计算机工程与应用2018，Vol.54Issue(4)：167-173,7.DOI:10.3778/j.issn.1002-8331.1609-0236
改进独立分量算法的眼电伪迹去除方法研究
Research on improved independent component analysis to ocular artifacts removal from EEG signals
摘要
Abstract
Electroencephalogram(EEG)is easily affected by Ocular Artifacts(OA),which would be harmful to analysis. The Improved Independent Component Analysis(IICA)is a novel method for Ocular Artifacts removing automatically. Firstly,the horizontal and vertical electro-oculogram aliasing together,and together with EEG as the input,the indepen-dent components are gained through the FastICA algorithm.Secondly,it records the negative entropy criterion parameter, uses the correlation coefficient to recognize the independent component and records the corresponding correlation coeffi-cient. Thirdly, the parameter adds steps. Then repeats the above steps until the parameter achieves threshold. Fourthly, pick up the maximum coefficient of the above coefficients and the corresponding parameter.Finally,use the new parame-ter to gain the independent components, and use correlation coefficient to recognize the aliasing signal component.The EEG without Ocular Artifacts are reconstructed using inverse transformation of ICA.Experimental results show that IICA lowers time-consuming,and improves the signal-to-noise ratio,and reduces root-mean-square errors.关键词
眼电伪迹/改进独立分量分析/混叠/负熵判据Key words
ocular artifacts/Improve Independent Component Analysis(IICA)/aliasing/negative entropy criterion分类
医药卫生引用本文复制引用
王灿锋,孙曜..改进独立分量算法的眼电伪迹去除方法研究[J].计算机工程与应用,2018,54(4):167-173,7.基金项目
浙江省自然科学基金(No.LQ13F010014). （No.LQ13F010014）
