能量熵与峰值窗口结合去除眼电伪迹研究  

Research on Removing Ocular Artifacts from EEG by Using Energy Entropy and Peak Window

作　　者：周元 于明[2] 黄炜嘉[1] 李效龙[1] ZHOU Yuan;YU Ming;HUANG Weijia;LI Xiaolong(College of Electrical and Information Engineering,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China;Medical College,Affiliated Hospital of Jiangsu University,Zhenjiang,Jiangsu 212000,China)

机构地区：[1]江苏科技大学电子信息学院,江苏镇江212003 [2]江苏大学附属医院医学院,江苏镇江212000

出　　处：《计算机工程与应用》2020年第19期168-175,共8页Computer Engineering and Applications

基　　金：国家自然科学基金面上项目(No.61671221)。

摘　　要：为改进传统独立分量分析自动去除眼电伪迹算法中存在识别眼电分量速度慢、需采集同步参考眼电信号、丢失脑电信号问题,提出一种不需要参考眼电信号的眼电伪迹自动识别去除方法。利用FastICA分解出独立分量,计算各独立分量频谱能量熵,以频谱能量熵值作为判据识别出眼电分量;然后使用峰值窗口分离出眼电分量中存在的脑电信号,与其他独立分量进行拼接;利用FastICA逆变换重构出去眼电伪迹的脑电信号。实验结果表明:该方法能准确快速自动地去除眼电伪迹,并较好地保留其他的脑电信号成分;频谱能量熵识别眼电伪迹平均用时为0.01 s,准确率为98%,适用于实时EOG去除。In order to improve the problems existing in the traditional Independent Component Analysis(ICA)algorithm for automatic removal of Ocular Artifact(OA), such as slow speed of recognition of OA, acquisition of synchronous reference EOG signals, and loss of EEG signals, a recognition method without reference Electro-Oculogram(EOG)signalsis proposed, which can automatically removal OA. Firstly, using the FastICA into independent component, calculate the spectrum energy entropy of each independent components, with energy spectrum entropy as the criterion to identify the OA components. Then the EEG signals in the OA component are isolated by the peak window and spliced with other independent components. Further more, the clean EEG signals are restored with the inverse algorithm of FastICA. These experimental results show that this method can quickly, precisely and automatically remove the OA and retain other EEG components. The average time of spectral energy entropy for the recognition of OA is 0.01 s, and the accuracy rate is98%, which is suitable for real-time EOG removal.

关 键 词：脑电信号(EEG) 眼电伪迹 能量熵 峰值窗口 

分 类 号：R318[医药卫生—生物医学工程]