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Euler common spatial patterns for EEG classification

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Abstract

The technique of common spatial patterns (CSP) is a widely used method in the field of feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine distance can enlarge the distance between samples of different classes, we propose the Euler CSP (e-CSP) for the feature extraction of EEG signals, and it is then used for EEG classification. The e-CSP is essentially the conventional CSP with the Euler representation. It includes the following two stages: each sample value is first mapped into a complex space by using the Euler representation, and then the conventional CSP is performed in the Euler space. Thus, the e-CSP is equivalent to applying the Euler representation as a kernel function to the input of the CSP. It is computationally as straightforward as the CSP. However, it extracts more discriminative features from the EEG signals. Extensive experimental results illustrate the discrimination ability of the e-CSP.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62176054 and the University Synergy Innovation Program of Anhui Province under Grant GXXT-2020-015. The authors would like to thank the anonymous reviewers for their thoughtful comments and suggestions.

Funding

National Natural Science Foundation of China, 62176054, Haixian Wang, the University Synergy Innovation Program of Anhui Province, GXXT-2020–015, Haixian Wang

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Authors and Affiliations

  1. Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, Jiangsu, People’s Republic of China

    Jing Sun & Haixian Wang

  2. Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei, 230094, Anhui, People’s Republic of China

    Jing Sun & Haixian Wang

  3. Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Mengting Wei

  4. Institute of Software, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Ning Luo

  5. College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, Shanxi, People’s Republic of China

    Zhanli Li

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  1. Jing Sun
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Correspondence to Haixian Wang.

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Sun, J., Wei, M., Luo, N. et al. Euler common spatial patterns for EEG classification. Med Biol Eng Comput 60, 753–767 (2022). https://doi.org/10.1007/s11517-021-02488-7

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