CAJ | 학술논문

针对脑机接口运动想象脑电信号的分类识别问题，提出了一种基于小波包分解的C3、C4二通道能量特征提取方法。该方法首先采用6阶的巴特沃斯带通滤波对二通道脑电信号进行降噪；然后采用Daubechies类小波函数对其进行5层分解，选择第四层CD4、第五层CD5的小波系数进行重构并抽取其能量特征；最后采用线性距离判别进行分类和使用Kappa系数进行分类衡量。利用BCI2008竞赛的标准数据BCICIV_2b_gdf进行验证，结果表明利用该方法可以较好地反映事件相关同步和事件相关去同步的特征，为BCI研究中事件相关电位的分类识别提供了有效的手段。
침대뇌궤접구운동상상뇌전신호적분류식별문제，제출료일충기우소파포분해적C3、C4이통도능량특정제취방법。해방법수선채용6계적파특옥사대통려파대이통도뇌전신호진행강조；연후채용Daubechies류소파함수대기진행5층분해，선택제사층CD4、제오층CD5적소파계수진행중구병추취기능량특정；최후채용선성거리판별진행분류화사용Kappa계수진행분류형량。이용BCI2008경새적표준수거BCICIV_2b_gdf진행험증，결과표명이용해방법가이교호지반영사건상관동보화사건상관거동보적특정，위BCI연구중사건상관전위적분류식별제공료유효적수단。
Aiming at the issue of motor imagery electroencephalography(EEG) pattern recognition in the research of brain-computer interface(BCI), a power feature method based on discrete wavelet packet decomposition is proposed for the channels C3 and C4. Firstly, a six-border Butterworth filter is used to denoise the two-channel EEG signals. Secondly, two-channel EEG signals are decomposed to five levels using Daubechies wavelet and the fourth level and the fifth level are chosen to reconstruct the signals and compute its power feature. Final y, linear discriminant analysis (LDA) is utilized to classify the feature and the Kappa value is utilized to measure the accuracy of the classifier. This method is applied to the standard dataset BCICIV_2b_gdf of BCI Competition 2008, and experimental results show that this method reflect the feature of event-related sychronization and event-related desychronization obviously and it is an effective way to classify the EEG patterns in the research of BCI.