Improved SFFS method for channel selection in motor imagery based BCI

NEUROCOMPUTING

卷:207

Article

Qiu, Zhaoyang¹  Jin, Jing¹  Lam, Hak-Keung²  Zhang, Yu¹  Wang, Xingyu¹  Cichocki, Andrzej^3,4 

[1] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China

[2] Kings Coll London, Dept Informat, London WC2R 2LS, England

[3] RIKEN, Lab Adv Brain Signal Proc, Brain Sci Inst, 2-1 Hirosawa, Wako, Saitama 3510198, Japan

[4] Polish Acad Sci, Syst Res Inst, Warsaw, Poland

关键词: Brain-computer interface (BCI);    Motor imagery;    Channels selection;    SFFS;   

DOI  :  10.1016/j.neucom.2016.05.035

来源: Elsevier

【 摘 要 】

Background: Multichannels used in brain-computer interface (BCI) systems contain redundant information and cause inconvenience for practical application. Channel selection can enhance the performance of BCI by removing task-irrelevant and redundant channels. Sequential floating forward selection (SFFS) is an intelligent search algorithm and is considered one of the best feature selection methods in the literature. However, SFFS is time consuming when the number of features is large. Method: In this study, the SFFS method was improved to select channels for the common spatial pattern (CSP) in motor imagery (MI)-based BCI. Based on the distribution of channels in the cerebral cortex, the adjacent channels would be treated as one feature for selection. Thus, in the search process, the improved SFFS could select or remove several channels in each iteration and reduce the total computation time. Results: The improved SFFS yielded significantly better performance than using all channels (p < 0.01) and support vector machine recursive feature elimination method (p < 0.05). The computation time of the proposed method was significantly reduced (p < 0.005) compared with the original SFFS method. Conclusions: This study improved the SFFS method to select channels for CSP in MI-based BCI. The improved SFFS method could significantly reduce computation time compared with the original SFFS without compromising the classification accuracy. This study provided a way to optimize electroencephalogram. channels, which combined the distribution of channels and the intelligent selection method (SFFS). Improvements were mainly in the perspective of reducing computation time, which leads to convenience in the practical application of BCI systems. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

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