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. © 2022, Science Press. All right reserved.