Multiscale Intermuscular Coupling Analysis via Complex Network-Based High-Order O-Information
Intermuscular coupling analysis (IMC) provides important clues for understanding human muscle motion control and serves as a valuable reference for the rehabilitation assessment of stroke patients. However, the higher-order interactions and microscopic characteristics implied in IMC are not fully un...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10821496/ |
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| Summary: | Intermuscular coupling analysis (IMC) provides important clues for understanding human muscle motion control and serves as a valuable reference for the rehabilitation assessment of stroke patients. However, the higher-order interactions and microscopic characteristics implied in IMC are not fully understood. This study introduced a multiscale intermuscular coupling analysis framework based on complex networks with O-Information (Information About Organizational Structure). In addition, to introduce microscopic neural information, sEMG signals were decomposed to obtain motor units (MU). We applied this framework to data collected from experiments on three different upper limb movements. Graph theory-based analysis revealed significant differences in muscle network connectivity across the various upper limb movement tasks. Furthermore, the community division based on MU showed a mismatch between the distribution of muscle and motor neuron inputs, with a reduction in the dimension of motor unit control during multi-joint activity tasks. O-Information was used to explore higher-order interactions in the network. The analysis of redundant and synergistic information within the network indicated that numerous low-order synergistic subsystems were present while sEMG networks and MU networks were predominantly characterized by redundant information. Moreover, the graph features of macroscopic and microscopic network exhibit promising classification accuracy under KNN, showing the potential for engineering applications of the proposed framework. |
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| ISSN: | 1534-4320 1558-0210 |