Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, People's Republic of China.
J Neural Eng. 2019 Apr;16(2):026012. doi: 10.1088/1741-2552/aaf594. Epub 2018 Dec 3.
Recent attempts in developing brain-computer interface (BCI)-controlled robots have shown the potential of this area in the field of assistive robots. However, implementing the process of picking and placing objects using a BCI-controlled robotic arm still remains challenging. BCI performance, system portability, and user comfort need to be further improved.
In this study, a novel control approach, which combines high-frequency steady-state visual evoked potential (SSVEP)-based BCI and computer vision-based object recognition, is proposed to control a robotic arm for performing pick and place tasks that require control with multiple degrees of freedom. The computer vision can identify objects in the workspace and locate their positions, while the BCI allows the user to select one of these objects to be acted upon by the robotic arm. The robotic arm was programmed to be able to autonomously pick up and place the selected target object without moment-by-moment supervision by the user.
Online results obtained from ten healthy subjects indicated that a BCI command for the proposed system could be selected from four possible choices in 6.5 s (i.e. 2.25 s for visual stimulation and 4.25 s for gaze shifting) with 97.75% accuracy. All subjects could successfully complete the pick and place tasks using the proposed system.
These results demonstrated the feasibility and efficiency of combining high-frequency SSVEP-based BCI and computer vision-based object recognition to control robotic arms. The control strategy presented here could be extended to control robotic arms to perform other complicated tasks.
最近开发脑机接口（BCI）控制机器人的尝试表明，该领域在辅助机器人领域具有潜力。然而，使用 BCI 控制机械臂实现物体的拾取和放置过程仍然具有挑战性。BCI 性能、系统便携性和用户舒适度需要进一步提高。
在这项研究中，提出了一种新的控制方法，将基于高频稳态视觉诱发电位（SSVEP）的 BCI 和基于计算机视觉的物体识别相结合，用于控制机械臂执行需要多自由度控制的拾取和放置任务。计算机视觉可以识别工作空间中的物体并定位它们的位置，而 BCI 允许用户选择其中一个物体由机械臂进行操作。机械臂被编程为能够自主拾取和放置所选目标物体，而无需用户时刻监督。
从十位健康受试者获得的在线结果表明，在 6.5 秒内（即视觉刺激 2.25 秒和注视转移 4.25 秒），可以从四个可能的选择中选择用于提出系统的 BCI 命令，准确率为 97.75%。所有受试者都可以成功使用提出的系统完成拾取和放置任务。
这些结果证明了将基于高频 SSVEP 的 BCI 和基于计算机视觉的物体识别相结合来控制机械臂的可行性和效率。这里提出的控制策略可以扩展到控制机械臂执行其他复杂任务。
