用脑电波直接控制机器人抓取和放置物体，无需外部提示。
Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery
- 通过视觉与运动想象的双通道脑电解码，实现意图驱动控制。
- 在线解码准确率达40.23%（视觉）和62.59%（运动），任务成功率20.88%。
- 纯想象模式下运行，适合残障人士或人机协同场景使用。
我们提出一个将基于脑电图（EEG）的视觉想象（VI）与运动想象（MI）结合的框架，实现实时、意图驱动的机器人抓取与放置。受脑机接口（BCI）增强人机交互潜力的启发，该系统在零样本条件下，将离线预训练解码器部署于在线流式处理管道中，构建双通道意图接口：视觉想象用于识别待抓取物体，运动想象用于确定放置姿态，从而直观控制抓什么、放哪里。系统仅依赖脑电信号，采用无提示想象协议，完成集成与在线验证。在Base机器人平台上评估，涵盖遮挡目标或不同体位等多样场景，实现在线解码准确率分别为40.23%（VI）和62.59%（MI），端到端任务成功率为20.88%。结果表明，高阶视觉认知可实时解码并转化为可执行机器人指令，弥合神经信号与物理交互之间的鸿沟，验证了纯想象型BCI范式的实用性与灵活性。
原文摘要 · Abstract (English)
We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement. Motivated by the promise of BCI-driven robotics to enhance human-robot interaction, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner within an online streaming pipeline. This establishes a dual-channel intent interface that translates visual intent into robotic actions, with VI identifying objects for grasping and MI determining placement poses, enabling intuitive control over both what to grasp and where to place. The system operates solely on EEG via a cue-free imagery protocol, achieving integration and online validation. Implemented on a Base robotic platform and evaluated across diverse scenarios, including occluded targets or varying participant postures, the system achieves online decoding accuracies of 40.23% (VI) and 62.59% (MI), with an end-to-end task success rate of 20.88%. These results demonstrate that high-level visual cognition can be decoded in real time and translated into executable robot commands, bridging the gap between neural signals and physical interaction, and validating the flexibility of a purely imagery-based BCI paradigm for practical human-robot collaboration.
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