Brain-controlled Robotic Arm System based on Improved FBCCA Algorithm with Dynamic Window

Brain-controlled system has been used in assisting disabilities, and how to enable such groups to have the ability to communicate with the surroundings has become the focus of research. However, due to the individual differences and the dynamic time-varying brain states, the fixed time window used in the traditional Filter Bank Canonical Correlation Analysis (FBCCA) algorithm for Steady-State Visual Evoked Potential-based Brain-Computer Interface (SSVEP-BCI) is not optimal, which limits its generalizability. Therefore, in this paper, based on the SSVEP experimental paradigm, we propose an improved FBCCA algorithm with dynamic window based on the difference level between correlation coefficients. Compared with the traditional FBCCA algorithm, the accuracy is significantly improved. Furthermore, this paper designs a BCI-based robotic arm control system, achieving real-time and precise object grasping. This validates the feasibility and effectiveness of the proposed algorithm. Our findings may provide a theoretical basis and technical support for the practical application of BCI.