用脑电波控制机器人抓取，无需触碰，适合行动不便者使用。
EEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation
- 结合脑电解码与增强现实反馈，实现稳定意念操控
- 脑控抓取成功率97.2%，信息传输率达21.3 bit/min
- 适合残障人士的无接触辅助机器人应用
可靠的脑机接口（BCI）控制机器人可为运动障碍者提供直观、便捷的人机交互方式。然而，现有系统存在脑电信号噪声大、目标选择预设僵化、多数研究仅限仿真缺乏闭环验证等问题，限制了实际应用。为此，我们提出一个闭环脑电-增强现实-机器人系统，融合运动想象（MI）脑电解码、增强现实神经反馈与机器人抓取，实现零接触操作。采用14通道脑电头戴设备进行个体化运动想象校准，手机端增强现实界面支持多目标导航并提供方向一致的反馈以提升稳定性，机械臂结合决策输出与视觉位姿估计实现自主抓取。实验验证：运动想象训练准确率达93.1%，平均信息传输率（ITR）为14.8 bit/min；增强现实反馈显著提升持续控制能力（SCI = 0.210），ITR达21.3 bit/min，优于静态、假反馈及无AR基线；闭环抓取成功率达97.2%，效率高且用户主观控制体验良好。结果表明，增强现实反馈能有效稳定脑电控制，该框架可实现鲁棒的零接触抓取，推动辅助机器人应用与人机交互新范式的发展。
原文摘要 · Abstract (English)
Reliable brain-computer interface (BCI) control of robots provides an intuitive and accessible means of human-robot interaction, particularly valuable for individuals with motor impairments. However, existing BCI-Robot systems face major limitations: electroencephalography (EEG) signals are noisy and unstable, target selection is often predefined and inflexible, and most studies remain restricted to simulation without closed-loop validation. These issues hinder real-world deployment in assistive scenarios. To address them, we propose a closed-loop BCI-AR-Robot system that integrates motor imagery (MI)-based EEG decoding, augmented reality (AR) neurofeedback, and robotic grasping for zero-touch operation. A 14-channel EEG headset enabled individualized MI calibration, a smartphone-based AR interface supported multi-target navigation with direction-congruent feedback to enhance stability, and the robotic arm combined decision outputs with vision-based pose estimation for autonomous grasping. Experiments are conducted to validate the framework: MI training achieved 93.1 percent accuracy with an average information transfer rate (ITR) of 14.8 bit/min; AR neurofeedback significantly improved sustained control (SCI = 0.210) and achieved the highest ITR (21.3 bit/min) compared with static, sham, and no-AR baselines; and closed-loop grasping achieved a 97.2 percent success rate with good efficiency and strong user-reported control. These results show that AR feedback substantially stabilizes EEG-based control and that the proposed framework enables robust zero-touch grasping, advancing assistive robotic applications and future modes of human-robot interaction.
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