Analysis of Multiscale Corticomuscular Coupling Networks Based on Ordinal Patterns

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 1045 - 1054
• Main Authors: Liu, Lantian, Gao, Yunyuan, Meng, Ming, Houston, Michael, Zhang, Yingchun
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active control; Cerebral cortex; Cerebral Cortex - physiology; Coupling; Couplings; Decomposition; EEG; Electroencephalography; Electromyography; Force; functional corticomuscular coupling; granger causality; Grasping; Grip force; Grip strength; Hand; Humans; Inference algorithms; Information processing; Information transfer; Low frequencies; Mathematical analysis; Motion control; Multivariate variational modal decomposition; Muscle, Skeletal - physiology; Muscles; ordinal partition transition networks; Physiology; Time series analysis; Time-frequency analysis
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3337229

The coupled analysis of corticomuscular function based on physiological electrical signals can identify differences in causal relationships between electroencephalogram (EEG) and surface electromyogram (sEMG) in different motor states. The existing methods are mainly devoted to the analysis in the same frequency band, while ignoring the cross-band coupling, which plays an active role in motion control. Considering the inherent multiscale characteristics of physiological signals, a method combining Ordinal Partition Transition Networks (OPTNs) and Multivariate Variational Modal Decomposition (MVMD) was proposed in this paper. The EEG and sEMG were firstly decomposed on a time-frequency scale using MVMD, and then the coupling strength was calculated by the OPTNs to construct a corticomuscular coupling network, which was analyzed with complex network parameters. Experimental data were obtained from a self-acquired dataset consisting of EEG and sEMG of 16 healthy subjects at different sizes of constant grip force. The results showed that the method was superior in representing changes in the causal link among multichannel signals characterized by different frequency bands and grip strength patterns. Complex information transfer between the cerebral cortex and the corresponding muscle groups during constant grip force output from the human upper limb. Furthermore, the sEMG of the flexor digitorum superficialis (FDS) in the low frequency band is the hub in the effective information transmission between the cortex and the muscle, while the importance of each frequency component in this transmission network becomes more dispersed as the grip strength grows, and the increase in coupling strength and node status is mainly in the <inline-formula> <tex-math notation="LaTeX">\gamma </tex-math></inline-formula> band (30~60Hz). This study provides new ideas for deconstructing the mechanisms of neural control of muscle movements.