【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。更多还原

【Abstract】 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.更多还原