Acta Metrologica Sinica  2019, Vol. 40 Issue (4): 708-713    DOI: 10.3969/j.issn.1000-1158.2019.04.27
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Comparative Study on Denoising Methods of EEG Signals Based on Subcomponent Decomposition
FU Rong-rong1,BAO Tian-tian1,TIAN Yong-sheng1,WANG Lin2
1. School of Electrical Engineering, Yanshan University, Qinghuangdao, Hebei 066004, China
2. School of Mechanical Engineering, Shenyang Institute of Technology, Shenyang, Liaoning 110136, China
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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 wordsmetrology      EEG      maximum signal fraction analysis      independent component analysis      subcomponent decomposition     
Received: 28 September 2018      Published: 10 June 2019
PACS:  TB973  
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FU Rong-rong
BAO Tian-tian
TIAN Yong-sheng
WANG Lin
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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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