Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine

Qingshan She, Jie Zou, Zhizeng Luo, Thinh Nguyen, Rihui Li, Yingchun Zhang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM). [Figure not available: see fulltext.].

Original languageEnglish (US)
Pages (from-to)2119-2130
Number of pages12
JournalMedical and Biological Engineering and Computing
Volume58
Issue number9
DOIs
StatePublished - Sep 1 2020

Keywords

  • Brain-computer interface
  • Collaborative representation
  • Electroencephalogram
  • Multi-class motor imagery
  • Safety aware
  • Semi-supervised extreme learning machine

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

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