A closed-loop BMI system design based on the improved SJIT model and the network of Izhikevich neurons

Brain–machine interface (BMI) is a useful technology which creates a new way for disable people to communicate with the world, but experimenting with human brains is risky. Hence, a precise mathematical model of the information transmission in the process of limb movement is necessary to be established. In this paper, firstly, we improve the classical single-joint information transmission (SJIT) model through introducing several neuron models, and the improved model is closer to the true single-joint movements. Secondly, a closed-loop system with a Wiener filter-based decoder, an auxiliary controller based on model predictive control (MPC) and a network of Izhikevich neurons is formulated based on the improved model, and the used network of Izhikevich neurons is more time efficient than the existing one. Finally, in this closed-loop system, the intracortical micro-stimulation (ICMS) technology is introduced to feedback the information from the MPC controller in real time. The auxiliary controller assist the brain to control artificial arm by changing the frequency of stimulation current. In this way, the computational complexity of the optimization problem proposed in this paper is greatly reduced, and the closed-loop BMI system designed in this paper can well track the desired trajectory.