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A hierarchical semi-supervised extreme learning machine method for EEG recognition

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Abstract

Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples are usually scarce for electroencephalography (EEG) data, while unlabeled samples are available in large quantity and easy to collect. In addition, traditional DL algorithms are notoriously time-consuming for the training process. To address these issues, a novel method of hierarchical semi-supervised extreme learning machine (HSS-ELM) is proposed in this paper and applied for motor imagery (MI) task classification. Firstly, the deep architecture of hierarchical ELM (H-ELM) approach is employed for feature learning automatically, and then these new high-level features are classified using the semi-supervised ELM (SS-ELM) algorithm which can exploit the information from both labeled and unlabeled data. Extensive experiments were conducted on some benchmark datasets and EEG datasets to evaluate the effectiveness of the proposed method. Compared with several state-of-the-art methods, including SVM, ELM, SAE, H-ELM, and SS-ELM, our HSS-ELM method can achieve better classification accuracy, a mean kappa value of 0.7945 and 0.5701 across all subjects in the training and evaluation sessions of BCI Competition IV Dataset 2a, respectively. Finally, it comes to the conclusion that the proposed method has achieved superior performance for feature extraction and classification of EEG signals.

The schematic of the proposed HSS-ELM algorithm.

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Acknowledgments

This work is supported by National Nature Science Foundation under Grant (No.61201302, 61671197 and 61601162), Zhejiang Province Natural Science Foundation (LY15F010009), Guangdong Provincial Work Injury Rehabilitation Center and the University of Houston. The authors would like to acknowledge the BCI Competition IV Dataset 2a which was used to test the algorithms proposed in this study.

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Authors and Affiliations

  1. Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Zhejiang, Hangzhou, 310018, China

    Qingshan She, Bo Hu, Zhizeng Luo & Yingchun Zhang

  2. Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA

    Thinh Nguyen & Yingchun Zhang

Authors
  1. Qingshan She
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  2. Bo Hu
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  3. Zhizeng Luo
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  4. Thinh Nguyen
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  5. Yingchun Zhang
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Correspondence to Qingshan She or Yingchun Zhang.

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She, Q., Hu, B., Luo, Z. et al. A hierarchical semi-supervised extreme learning machine method for EEG recognition. Med Biol Eng Comput 57, 147–157 (2019). https://doi.org/10.1007/s11517-018-1875-3

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  • DOI: https://doi.org/10.1007/s11517-018-1875-3

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