Domain Adaptation with Source Selection for Motor-Imagery based BCI

Eunjin Jeon, Wonjun Ko, Heung-Il Suk

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

1 Citation (Scopus)

Abstract

Recent successes of deep learning methods in various applications have inspired BCI researchers for their use in EEG classification. However, data insufficiency and high intra-and inter-subject variabilities hinder from taking their advantage of discovering complex patterns inherent in data, which can be potentially useful to enhance EEG classification accuracy. In this paper, we devise a novel framework of training a deep network by adapting samples of other subjects as a means of domain adaptation. Assuming that there are EEG trials of motor-imagery tasks from multiple subjects available, we first select a subject whose EEG signal characteristics are similar to the target subject based on their power spectral density in resting-state EEG signals. We then use EEG signals of both the selected subject (called a source subject) and the target subject jointly in training a deep network. Rather than training a single path network, we adopt a multi-path network architecture, where the shared bottom layers are used to discover common features for both source and target subjects, while the upper layers branch out into (1) source-Target subject identification, (2) label prediction optimized for a source subject, and (3) label prediction optimized for a target subject. Based on our experimental results over the BCI Competition IV-IIa dataset, we validated the effectiveness of the proposed framework in various aspects.

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
DOIs
Publication statusPublished - 2019 Feb 1
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

Conference

Conference7th International Winter Conference on Brain-Computer Interface, BCI 2019
CountryKorea, Republic of
CityGangwon
Period19/2/1819/2/20

Keywords

  • Brain-Computer Interface
  • Deep Learning
  • Domain Adaptation
  • Electroencephalogram (EEG)
  • Motor Imagery
  • Transfer Learning

ASJC Scopus subject areas

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

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  • Cite this

    Jeon, E., Ko, W., & Suk, H-I. (2019). Domain Adaptation with Source Selection for Motor-Imagery based BCI. In 7th International Winter Conference on Brain-Computer Interface, BCI 2019 [8737340] (7th International Winter Conference on Brain-Computer Interface, BCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2019.8737340