Exploiting multiple EEG data domains with adversarial learning

David Bethge, Philipp Hallgarten, Ozan Özdenizci, Ralf Mikut, Albrecht Schmidt, Tobias Grosse-Puppendahl

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly subject-dependent, and are bound to the equipment and experimental setup used, (i.e. domain). This leads to machine learning models often suffer from poor generalization ability, where they perform significantly worse on real-world data than on the exploited training data. Recent research heavily focuses on cross-subject and cross-session transfer learning frameworks to reduce domain calibration efforts for EEG signals. We argue that multi-source learning via learning domain-invariant representations from multiple data-sources is a viable alternative, as the available data from different EEG data-source domains (e.g., subjects, sessions, experimental setups) grow massively. We propose an adversarial inference approach to learn data-source invariant representations in this context, enabling multi-source learning for EEG-based brain- computer interfaces. We unify EEG recordings from different source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP, DREAMER), and demonstrate the feasibility of our invariant representation learning approach in suppressing data- source-relevant information leakage by 35% while still achieving stable EEG-based emotion classification performance.
Original languageEnglish
Title of host publication2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Number of pages5
ISBN (Electronic)978-1-7281-2782-8
Publication statusPublished - 2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: EMBC 2022 - Glasgow, United Kingdom
Duration: 11 Jul 202215 Jul 2022


Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2022
Country/TerritoryUnited Kingdom


  • adversariallearning
  • domain invariance
  • EEG

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics
  • Computer Vision and Pattern Recognition

Fields of Expertise

  • Human- & Biotechnology
  • Information, Communication & Computing


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