Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.

Current brain–computer interface (BCI) research predominantly focuses on decoding bilateral limb movements, whereas practical stroke rehabilitation typically involves unilateral upper limb control. Decoding unilateral multi-task motor attempts remains challenging due to overlapping cortical activations across different movement categories. To address this, we propose a Multi-View Cortical Muscle Graph Network (MVCMGNet), a novel architecture grounded in neurophysiological principles. MVCMGNet enhances decoding through three integrated approaches: multi-view graph convolutional block extracts discriminative spectral-spatial features; cortical muscular connection module identifies distinctive connectivity signatures for different movements; and a mixture of experts module robustly fuses these multi-modal features. Evaluated on a dataset comprising 45 chronic stroke patients, MVCMGNet demonstrated strong performance in the dual-task scenario, achieving a classification accuracy of 78.52%, while exhibiting moderate performance in the more challenging quad-task scenario, attaining a classification accuracy of 52.79%. This study demonstrates that cortical-muscular connectivity patterns can effectively decode multi-category motor attempts in unilateral limbs. Our findings confirm that the distinctiveness of action-specific neuromuscular patterns enhances decoding accuracy, providing valuable insights for future BCI research and supporting the feasibility of complex unilateral decoding tasks for clinical rehabilitation.