基于子成分分解的脑电信号去噪方法比较研究

doi:10.3969/j.issn.1000-1158.2019.04.27

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Abstract EEG signals contain abundant time and spatial information. In order to obtain good real-time and anti-interference physiological signals, a large number of weak EEG signal extraction techniques have been applied. In view of the fact that the actual physiological EEG signals are susceptible to irrelevant noise and other issues, the maximum signal fraction analysis and independent component analysis methods are compared to remove the artifacts contained in the EEG signals and the real-time performance of the processing. The effect of the solution is evaluated from the three aspects of scatter plot analysis, correlation index comparison and calculation speed. The results show that both methods have the effect of separating noise signals and can guarantee the real-time performance. However, maximum signal fraction analysis has better separation effect than independent component analysis, higher correlation index, higher similarity, similarity value floating smaller advantages of a more stable, and has a wider range of application prospects.

Key words： metrology EEG maximum signal fraction analysis independent component analysis subcomponent decomposition

Received: 28 September 2018 Published: 10 June 2019

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TB973

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	Articles by authors
	FU Rong-rong
	BAO Tian-tian
	TIAN Yong-sheng
	WANG Lin

Cite this article:

FU Rong-rong,BAO Tian-tian,TIAN Yong-sheng, et al. Comparative Study on Denoising Methods of EEG Signals Based on Subcomponent Decomposition[J]. Acta Metrologica Sinica, 2019, 40(4): 708-713.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.04.27 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I4/708

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