IEEE Trans Neural Syst Rehabil Eng. 2019 Mar;27(3):533-542. doi: 10.1109/TNSRE.2019.2897323. Epub 2019 Feb 4.
This paper presents a new brain-robot interaction system by fusing human and machine intelligence to improve the real-time control performance. This system consists of a hybrid P300 and steady-state visual evoked potential (SSVEP) mode conveying a human being's intention, and the machine intelligence combining a fuzzy-logic-based image processing algorithm with multi-sensor fusion technology. A subject selects an object of interest via P300, and the classification algorithm transfers the corresponding parameters to an improved fuzzy color extractor for object extraction. A central vision tracking strategy automatically guides the NAO humanoid robot to the destination selected by the subject intentions represented by brainwaves. During this process, human supervises the system at high level, while machine intelligence assists the robot in accomplishing tasks by analyzing image feeding back from the camera, distance monitoring using out-of-gauge alarms from sonars, and collision detecting from bumper sensors. In this scenario, the SSVEP takes over the situations in which the machine intelligence cannot make decisions. The experimental results show that the subjects can control the robot to a destination of interest, with fewer commands than only using a brain-robot interface. Therefore, the fusion of human and machine intelligence greatly alleviates the brain load and enhances the robot executive efficiency of a brain-robot interaction system.
本文提出了一种新的脑机交互系统，通过融合人机智能来提高实时控制性能。该系统由混合 P300 和稳态视觉诱发电位 (SSVEP) 模式组成，以传达人类的意图，机器智能结合基于模糊逻辑的图像处理算法和多传感器融合技术。主体通过 P300 选择感兴趣的对象，分类算法将相应的参数传输到改进的模糊颜色提取器，以提取对象。中央视觉跟踪策略自动引导 NAO 人形机器人到主体通过脑波表示的意图选择的目的地。在此过程中，人类在高级别监督系统，而机器智能通过分析来自摄像头的反馈图像、使用超声波的超限警报进行距离监测以及使用缓冲器传感器进行碰撞检测来协助机器人完成任务。在这种情况下，SSVEP 接管机器智能无法做出决策的情况。实验结果表明，与仅使用脑机接口相比，主体可以使用更少的命令来控制机器人到达感兴趣的目的地。因此，人机智能的融合大大减轻了大脑的负担，提高了脑机交互系统的机器人执行效率。
