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A Novel Feature Fusion Approach for Classification of Motor Imagery EEG Based on Hierarchical Extreme Learning Machine

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Abstract

Because feature extraction from electroencephalogram (EEG) signals is essential for cognitive investigations, effective feature extraction approaches are needed to improve the practical recognition accuracy of EEG signals. In this paper, a strategy is presented for fusing both the linear and nonlinear features from EEG signals to improve the accuracy of motor imagery classification. First, principal component analysis (PCA) is used to extract the linear features from EEG, and linear discriminant analysis (LDA) is introduced to supplement the discriminant features by utilizing the label information of the training data. Second, we use parametric t-distributed stochastic neighbor embedding (PTSNE) to extract the nonlinear features reflecting the original manifold structure of the EEG data. Third, these linear and nonlinear features are fused to generate the final features for classification. After feature extraction, we choose the hierarchical extreme learning machine (HELM) algorithm, which has a high classification accuracy for EEG signal classification of motor imagery. To verify the validity of the strategy, we compare the accuracy of the proposed method with that of other methods on the motor imagery dataset. We achieve a high accuracy of 95.89% and an average accuracy of 93.45%. The performance shows that the accuracy of the proposed feature fusion strategy is effective for classification and that the recognition accuracy is improved compared with other state-of-the-art methods.

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Data Availability

The open-source dataset [33] has been used in this paper. The code can be available on request from the authors.

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Funding

This work was supported in part by the National Natural Science Foundation of China under grants 62176009 and 62173010. The authors also acknowledge the joint support provided by the Beijing Municipal Education Commission and the Municipal Natural Science Foundation of China under grant 23JA002.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Lijuan Duan, Zhaoyang Lian, Juncheng Chen & Mingai Li

  2. Beijing Key Laboratory of Trusted Computing, Beijing, 100124, China

    Lijuan Duan & Zhaoyang Lian

  3. National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing, 100124, China

    Lijuan Duan & Zhaoyang Lian

  4. Applied Sciences, Beijing University of Technology, Beijing, 100124, China

    Yuanhua Qiao

  5. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing, 100101, China

    Jun Miao

Authors
  1. Lijuan Duan
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  2. Zhaoyang Lian
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  3. Yuanhua Qiao
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  4. Juncheng Chen
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  5. Jun Miao
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Correspondence to Yuanhua Qiao.

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Duan, L., Lian, Z., Qiao, Y. et al. A Novel Feature Fusion Approach for Classification of Motor Imagery EEG Based on Hierarchical Extreme Learning Machine. Cogn Comput 16, 566–580 (2024). https://doi.org/10.1007/s12559-023-10217-5

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  • DOI: https://doi.org/10.1007/s12559-023-10217-5

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