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Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems

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Abstract

The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively.

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Capped L21-norm-based common spatial patterns—a robust model for EEG signals classification

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Acknowledgements

The authors would like to thank the anonymous reviewers for their useful suggestions.

Funding

This work was supported by the National Natural Science Foundation of China under Grant 62176054 and the Key Research and Development Plan (Industry Foresight and Common Key Technology) of Jiangsu Province, China, under Grant BE2022157.

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Author notes

  1. Jingyu Gu and Jiuchuan Jiang contributed equally.

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

    Jingyu Gu, Sheng Ge & Haixian Wang

  2. School of Information Engineering, Nanjing University of Finance and Economics, Nanjing, 210003, Jiangsu, People’s Republic of China

    Jiuchuan Jiang

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  1. Jingyu Gu
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  2. Jiuchuan Jiang
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  3. Sheng Ge
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  4. Haixian Wang
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Correspondence to Haixian Wang.

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Gu, J., Jiang, J., Ge, S. et al. Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems. Med Biol Eng Comput 61, 1083–1092 (2023). https://doi.org/10.1007/s11517-023-02782-6

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  • DOI: https://doi.org/10.1007/s11517-023-02782-6

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