北京生物医学工程

基于典型相关分析和小波变换的眼电伪迹去除

Automatic Removal of Ocular Artifacts in EEG Signals by Using CCA and Wavelet Transformation

作者：赵春煜邱天爽

单位：大连理工大学电子信息与电气工程学部（大连116024）

分类号：

出版年·卷·期（页码）：2011·30·5（474-479）

摘要：

目的针对脑电信号中眼电伪迹去除尚存在的问题，提出一种基于典型相关分析与小波变换的
(wavelet-enhanced canonical correlation analysis, wCCA)自动去除眼电伪迹的算法。方法首先，
充分利用脑电信号和眼电伪迹的空间分布特征，将基于典型相关分析的盲源分离算法分别应用于左右脑
区的混合信号中，从而保证典型相关分析分解得到的第一个典型相关变量（即左右脑区之间的最公共成
分），就是眼电伪迹分量。然后为了恢复泄漏在该伪迹分量中的脑电成分，对伪迹分量进行小波阈值滤
波，将高于某一阈值的小波系数置零，而保留低于阈值的系数。结果与其他三种基于盲源分离去除眼电
伪迹的方法相比较，该方法在有效地自动去除眼电伪迹的同时，很好地保留了潜在的脑电信号，去除效
果明显优于其他三种方法。结论由于该算法简单，处理速度较快，因此应用于实时的脑机接口系统中更
具优越性，为后续脑电信号的特征提取和分类分析提供了良好的基础。

Objective A new method of ocular artifacts removal in EEG
(electroencephalography) recordings, wavelet-enhanced canonical correlation analysis
(wCCA), is presented in this paper. Methods Firstly, considering the differences between
the spatial distributions of the EEG signals and the EOG signals, CCA is applied to the
mixed signals of left and right brain separately. There is no need to identify the artifact
component by subjective visual inspection, because the first canonical component found by
CCA for each dataset, also the most common component between the left and right hemisphere,
is definitely related to artifacts. Then wavelet thresholding is employed to recover the
cerebral activities leaked into this artifact component. The performance of the proposed
method is compared to the three popular ocular artifacts removal methods CCA, second-order
-blind identification(SOBI) and wavelet independent component analysis(ICA), in terms of
correlation coefficient and signal-to-artifact ratio (SAR). Results It shows that wCCA’s
performance is better than those of the other three methods for removing the most ocular
artifacts from EEG recording automatically without altering the cerebral components.
Conclusions Since wCCA is simple and rapid, it is more advantageous to be applied in true
time brain-computer interface system than the other three, and provides a good groundwork
for the feature extraction and classification analysis of electroencephalography.

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