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Exploring Multi-scale Temporal and Spectral CSP Feature for Multi-class Motion Imagination Task Classification

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

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Abstract

Effective features extracted from electroencephalogram (EEG) data greatly benefit the classification of motor imagery brain-computer interfaces (MI-BCI) systems. In this paper, we further investigate the factors affecting the performance of common spatial pattern (CSP). A novel method based on CSP is proposed to extract more discriminant features to improve the classification accuracy in training and testing for a support vector machine (SVM) classifier. We extend CSP feature extractor to multiscale temporal and spectral conditions. Experimental results show that compared with many improved CSP features and several deep learning methods of recent years, the multiscale temporal and spectral CSP features achieve superior classification accuracy.

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Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jian-Xun Mi, Rong-Feng Li & Guo Chen

  2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jian-Xun Mi, Rong-Feng Li & Guo Chen

Authors
  1. Jian-Xun Mi
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  2. Rong-Feng Li
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  3. Guo Chen
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Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Mi, JX., Li, RF., Chen, G. (2021). Exploring Multi-scale Temporal and Spectral CSP Feature for Multi-class Motion Imagination Task Classification. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-84532-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84531-5

  • Online ISBN: 978-3-030-84532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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