Abstract 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.

中文翻译：

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基于改进SJIT模型和Izhikevich神经元网络的闭环BMI系统设计

摘要 脑机接口（BMI）是一种有用的技术，它为残疾人与世界交流创造了一种新的方式，但用人脑进行实验是有风险的。因此，有必要建立精确的肢体运动过程中信息传递的数学模型。在本文中，我们首先通过引入多个神经元模型来改进经典的单关节信息传输（SJIT）模型，改进后的模型更接近真实的单关节运动。其次，在改进模型的基础上制定了一个带有基于 Wiener 滤波器的解码器、一个基于模型预测控制 (MPC) 的辅助控制器和一个 Izhikevich 神经元网络的闭环系统，使用的 Izhikevich 神经元网络时间更长效率高于现有的。最后，在这个闭环系统中，引入了皮层内微刺激（ICMS）技术，实时反馈来自MPC控制器的信息。辅助控制器通过改变刺激电流的频率来辅助大脑控制人工手臂。这样，本文提出的优化问题的计算复杂度大大降低，本文设计的闭环BMI系统可以很好地跟踪期望的轨迹。