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To address the issues of unnatural interaction, screen fixation, and visual fatigue in traditional SSVEP-based brain-controlled exoskeletons, this paper proposes an SSMVEP-based system enhanced by mixed reality (MR). A ring-shaped motion checkerboard paradigm is used for visual stimulation, combined with real-time EEG signal processing and gait control algorithms to enable low-fatigue, high-accuracy interaction. Experiments show that in a 6-target classification task, 5-second SSMVEP signals achieve 96.11% accuracy, with visual fatigue reduced by 35% compared to traditional SSVEP. In online exoskeleton control, the system achieves a 97.7% success rate in complex gait transitions. This system offers a practical and efficient rehabilitation solution for lower-limb motor impairments, demonstrating the potential of SSMVEP in real-time brain-controlled exoskeleton applications.

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A Mixed Reality-Based SSMVEP Brain-Computer Interface for Exoskeletons

  • 会议论文

  • 发表日期：

    2026年

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  • Xiuyuan Wu,
  • Yichen Lin,
  • Xinyang Du,
  • Zengle Ren,
  • Wujing Cao,
  • Meng Yin,
  • Yue Ma

摘要

To address the issues of unnatural interaction, screen fixation, and visual fatigue in traditional SSVEP-based brain-controlled exoskeletons, this paper proposes an SSMVEP-based system enhanced by mixed reality (MR). A ring-shaped motion checkerboard paradigm is used for visual stimulation, combined with real-time EEG signal processing and gait control algorithms to enable low-fatigue, high-accuracy interaction. Experiments show that in a 6-target classification task, 5-second SSMVEP signals achieve 96.11% accuracy, with visual fatigue reduced by 35% compared to traditional SSVEP. In online exoskeleton control, the system achieves a 97.7% success rate in complex gait transitions. This system offers a practical and efficient rehabilitation solution for lower-limb motor impairments, demonstrating the potential of SSMVEP in real-time brain-controlled exoskeleton applications.