lianqing Liu. Springer Science and Business Media Deutschland GmbH, 2023. pp. 256-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{07835aee4fda42d2a56a5da9065583e8,

title = "Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis",

abstract = "The interaction of information between the brain and muscles is widely present in human activities. Brain-muscle coupling has been proven to quantify the amount of information transmission between the cortex and muscles. Motor dysfunction is a typical sequela of stroke. Accurate quantitative assessment of motor function is the basis for formulating rehabilitation strategies. The application of brain-muscle coupling analysis to stroke patients{\textquoteright} motor evaluation has received extensive attention from researchers. In this study, brain and muscle data were collected synchronously from 6 healthy subjects and 2 stroke patients during an upper limb iso-velocity motion paradigm. Linear brain-muscle coupling results were obtained based on the method of wavelet coherence. The results revealed that CMC during the dynamic force output process predominantly manifested in the gamma band. The CMC strength of stroke patients was significantly lower than that of healthy individuals. Furthermore, stroke patients exhibited a shift of CMC towards the lower frequency band (beta band). This study proves the effectiveness of brain-muscle coupling in quantifying stroke patients{\textquoteright} motor function and has certain value in promoting motor rehabilitation.",

keywords = "Cortico-muscular coupling, Motor Rehabilitation, Stroke",

author = "Hui Chang and Ruikai Cao and Sheng, \{Yi Xuan\} and Zhiyong Wang and Pu Wang and Rong Xin and Yu, \{Xian Xian\} and Honghai Liu",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023 ; Conference date: 05-07-2023 Through 07-07-2023",

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publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "256--267",

editor = "Huayong Yang and Jun Zou and Geng Yang and Xiaoping Ouyang and Honghai Liu and Zhiyong Wang and Zhouping Yin and Lianqing Liu",

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Chang, H, Cao, R, Sheng, YX, Wang, Z, Wang, P, Xin, R, Yu, XX & Liu, H 2023, Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis. in H Yang, J Zou, G Yang, X Ouyang, H Liu, Z Wang, Z Yin & L Liu (eds), Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14267 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 256-267, 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023, Hangzhou, China, 5/07/23. https://doi.org/10.1007/978-981-99-6483-3_23

Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis. / Chang, Hui; Cao, Ruikai; Sheng, Yi Xuan et al.
Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. ed. / Huayong Yang; Jun Zou; Geng Yang; Xiaoping Ouyang; Honghai Liu; Zhiyong Wang; Zhouping Yin; Lianqing Liu. Springer Science and Business Media Deutschland GmbH, 2023. p. 256-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14267 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Chang, Hui

AU - Cao, Ruikai

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AU - Wang, Pu

AU - Xin, Rong

AU - Yu, Xian Xian

AU - Liu, Honghai

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N2 - The interaction of information between the brain and muscles is widely present in human activities. Brain-muscle coupling has been proven to quantify the amount of information transmission between the cortex and muscles. Motor dysfunction is a typical sequela of stroke. Accurate quantitative assessment of motor function is the basis for formulating rehabilitation strategies. The application of brain-muscle coupling analysis to stroke patients’ motor evaluation has received extensive attention from researchers. In this study, brain and muscle data were collected synchronously from 6 healthy subjects and 2 stroke patients during an upper limb iso-velocity motion paradigm. Linear brain-muscle coupling results were obtained based on the method of wavelet coherence. The results revealed that CMC during the dynamic force output process predominantly manifested in the gamma band. The CMC strength of stroke patients was significantly lower than that of healthy individuals. Furthermore, stroke patients exhibited a shift of CMC towards the lower frequency band (beta band). This study proves the effectiveness of brain-muscle coupling in quantifying stroke patients’ motor function and has certain value in promoting motor rehabilitation.

AB - The interaction of information between the brain and muscles is widely present in human activities. Brain-muscle coupling has been proven to quantify the amount of information transmission between the cortex and muscles. Motor dysfunction is a typical sequela of stroke. Accurate quantitative assessment of motor function is the basis for formulating rehabilitation strategies. The application of brain-muscle coupling analysis to stroke patients’ motor evaluation has received extensive attention from researchers. In this study, brain and muscle data were collected synchronously from 6 healthy subjects and 2 stroke patients during an upper limb iso-velocity motion paradigm. Linear brain-muscle coupling results were obtained based on the method of wavelet coherence. The results revealed that CMC during the dynamic force output process predominantly manifested in the gamma band. The CMC strength of stroke patients was significantly lower than that of healthy individuals. Furthermore, stroke patients exhibited a shift of CMC towards the lower frequency band (beta band). This study proves the effectiveness of brain-muscle coupling in quantifying stroke patients’ motor function and has certain value in promoting motor rehabilitation.

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Chang H, Cao R, Sheng YX, Wang Z, Wang P, Xin R et al. Post-Stroke Motor Function Assessment Based on Brain-Muscle Coupling Analysis. In Yang H, Zou J, Yang G, Ouyang X, Liu H, Wang Z, Yin Z, Liu L, editors, Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 256-267. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-6483-3_23