Corticomuscular Coupling Analysis in Stroke Rehabilitation Based on Variational Mode Decomposition-Transfer Entropy

• Κύριοι συγγραφείς: Ting Wang (Συγγραφέας), Jianpeng Tang (Συγγραφέας), Xugang Xi (Συγγραφέας), Yong Peng (Συγγραφέας), Maofeng Wang (Συγγραφέας), Lihua Li (Συγγραφέας)
• Μορφή: Βιβλίο
• Έκδοση: IEEE, 2024-01-01T00:00:00Z.
• Θέματα: article
• Διαθέσιμο Online: Connect to this object online.
• Notes: 1534-4320; 1558-0210; 10.1109/TNSRE.2024.3436077
• Keywords: Cortico-muscular functional network; electroencephalogram; electromyography; variational modal decomposition-transfer entropy; stroke rehabilitation; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3506-3514 (2024)

This study aims to explore alterations in corticomuscular and cortical coupling during the rehabilitation of stroke patients. We initiated the analysis by employing variational modal decomposition (VMD) on electromyography (EMG) data, followed by the application of VDM-transfer entropy (VMD-TE) to quantify the coupling strength between electroencephalogram (EEG) and EMG signals. Subsequently, we constructed the VMD-TE connection matrix and analyzed the clustering coefficient and small-world attributes within the cortico-muscular functional network (CMFN). Finally, a random forest algorithm was employed to extract features from the VMD-TE connection matrix across different rehabilitation periods. Beta waves in EEG were emerged as the key information carrier between the cortex and muscle, and the CMFN of patients with the beta frequency band has small-world characteristics. During rehabilitation, we observed a decrease in coupling between the initially affected motor cortex and muscle, accompanied by an increase in coupling between the frontal region and muscle. Our findings suggest potential neuro-remodeling in stroke patients after rehabilitation, with CFMN serving as a valuable metric for assessing cortico-muscular coupling.