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

11 Citations (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
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
Country/TerritoryKorea, 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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