眼电伪迹自动去除方法的研究与分析  

Research and analysis of ocular artifact automatic removal

作　　者：李明爱[1] 梅意城 孙炎珺[1] 杨金福[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《仪器仪表学报》2014年第11期2515-2523,共9页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(81471770;61201362);北京市自然科学基金(7132021);北京市教育委员会面上项目(KM201110005005)资助项目

摘　　要：脑电信号采集时很容易受到眼电信号的干扰,从而影响脑机接口系统的性能。为此,提出一种基于离散小波变换(DWT)和典型相关分析(CCA)的眼电伪迹自动去除方法,即DCCA法。首先,对采集的多导脑电信号和眼电信号进行离散小波变换,获得多尺度小波系数,并利用典型相关分析去除小波系数间的相关性,得到互不相关的典型小波系数;进而,利用相关系数判别眼迹成分,将相应典型小波系数置零并依次采用CCA逆变换和DWT逆变换重构剔除眼电伪迹后的脑电信号。基于9位实验者的4种眼电数据进行实验研究,并从统计学的角度对实验结果进行显著性检验。结果表明,DCCA法相对其他方法在均方根误差、信噪比方面具有显著优势,且具有较好的实时性,并表现出较强的适应能力。The electroencephalography（EEG） is easily affected by the ocular artifact（OA） when sampled, and this will produce great impact on the performance of brain-computer interface system. A novel method was proposed based on the combination of canonical correlation analysis（CCA） and discrete wavelet transform（DWT）, and it is denoted as DCCA. Firstly, DWT was applied to the collected EEG and electrooculogram（EOG） signals to acquire the multiple scale wavelet coefficients, and CCA to eliminate the correlation among the coefficients. Then, the correlation coefficient was used as a criterion to recognize the ocular components, and the corresponding canonical wavelet coefficient vectors were set to zero. At last, the inverse algorithms of CCA and DWT were applied in sequence. So, the OA was removed from EEG in this way. By using DCCA and other methods, experiment research was finished based on the BCI data sets which contained 4 kinds of EOG data and were sampled from 9 subjects at different time. The significant tests show that the proposed method has obvious superiority in the aspects of root mean square error（RMSE） and signal noise rate（SNR）. Furthermore, it has good real-time performance and excellent adaptive capabilities.

关 键 词：脑电接口 眼电伪迹去除 离散小波变换 典型相关分析 显著性检验 

分 类 号：TH79[机械工程—仪器科学与技术]