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A novel method to reduce the motor imagery BCI illiteracy

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Abstract

To reduce the motor imagery brain-computer interface (MI-BCI) illiteracy phenomenon and improve the classification accuracy, this paper proposed a novel method combining paradigm selection and Riemann distance classification. Firstly, a novel sensitivity-based paradigm selection (SPS) algorithm is designed for the optimization of classification to find the best classification pattern through a sensitive indicator. Then, a generalized Riemann minimum distance mean (GRMDM) classifier is proposed by introducing a weight factor to fuse the Log-Euclidean Metric classifier and the Riemannian Stein divergence classifier. The experimental results show that the proposed method achieves a better performance for multi-class motor imagery tasks. The average classification accuracy on the BCI competition IV dataset2a is 80.98%, which is 11.04% higher than Stein divergence classifier on the original two-class paradigm. Furthermore, the proposed method demonstrates its capacity on reducing MI-BCI illiteracy.

Here we investigate whether the BCI illiteracy phenomenon can be reduced through sensitivity-based paradigm selection (SPS) method and generalized Riemann minimum distance mean (GRMDM) classifier when performing motor imagery tasks.

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Funding

This work was partially supported by the Natural Science Foundation of Tianjin (No.18JCYBJC87700), and South African National Research Foundation Incentive Grant (No. 114911).

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Authors and Affiliations

  1. Institute of Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin, 300384, China

    Tingting Wang & Enzeng Dong

  2. Department of Electrical Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa

    Shengzhi Du

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  1. Tingting Wang
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  2. Shengzhi Du
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  3. Enzeng Dong
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Correspondence to Enzeng Dong.

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Wang, T., Du, S. & Dong, E. A novel method to reduce the motor imagery BCI illiteracy. Med Biol Eng Comput 59, 2205–2217 (2021). https://doi.org/10.1007/s11517-021-02449-0

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