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Multi-class Motor Imagery Recognition of Single Joint in Upper Limb Based on Multi-domain Feature Fusion

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Abstract

Aiming at the difficulties in extracting effective features and low classification accuracy in the current multi-class motor imagery recognition, this paper proposes a multi-class motor imagery recognition method based on the combination of multi-domain feature fusion and twin support vector machine (TWSVM). First, the Autoregressive (AR) model, the bispectrum analysis method, and the common spatial pattern method are used to extract the features of the signal in temporal domain, frequency domain, and space domain, and construct a joint feature; then use the kernel principal component analysis to fuse the joint feature, the fusion features are generated by extracting the principal components whose cumulative contribution rate is more than 95%; Finally, the fusion features are sent to TWSVM optimized by bat algorithm for classification of the EEG, obtain an average recognition rate of 92.38%, which provides an effective method for multi-class motor imagery recognition, which will greatly promote in practical application based on BCI.

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Acknowledgements

We thank all the subjects who participated in the experiment. We thank Kai Zhao for his guidance on the EEG data acquisition.

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Authors and Affiliations

  1. School of Mechanical Engineering, Northeast Electric Power University, Jilin City, 132012, Jilin Province, China

    Shan Guan, Zhen Yuan, Fuwang Wang, Jixian Li, Xiaogang Kang & Bin Lu

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  1. Shan Guan
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  2. Zhen Yuan
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  3. Fuwang Wang
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  4. Jixian Li
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Correspondence to Zhen Yuan.

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Guan, S., Yuan, Z., Wang, F. et al. Multi-class Motor Imagery Recognition of Single Joint in Upper Limb Based on Multi-domain Feature Fusion. Neural Process Lett 55, 8927–8945 (2023). https://doi.org/10.1007/s11063-023-11185-5

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