Auxiliary controller design and performance comparative analysis in closed-loop brain–machine interface system

Brain–machine interface (BMI) can realize information interaction between the brain and external devices, and yet the control accuracy is limited by the change of electroencephalogram signals. The introduction of auxiliary controller can overcome the above problems, but the performance of different auxiliary controllers is quite different. Hence, in this paper, we comprehensively compare and analyze the performance of different auxiliary controllers to provide a theoretical basis for designing BMI system. The main work includes: (1) designing four kinds of auxiliary controllers based on simultaneous perturbation stochastic approximation-function approximator (SPSA-FA), iterative feedback tuning-PID (IFT-PID), model predictive control (MPC) and model-free control (MFC); (2) based on the model of improved single-joint information transmission, constructing the closed-loop BMI systems with the decoder-based Wiener filter; and (3) comparing their performance in the constructed closed-loop BMI systems for dynamic motion restoration. The results show that the order of tracking accuracy is MPC, IFT-PID, SPSA-FA, MFC, and the order of time consumed is opposite. A good control effectiveness is achieved at the expense of time, so a suitable auxiliary controller should be selected according to the actual requirements.