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《Journal of Zhejiang University(Engineering Science)》 2014-07
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Hybrid methodology combining fractal dimension and RLS-ICA for rejection of electroencephalography noise

YANG Bang-hua;HAN Zhi-jun;WANG Qian;HE liang-fei;School of Mechatronics Engineering and Automation,Shanghai University;  
A novel method combining fractal dimension and recursive least-squares(RLS)-independent component analysis(ICA)was presented in order to remove noise from electroencephalography(EEG)in the study on brain computer interfaces(BCIs).The ICA was used to decompose the contaminated EEG signals into independent components(ICs).Then the fractal dimension was used to automatically identify ICs containing noises.The RLS adaptive filters were applied to filter noise in the identified ICs further.The processed ICs were projected back to reconstruct the uncontaminated EEG signals.The proposed method has two obvious advantages.One is that it only filters ICs identified to contain noise by fractal dimension,which can overcome the shortage that RLS-ICA filters all the ICs to result in some useful EEG being deleted.The other is that it can accelerate the speed of RLS-ICA by decreasing the number of ICs to be filtered.The 2008International BCI competition data and the laboratory data were preprocessed in order to verify the effectiveness of the proposed method.The proposed method was compared with RLS-ICA.Experimental results showed that the novel method had better performance than RLS-ICA in removing noise.The running time of one sample by the proposed method was 0.07seconds shorter than that by the RLS-ICA in average.The proposed method can not only remove electrooculogram(EOG)and electromyography(EMG),but also remove some unknown noises.
【Fund】: 国家自然科学基金资助项目(60975079 31100709);; 上海市教育委员会创新项目(11YZ19 12ZZ099)
【CateGory Index】: TN911.4
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