CAJ | 학술논문

提出了一种基于双树复小波变换的运动想象脑电信号特征提取方法。针对传统离散小波抗混叠性差的缺陷，采用双树复小波变换对脑电信号进行分解与重构，得到各子带信号能量并进行归一化处理，选取α、β节律信号的归一化能量作为想象运动的特征进行SVM分类。通过对仿真信号的分析，证实双树复小波变换具有良好的混叠抑制能力和抗噪性。最后选用国际脑机接口竞赛和实验室实测的运动想象数据进行分类识别。实验结果表明，双树复小波变换是一种有效的特征提取方法，其运动想象特征的识别率要优于常用的特征分析方法。
제출료일충기우쌍수복소파변환적운동상상뇌전신호특정제취방법。침대전통리산소파항혼첩성차적결함，채용쌍수복소파변환대뇌전신호진행분해여중구，득도각자대신호능량병진행귀일화처리，선취α、β절률신호적귀일화능량작위상상운동적특정진행SVM분류。통과대방진신호적분석，증실쌍수복소파변환구유량호적혼첩억제능력화항조성。최후선용국제뇌궤접구경새화실험실실측적운동상상수거진행분류식별。실험결과표명，쌍수복소파변환시일충유효적특정제취방법，기운동상상특정적식별솔요우우상용적특정분석방법。
The paper proposed an algorithm of feature extraction of EEG based on Dual-Tree Complex Wavelet Transform. Considering the defect of severe frequency aliasing resulted from Discrete Wavelet Transform,this paper first extracted the sub-band signals of EEG by DTCWT decomposition and reconstruction,and then calculated the energy of each signal and normalized them. Support Vector Machine was applied to recognize the pattern of motor imagery by selecting the normalized rhythmα,βas the features. Also,the simulated signals were analysed to confirm that the DTCWT had a satisfying effect on reducing aliasing effects and noise resistance. Finally,international BCI competition signals and the measured motor imagery data were selected for classification. The results showed that the DTCWT was an effective method of feature extraction,which could also obtain a higher recognition rate than the methods in common use.