文献类型：期刊文献

中文题名：基于遗传算法LS-SVM直接逆模型的闭环脑机接口单关节控制

英文题名：Single-joint Control of Closed-loop Brain-machine Interfaces Based on Genetic Algorithm LS-SVM Direct Inverse Model

作者：孙京诰[1];王硕[1];杨嘉雄[1];薛瑞[1];潘红光[2]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237;[2]西安科技大学电气与控制工程学院,西安710049

年份：2018

卷号：47

期号：6

起止页码：656

中文期刊名：信息与控制

外文期刊名：Information and Control

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2017_2018】；

基金：国家自然科学青年基金资助项目(61603295)

语种：中文

中文关键词：脑机接口(BMI);最小二乘支持向量机(LSSVM);单关节控制递归神经网络(RNN);回声状态网络(ESN)

外文关键词：brain-computer interface (BMI);least squares support vector machine (LSSVM);single joint control; recurrent neural network (RNN);echo state network (ESN)

摘要：脑机接口系统通过大脑—计算机接口技术和控制理论的组合来弥补由于肌体的受损部分而造成的信息缺失.本研究基于心理生理皮质神经元放电率电路模型,在脑机接口控制理论分析的基础上进行自发单关节运动任务,采用自适应ESN(echo state network)设计非线性解码器,并引入FORCE(First Order Reduced and Contrdled Error learning)算法更新网络输出权值,通过仿真有无自然本体反馈信息情况下的解码器的性能来验证所设计的解码器的有效性.最后,通过基于遗传算法LS-SVM(least squares support vector machine)的直接逆模型框架,设计近似大脑皮层感觉区神经元放电率的最佳人工本体反馈去刺激大脑皮层感觉区神经元.仿真结果发现,所设计的闭环脑机接口(BMI)系统框架能够很好地恢复在线自发单关节自然运动任务性能,这也为当系统模型未知时,根据对象的输入输出数据恢复闭环系统的性能提供了新的研究思路.
Brain-machine interface (BMI) systems compensate for the lack of information due to damaged body parts via a combination of BMI technology and control theory. In this paper,we study a spontaneous single-joint motion task based on an experimental model of the psychoacoustic cortical neuron firing rate and the BMI control theory. In addition,we design a nonlinear decoder using an adaptive echo state network (ESN) and introduce the First Order Reduced and Contrdled Error learning (FORCE) algorithm to update the network’s output weight. We verify the effectiveness of the designed decoder by simulating the performance of the decoder in the presence of natural ontological feedback information. Finally,using a direct inverse model framework based on a least-squares-support-vector-machine genetic algorithm,we design the optimal artificial sensory feedback of the neuronal firing rate in the sensory area of the cerebral cortex to stimulate the neurons of the cerebral cortex.The simulation results show that the designed closed-loop brain-computer interface (BMI) framework can restore the performance of a spontaneous single-joint natural motion task,which provides a new way to study the performance of closed-loop systems based on object input and output data when the system model is unknown.