Teng Jian, Cho Sukyoung, Lee Shaw-Mung
School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang, China.
College of Education, Sehan University, Yeongam, Jeollanam-do, Republic of Korea.
Front Neurorobot. 2025 Aug 14;19:1628968. doi: 10.3389/fnbot.2025.1628968. eCollection 2025.
Brain-computer interface (BCI) integration with virtual reality (VR) has progressed from single-limb control to multi-limb coordination, yet achieving intuitive tri-manual operation remains challenging. This study presents a consumer-grade hybrid BCI-VR framework enabling simultaneous control of two biological hands and a virtual third limb through integration of Tobii eye-tracking, NeuroSky single-channel EEG, and non-haptic controllers. The system employs e-Sense attention thresholds (>80% for 300 ms) to trigger virtual hand activation combined with gaze-driven targeting within 45° visual cones. A soft maximum weighted arbitration algorithm resolves spatiotemporal conflicts between manual and virtual inputs with 92.4% success rate. Experimental validation with eight participants across 160 trials demonstrated 87.5% virtual hand success rate and 41% spatial error reduction ( = 0.23 mm vs. 0.39 mm) compared to traditional dual-hand control. The framework achieved 320 ms activation latency and 22% NASA-TLX workload reduction through adaptive cognitive load management. Time-frequency analysis revealed characteristic beta-band (15-20 Hz) energy modulations during successful virtual limb control, providing neurophysiological evidence for attention-mediated supernumerary limb embodiment. These findings demonstrate that sophisticated algorithmic approaches can compensate for consumer-grade hardware limitations, enabling laboratory-grade precision in accessible tri-manual VR applications for rehabilitation, training, and assistive technologies.
脑机接口(BCI)与虚拟现实(VR)的整合已从单肢体控制发展到多肢体协调,但实现直观的三手动操作仍然具有挑战性。本研究提出了一种消费级混合BCI-VR框架,通过整合托比眼动追踪、神念单通道脑电图和非触觉控制器,实现对两只生物手和一个虚拟第三肢体的同时控制。该系统采用电子感知注意力阈值(300毫秒内>80%)来触发虚拟手激活,并结合在45°视锥内的注视驱动目标定位。一种软最大加权仲裁算法解决了手动和虚拟输入之间的时空冲突,成功率为92.4%。对8名参与者进行的160次试验的实验验证表明,与传统双手控制相比,虚拟手成功率为87.5%,空间误差减少41%(从0.39毫米降至0.23毫米)。该框架通过自适应认知负荷管理实现了320毫秒的激活延迟和22%的NASA-TLX工作量减少。时频分析揭示了成功进行虚拟肢体控制期间特征性的β波段(15-20赫兹)能量调制,为注意力介导的多余肢体体现提供了神经生理学证据。这些发现表明,复杂的算法方法可以弥补消费级硬件的局限性,在用于康复、训练和辅助技术的可及三手动VR应用中实现实验室级精度。