摘要
针对生机电一体化系统中生物体与机电装置之间的信息传递与交互能力不足的问题,利用脑电采集设备EmotivEPOC和机械臂,搭建了一个信息传输速率较快的基于脑机接口技术的写字系统并进行了系统建模仿真与实现。在系统建模中通过带通滤波和CCA(Canonical Correlation Analysis)算法组合对稳态视觉诱发电位进行频率识别,其四分类的准确率高达91.6%。在线实验中通过Emotiv EPOC控制机械臂实现了对简单汉字的书写,达到了系统建模仿真的目的。
In order to solve the problem of insufficient information transmission and interaction between organisms and electromechanical devices in the integrated system of vital electricity, Emotiv EPOC and mechanical arm of eeg acquisition equipment were used to build a writing system based on brain-computer interface technology with faster information transmission rate The system modeling and simulation were conducted. In the system modeling, the frequency identification of steady-state visual evoked potential was carried out by a combination of bandpass filtering and Canonical Correlation Analysis algorithm, and the accuracy rate of the four classifications was as high as 91.6%. In the online experiment, Emotiv EPOC controlled the mechanical arm to write simple Chinese characters, which showed the purpose of system modeling and simulation was achieved.
作者
陈超
平尧
郝斌
徐瑞
Chen Chao;Ping Yao;Hao Bin;Xu Rui(Tianjin University of Technology,Tianjin 300384,China;Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin 300072,China;Tianjin University of Technology Central Institute of Information,Tianjin 300380,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第12期4499-4505,共7页
Journal of System Simulation
基金
国家重点研发计划(2018YFC1314500)
国家自然科学基金(61806146)
关键词
生机电一体化
脑机接口
稳态视觉诱发电位
Emotiv
EPOC
机械臂
典型相关性分析
electromechanical integration
brain-computer interface
steady-state visual evoked potential
Emotiv EPOC
mechanical arm
typical correlation analysis
