信息与控制2018，Vol.47Issue(6)：656-662,7.DOI:10.13976/j.cnki.xk.2018.7324
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
Single-joint Control of Closed-loop Brain-machine Interfaces Based on Genetic Algorithm LS-SVM Direct Inverse Model
摘要
Abstract
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.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)Key words
brain-computer interface (BMI)/least squares support vector machine (LSSVM)/single joint control/recurrent neural network (RNN)/echo state network (ESN)分类
信息技术与安全科学引用本文复制引用
孙京诰,王硕,杨嘉雄,薛瑞,潘红光..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[J].信息与控制,2018,47(6):656-662,7.基金项目
国家自然科学青年基金资助项目(61603295) （61603295）
