%0 Journal Article
%A WANG Canfeng
%A SUN Yao
%T Research on improved independent component analysis to ocular artifacts removal from EEG signals
%D 2018
%R 10.3778/j.issn.1002-8331.1609-0236
%J Computer Engineering and Applications
%P 167-173
%V 54
%N 4
%X 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 independent 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 coefficient. 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 parameter 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.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1609-0236