- 综合性科技类中文核心期刊
- 中国科技论文统计源期刊
- 中国科学引文数据库来源期刊
- 中国学术期刊文摘数据库（核心版）来源期刊
- 中国学术期刊综合评价数据库来源期刊
| Citation: |
LI Ming'ai, TIAN Xiaoxia, SUN Yanjun, YANG Jinfu. Ocular Artifact Removal Based on Local Mean Decomposition and Canonical Correlation Analysis[J]. Journal of Beijing University of Technology, 2016, 42(6): 843-850. DOI: 10.11936/bjutxb2015050009
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Based on local mean decomposition (LMD) and canonical correlation analysis (CCA), an automatic removal method, denoted as LMDC, was proposed to eliminate the serious impact of ocular artifact (OA) from electroencephalography (EEG). Each recorded EEG was decomposed into a series of physically meaningful production function (PF) components adaptively by LMD, and CCA was applied to eliminate the correlation among the PFs to get the corresponding canonical correlation variable. Then, the correlation coefficient matrix between each EEG and multi electrooculogram (EOG) was computed to recognize the OA component automatically. The random variables corresponding with OA components in the canonical correlation variable were set to zero, and the others remain unchanged to obtain a new canonical correlation variable. Finally, the inverse algorithm of CCA was utilized to project the new canonical correlation variable to the OA free PFs, and the OA removed EEG was reconstructed. Experimental research was conducted on a public brain computer interface (BCI) completion database. Experiment results show that LMDC has better performance than that of the other related methods, and has stronger adaptability for multi subjects and types of OA.
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