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Artifact Removal Methods in Motor Imagery of EEG

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

EEG reflects the strength of the neuronal activity in the brain. Since EEG signal is weak, noisy and mixed with a large number of artifacts, which causes interference to the processing and identification of the EEG signal. Using EEG related pretreatment can effectively remove artifact, noise, and improve EEG signal-noise ratio and efficient, which provides more accurate data for feature extraction and classification. In this paper, we introduce several methods including PCA, ICA and CSP. Based on these methods, the complete process of EEG signal de-noising, feature extraction and classification are established, which can complete the classification and recognition of the motor imagery signals. We use a combination of a lot of pretreatment methods to analysis and process motor imagery of EEG and propose an improved algorithm named CS-CSP. The experimental results show that the Chebyshev type II filter is superior to the conventional pre-treatment methods and the recognition accuracy of CS-CSP is higher than CSP.

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Acknowledgments

The research work is supported by National Natural Science Foundation of China (U1433116) and the Fundamental Research Funds for the Central Universities (NP2017208).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing, 211106, Jiangsu, China

    Yanlong Zhu, Zhongyu Wang, Chenglong Dai & Dechang Pi

Authors
  1. Yanlong Zhu
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  2. Zhongyu Wang
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  3. Chenglong Dai
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  4. Dechang Pi
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Corresponding author

Correspondence to Dechang Pi .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Zhu, Y., Wang, Z., Dai, C., Pi, D. (2017). Artifact Removal Methods in Motor Imagery of EEG. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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