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

Qingshan She, Bo Hu, Zhizeng Luo, Thinh Nguyen, Yingchun Zhang

Research output: Contribution to journalArticle

14 Scopus citations

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. [Figure not available: see fulltext.].

Original languageEnglish (US)
Pages (from-to)147-157
Number of pages11
JournalMedical and Biological Engineering and Computing
Volume57
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • Deep learning
  • Extreme learning machines
  • Hierarchical
  • Motor imagery electroencephalography
  • Semi-supervised learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

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