基于改进单关节信息传输模型的闭环脑机接口系统设计
Closed-loop brain-machine interface system design based on improved single-joint information transmission model
摘要点击 68  全文点击 130  投稿时间:2018-08-19  修订日期:2019-05-16
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DOI编号  10.7641/CTA.2019.80619
  2020,37(2):395-404
中文关键词  脑机接口  模型改进  解码器设计  闭环系统  预测控制
英文关键词  Brain-machine interface  Model improvement  Decoder design  Closed-loop system  Model predictive control
基金项目  国家自然科学基金(61603295), 中国博士后基金(2017M623207), 陕西省自然科学基础研究计划资助项目(2018JM6003, 2017JM5114), 陕西省重点研发计划(2017ZDCXL-GY-01-02-03), 西安科技大学优秀青年科技基金(2018YQ2-07) 资助.
学科分类代码  
作者单位E-mail
潘红光 西安科技大学 hongguangpan@163.com 
米文毓 西安科技大学  
邓军 西安科技大学 58111391@qq.com 
孙京诰 华东理工大学  
薛瑞 华东理工大学  
中文摘要
      近年来, 脑机接口技术在残疾人肢体功能康复、老年人生活辅助等方面的应用日益广泛. 本文以单关节信息传输模型为对象, 通过模型改进、设计解码器和辅助控制器构造了闭环脑机接口系统以恢复单关节的运动功能. 本文主要工作包括: 1) 引入相对速度向量对单关节信息传输模型改进以降低模型输出的超调量, 并测试了改进模型的性能; 2) 基于改进模型, 通过设计基于维纳滤波的解码器、基于预测控制策略的辅助控制器构造了闭环脑机接口系统以恢复缺失的信息通路. 离线和在线仿真说明, 改进模型的输出性能有较大提升、超调量明显下降; 构建的闭环系统很好地实现了对缺失信息通路的恢复和期望轨迹的跟踪.
英文摘要
      In recent years, brain-machine interface (BMI) technology has been used more and more widely in physical rehabilitation and life support for the disabled and the elderly. For the the single-joint information transmission (SJIT) model, through the model improvement, the decoder design and the auxiliary controller design, the closed-loop BMI system is formulated in this paper to restore the movement of the single-joint. The innovation of this paper mainly includes: 1) the relative velocity vector is introduced to improve the SJIT model to reduce the overshoot, and then the performance of improved model is tested; 2) based on this improved model, a decoder based on Wiener filter and an auxiliary controller based on model predictive control strategy are designed and introduced to restore the missing information loop. The offline and online simulation results show that the improved model can greatly improve the output performance, reduce the overshoot clearly; and the formulated closed-loop system can well restore the missing information loop and track the target trajectory.