基于MODWT的运动想象脑电信号识别  

Movement Imagery Electroencephalogram Recognition Based on MOWDT

作　　者：李东明[1] 王典洪[2] 严军[2] 王永涛[1] 宋麦玲[1] 余蓓蓓[2] 

机构地区：[1]中国地质大学信息技术教学实验中心,武汉430074 [2]中国地质大学机械与电子信息学院,武汉430074

出　　处：《计算机工程》2014年第10期161-167,共7页Computer Engineering

基　　金：中央高校基本科研业务费专项基金资助项目(CUGL120278);湖北省自然科学基金资助项目(2011335070)

摘　　要：对运动想象脑电信号进行分类识别,是脑机接口研究中的重要问题。为此,提出一种基于极大重叠小波变换和AR模型的脑电信号分类方法。将脑电信号波形进行极大重叠小波分解,抽取变换系数的统计特征,利用Burg算法提取其3层光滑的8阶AR模型系数以及3层光滑部分的能量曲线特征,将这3类特征进行组合后,使用神经网络、支持向量机及线性判别进行分类和比较。与BCI2003竞赛数据分类精度结果相比,该方法的识别率更高。将模型移植入自行研制的嵌入式脑电信号控制电机转向系统中,该模式识别方法的平均准确度达到了91.3%,可用于嵌入式脑机接口的系统设计。To classify and recognize the movement imagery electroencephalogram, is an important problem in Brain Computer Interface （ BCI ） research. This paper presents a novel method of extracting Electroencephalogram （ EEG ） features based on Maximum Overlap Wavelet Transform（ MODWT） and Autoregressive（ AR） model. The EEG signal is decomposed to three levels by MODWT and statistics of wavelet coefficients are computed. Meanwhile, in the EEG signal’ s approximation part, the eighth-order AR coefficients are estimated by Burg ’ s algorithm, and energe feature vector is also extracted. The combination features are used as an input vector for Neural Network（ NN） classifier,Support Vector Machine （ SVM ） classifier, and Linear Discriminant Analysis （ LDA ） classifier. The recognition result using BCI2003 competition data set is compared with the best result of the competition,and the classification results show the higher recognition rate of the algorithm. Moreover, transplanting the trained successfully model into embedded motor steering control system based on EEG,and the average recognition accuracy of 91. 3% is obtained. The method provides a new idea for the study of embedded BCI system for practical application.

关 键 词：脑机接口 运动想象 极大重叠小波变换 能量曲线 模式分类 电机转向控制 

分 类 号：TN911.7[电子电信—通信与信息系统]