TY - GEN
T1 - Integrated Analysis of Cortico-Muscular Coupling and Muscle Synergy for Functional Assessment in Exoskeleton-Assisted Stroke Rehabilitation
AU - Feng, Siyu
AU - Kuang, Qi
AU - Cao, Ruikai
AU - Wang, Zhuoqun
AU - Sheng, Yixuan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Quantitative assessment of motor function is critical for guiding rehabilitation in stroke patients. This study presents a framework that integrates cortico-muscular coupling (CMC) and muscle synergy analysis to evaluate and predict motor impairment levels during a standardized exoskeleton-assisted knee flexion-extension task. Muscle synergies were extracted from surface EMG signals and further characterized by cosine similarity and scalar descriptors. CMC was estimated using frequency domain time evolution (FDTE), distinguishing afferent and efferent cortical-muscular pathways. Group-level FDTE analysis revealed that patients exhibited elevated bidirectional CMC compared to healthy controls, suggesting higher cortical afferent demands and enhanced feedback information caused by compensatory mechanisms in stroke patients. By integrating these features, a support vector regression (SVR) model was trained to predict individual FMA scores, achieving high accuracy (R2 = 0.9589, MSE = 1.91). These results highlight the utility of combining neural coupling and synergy structure analysis during robotic-assisted movement to support objective, data-driven functional assessment in stroke rehabilitation.
AB - Quantitative assessment of motor function is critical for guiding rehabilitation in stroke patients. This study presents a framework that integrates cortico-muscular coupling (CMC) and muscle synergy analysis to evaluate and predict motor impairment levels during a standardized exoskeleton-assisted knee flexion-extension task. Muscle synergies were extracted from surface EMG signals and further characterized by cosine similarity and scalar descriptors. CMC was estimated using frequency domain time evolution (FDTE), distinguishing afferent and efferent cortical-muscular pathways. Group-level FDTE analysis revealed that patients exhibited elevated bidirectional CMC compared to healthy controls, suggesting higher cortical afferent demands and enhanced feedback information caused by compensatory mechanisms in stroke patients. By integrating these features, a support vector regression (SVR) model was trained to predict individual FMA scores, achieving high accuracy (R2 = 0.9589, MSE = 1.91). These results highlight the utility of combining neural coupling and synergy structure analysis during robotic-assisted movement to support objective, data-driven functional assessment in stroke rehabilitation.
KW - Cortico-muscular coupling
KW - Exoskeleton-Assisted Rehabilitation
KW - Motor function assessment
KW - Muscle synergy
UR - https://www.scopus.com/pages/publications/105020812853
U2 - 10.1007/978-981-95-2101-2_24
DO - 10.1007/978-981-95-2101-2_24
M3 - 会议稿件
AN - SCOPUS:105020812853
SN - 9789819521005
T3 - Lecture Notes in Computer Science
SP - 287
EP - 297
BT - Intelligent Robotics and Applications - 18th International Conference, ICIRA 2025, Proceedings
A2 - Matsuno, Takayuki
A2 - Liu, Honghai
A2 - Liu, Lianqing
A2 - Yin, Zhouping
A2 - Zhu, Xiangyang
A2 - Ren, Weihong
A2 - Wang, Zhiyong
A2 - Sheng, Yixuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Intelligent Robotics and Applications, ICIRA 2025
Y2 - 6 August 2025 through 9 August 2025
ER -