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A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data

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Abstract

This paper presents an approach to classifying electroencephalogram (EEG) signals for brain–computer interfaces (BCI). To eliminate redundancy in high-dimensional EEG signals and reduce the coupling among different classes of EEG signals, we use principle component analysis and linear discriminant analysis to extract features that represent the raw signals. Next, we introduce the voting-based extreme learning machine to classify the features. Experiments performed on real-world data from the 2003 BCI competition indicate that our classification method outperforms state-of-the-art methods in speed and accuracy.

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Acknowledgments

This research is partially sponsored by the Natural Science Foundation of China (Nos. 61175115, 61272320, 61001178, 61003105 and 61370113), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201304035, CIT&TCD201404052), the Jing-Hua Talents Project of Beijing University of Technology (2014-JH-L06), the Ri-Xin Talents Project of Beijing University of Technology (2014-RX-L06) and the International Communication Ability Development Plan for Young Teachers of Beijing University of Technology (No. 2014-16).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, China

    Lijuan Duan, Hongyan Zhong, Zhen Yang, Wei Ma & Xuan Zhang

  2. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Jun Miao

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  1. Lijuan Duan
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  2. Hongyan Zhong
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  3. Jun Miao
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  4. Zhen Yang
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  5. Wei Ma
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  6. Xuan Zhang
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Correspondence to Jun Miao.

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Duan, L., Zhong, H., Miao, J. et al. A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data. Cogn Comput 6, 477–483 (2014). https://doi.org/10.1007/s12559-014-9264-1

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  • DOI: https://doi.org/10.1007/s12559-014-9264-1

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