Semi-Supervised Deep Adversarial Learning for Brain-Computer Interface

Wonjun Ko, Eunjin Jeon, Jiyeon Lee, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


Recent advances in deep learning have made a progressive impact on BCI researches. In particular, convolutional neural networks (CNNs) with different architectural forms have been studied for spatio-Temporal or spatio-spectral feature representation learning. However, there still remain many challenges and limitations due to the necessity of a large annotated training samples for robustness. In this paper, we propose a semi-supervised deep adversarial learning framework that effectively utilizes generated artificial samples along with labeled and unlabelled real samples in discovering class-discriminative features to boost robustness of a classifier, thus to enhance BCI performance. It is also noteworthy that the proposed framework allows to exploit unlabelled real samples to better uncover the underlying patterns inherent in a user's EEG signals. In order to justify the validity of the proposed framework, we conducted exhaustive experiments with 'Recurrent Spatio-Temporal Neural Network' CNN architectures over the public BCI Competition IV-IIa dataset. From our experiments, we could observe statistically significant improvements on performance, compared to the competing methods with the conventional framework. We have also visualized learned convolutional weights in terms of activation pattern maps, separability of extracted features, and validity of generated artificial samples.

Original languageEnglish
Title of host publication7th International Winter Conference on Brain-Computer Interface, BCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538681169
Publication statusPublished - 2019 Feb
Event7th International Winter Conference on Brain-Computer Interface, BCI 2019 - Gangwon, Korea, Republic of
Duration: 2019 Feb 182019 Feb 20

Publication series

Name7th International Winter Conference on Brain-Computer Interface, BCI 2019


Conference7th International Winter Conference on Brain-Computer Interface, BCI 2019
Country/TerritoryKorea, Republic of


  • Brain-Computer Interface
  • Convolutional Neural Network
  • Deep Learning
  • Electroencephalogram
  • Generative Adversarial Learning
  • Motor Imagery
  • Semi-Supervised Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing
  • Neuroscience (miscellaneous)


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