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	An EEG signal denoising method based on ensemble empirical modedecomposition and independent component analysis
	Huimin Sun; Jun Cheng; Zheng Ma
	2018
会议日期	2018
英文摘要	Event-related potential (ERP) signal is very weak. The actual EEG signal is accompanied by a large amount of neurogenic noises and non-neurogenic interferences. Aimed at the issue of reconstructing the valuable ERP signal from polluted EEG signal, an efficient approach that combining ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA) is proposed. Firstly, the noisy signal is decomposed by EEMD, and several interested intrinsic mode functions (IMFs) components are obtained based on correlation between the decomposed IMFs and the original signal. Then, these interested IMF components are de-noised with ICA. Finally, the desired ERP signal is reconstructed from these de-noised IMFs. Experimental results show the merit of the proposed algorithm on EEG signal de-noising.
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13786]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Huimin Sun,Jun Cheng,Zheng Ma. An EEG signal denoising method based on ensemble empirical modedecomposition and independent component analysis[C]. 见:. 2018.

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