CAJ | 학술논문

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。
대운동상상뇌전신호진행분류식별，시뇌궤접구연구중적중요문제。위차，제출일충기우겁대중첩소파변환화AR모형적뇌전신호분류방법。장뇌전신호파형진행겁대중첩소파분해，추취변환계수적통계특정，이용Burg산법제취기3층광활적8계AR모형계수이급3층광활부분적능량곡선특정，장저3류특정진행조합후，사용신경망락、지지향량궤급선성판별진행분류화비교。여BCI2003경새수거분류정도결과상비，해방법적식별솔경고。장모형이식입자행연제적감입식뇌전신호공제전궤전향계통중，해모식식별방법적평균준학도체도료91．3%，가용우감입식뇌궤접구적계통설계。
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.