Control of The Robotic Arm System With an SSVEP-Based BCI

Abstract Recent studies on brain–computer interfaces (BCIs) implemented in robotic systems have shown that the system’s effectiveness in assisting individuals with movement disorders to enhance their human–computer interaction skills. However, achieving precise and rapid online completion of tasks remains a challenge for manipulators with multiple degrees of freedom (DOFs). In this paper, we explore a time-sharing control strategy for studying motion control of a robotic arm based on steady-state visual evoked potentials. The signals are generated by the joint frequency-phase modulation method, analyzed with the filter-bank canonical correlation analysis algorithm, and identified to control the six-DOF robotic arm for task execution. The shared control strategy not only reduces user’s cognitive fatigue but also enhances system in practical environments. The use of high-frequency stimuli significantly improves user comfort, and hybrid coding increases the universality of the BCI system. Additionally, by setting multiple locations and actions randomly, the robotic arm can adaptively program the optimal path. The online results showed that BCI instructions of the proposed system could be accurately chosen from six options within 6.45 s. Subjects used an average of 12 commands for the robotic arm to achieve the proposed task with an average accuracy of 98.21%. These findings validate the feasibility and effectiveness of applying the system to robotic control. The control strategy proposed in this study exhibits versatility in controlling robots to perform various complex tasks across different domains.