Key result
Combining CMCSI, BNDSI, and DTW-EEG features from EEG and EMG signals achieved the highest accuracy in predicting motor function rehabilitation at different levels of stroke.
Why the study?
Changes in functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation, requiring methods to quantify these connections.
Can a combination of corticomuscular coupling and graph theory metrics predict motor function rehabilitation in stroke patients?
Observational (n=34)
Can a combination of corticomuscular coupling and graph theory metrics predict motor function rehabilitation in stroke patients?
A novel symmetry index based on graph theory and cortical muscle coupling shows potential as a biomarker for predicting stroke recovery.
May support EEG-EMG biomarkers for stroke recovery stratification; leaves open prospective validation before clinical use.
Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.
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Hua et al. (2023) conducted an observational in Stroke (n=34). Symmetry metrics based on graph theory and corticomuscular coupling (CMCSI, BNDSI, DTW-EEG) vs. Healthy individuals was evaluated on Prediction of motor function rehabilitation at different levels of stroke. Combining CMCSI, BNDSI, and DTW-EEG features from EEG and EMG signals achieved the highest accuracy in predicting motor function rehabilitation at different levels of stroke.
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