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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月27日

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  • Jiuxiang Song,
  • Nan Wang,
  • Zhaolin Li,
  • Xuemin Zhang,
  • Zeping Lv,
  • Xinying Shan,
  • Yi Yang,
  • Jizhong Liu,
  • Xiaoke Chai

摘要

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.