Abstract
Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical executio n. However, the exploration of functional connectivity (FC) and lateralization mechanisms under dif- ferent MI actions remains insufficiently understood. In this work, the common orthogonal basis extraction (COBE) algorithm was employed to isolate action-specific com- ponents by removing shared background components from the raw FC of the MI process. We demonstrate that action-specific FC effectively captures the hemi- spheric statistical differences between left- and right-hand MI, outperforming traditional FC and temporal variability measures. And through a comprehensive analysis of net- work properties at three distinct levels, encompassing the whole-brain network properties, hemispherical properties, and individual nodal strength, complex lateralization pat- terns associated with diverse types of MI processes were successfully discerned. Furthermore, lateralization indices were further calculated to quantitatively reveal the degree of brain lateralization. Notably, the lateralization perfor- mance (LP) derived from action-specific FC exhibited a significant predictive capacity for MI performance, thereby suggesting its potential to evaluate individual MI capabil- ity. Collectively, these findings validate the action-specific FC patterns in characterizing neural mechanisms of MI processes and indicate that the LP could potentially be a useful tool to predict the MI performance of MI-based brain-computer inference (BCI), thereby contributing to the formulation of personalized therapeutic strategies for clini- cal rehabilitation from a new perspective.
Authors
Wang, G., Jiang, L., Song, X., Zhang, Y., Yao, D., Lu, J., Xu, P., Li, F., & Liang, Y.
https://doi.org/10.1109/TNSRE.2025.3605612