增强现实场景下基于稳态视觉诱发电位的机械臂控制系统

目前脑控机械臂在医疗康复等多个领域展现出了宽广的应用前景，但也存在灵活性较差、使用者易疲劳等不足之处。针对上述不足，该文设计一套增强现实(AR)环境下基于稳态视觉诱发电位(SSVEP)的机械臂异步控制系统。利用滤波器组典型相关分析方法(FBCCA)实现对12个目标的识别；提出基于投票策略和差值预测的动态窗口，实现刺激时长的自适应调节；利用伪密钥实现机械臂异步控制，完成拼图任务。试验结果表明，动态窗口可以根据受试者状态自动调整刺激时长，离线平均准确度为(93.11±5.85)％，平均信息传输速率(ITR)为(59.69±8.11) bit·min^–1。在线单次命令平均选择时间为2.18 s，有效地减轻受试者的视觉疲劳。每位受试者均能迅速完成拼图任务，证明了该人机交互方法的可行性。

Control System of Robotic Arm Based on Steady-State Visual Evoked Potentials in Augmented Reality Scenarios

At present, brain-controlled robotic arms have shown broad application prospects in many fields such as medical rehabilitation, but they also have disadvantages such as poor flexibility and fatigue of users. In view of the above shortcomings, an asynchronous control system based on Steady-State Visual Evoked Potential (SSVEP) in an Augmented Reality (AR) environment is designed. A Filter Bank Canonical Correlation Analysis (FBCCA) is applied to identify 12 targets. A dynamic window based on voting strategy and difference prediction is proposed to adjust the stimulus duration adaptively. The robotic arm is asynchronously controlled by pseudo-key to complete the task of the Jigsaw Puzzle. The experimental results demonstrate that the dynamic window can automatically adjust the length of stimulation according to the state of subjects. The average offline accuracy is (93.11±5.85)％, the average offline ITR is (59.69±8.11) bit·min–1. The average selection time of an online single command is 2.18 s. It can reduce the visual fatigue of the subjects effectively. Each subject can accomplish the puzzle task quickly, which indicates the feasibility of this human-computer interaction method.