基于EEMD-ICA的脑电去噪算法研究

doi:10.3969/j.issn.1000-1158.2021.03.22

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Abstract The electroencephalograph (EEG) signal is the electro physiological activity of brain cells, it is reflected in the scalp surface. However it is usually interfered by noises during signal acquisition process. In order to reserve the effective information and eliminate as much noise as possible, a method of ensemble empirical mode decomposition (EEMD) combined with independent component analysis (ICA) is introduced. Firstly, the EEMD decomposition can get a certain number of intrinsic mode function (IMF) of EEG signals. The virtual channel is reconstructed by the IMF components with more noise components which are selected based on correlation coefficient and de-noised by ICA algorithm. Secondly, the denoised results and the IMF components with multiple signal are reconstructed. Finally, the reconstructed signal is denoised by ICA again, and the final denoised signal is obtained. The experimental results show that the mentioned method has better signal-to-noise ratio and smaller RMSE than the other denoising methods, including wavelet denoising, EEMD denoising and ICA denoising. It shows that the mentioned method can denoise better.

Key words： metrology EEG signal denoise ensemble empirical mode decomposition independent component analysis virtual channel

Received: 28 June 2019 Published: 23 March 2021

PACS:	TB99
	TB973

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	Articles by authors
	FAN Feng-jie
	BAI Yang
	JI Hui-fang

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FAN Feng-jie,BAI Yang,JI Hui-fang. Denoising Method of EEG Signal Based on EEMD-ICA[J]. Acta Metrologica Sinica, 2021, 42(3): 395-400.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.03.22 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I3/395

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