【摘要】 给出了一种基于典型相关分析(Canonical Correlation Analysis,CCA)的盲源分离技术来去除脑电信号中的眼电伪迹。通过实验验证了基于CCA的盲源分离方法去除眼电伪迹的有效性,并将该方法与广泛使用的独立分量分析(Independent Component Analysis,ICA)进行了比较。实验结果表明,基于CCA的盲源分离方法可以对眼电伪迹进行成功地分离和消除,该方法相较于ICA方法而言,算法更为简单,计算速度更快。更多还原

【Abstract】 Canonical Correlation Analysis(CCA) as a Blind Source Separation(BSS) technique is applied to the removal of Electroencephalography(EEG) artifacts.This method is tested and compared with the widely used Independent Component Analysis(ICA) method through experiments.The experiment results show that the CCA-based method is effective in separating and eliminating Electrooculography(EOG) contamination.Compared with ICA method,the proposed method has the advantages of simplicity and high speed.更多还原