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Motor Imagery EEG Recognition Based on FBCSP and PCA

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Advances in Brain Inspired Cognitive Systems (BICS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10989))

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Abstract

In motor imagery-based Brain Computer interfaces (BCIs), the classification accuracy of using the Common Spatial Pattern (CSP) algorithm to deal with the electroencephalogram (EEG) is closely related to the frequency range selected. Due to individual differences, the frequency range selected that reaches the best performance is different, which limits the generality and the actual use of the algorithm. To solve this problem, this paper proposes a motor imagery recognition method based on Filter Bank Common Spatial Pattern (FBCSP) and Principal Components Analysis (PCA), which is called FBCSP+PCA. The feasibility of the FBCSP+CSP is preliminary verified using the 2008 BCI competition data and further verified using data collected by our laboratory with wireless dry electrode device. The average classification accuracy of the data collected by our laboratory reaches 75.7% in the absence of individual band selection. That is also to say that the proposed method has good generality and and practical value because it can obtain high performance without the need of giving each individual a specific optimum frequency band.

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Acknowledgments

This project is supported by National Natural Science Foundation of China (60975079, 31100709).

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Authors and Affiliations

  1. Department of Automation, School of Mechanical Engineering and Automation, Shanghai University, No. 149, Yanchang Road, Shanghai, 200072, China

    Banghua Yang & Jianzhen Tang

  2. School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Jurong West, 639798, Singapore

    Cuntai Guan & Bo Li

Authors
  1. Banghua Yang
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  2. Jianzhen Tang
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  3. Cuntai Guan
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  4. Bo Li
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Corresponding author

Correspondence to Jianzhen Tang .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Northwestern Polytechnical University, Xi’an, China

    Jiangbin Zheng

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. Anhui University, Hefei, China

    Bin Luo

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  7. Northwestern Polytechnical University, Xi’an, China

    Xinbo Zhao

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Yang, B., Tang, J., Guan, C., Li, B. (2018). Motor Imagery EEG Recognition Based on FBCSP and PCA. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-00563-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00562-7

  • Online ISBN: 978-3-030-00563-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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