EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation

Wonjun Ko, Heung Il Suk

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

1 Citation (Scopus)


Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have gained widespread attention to monitor a user's clinical condition or identify his/her intention/emotion. Nevertheless, the existing methods mostly model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, thus suffering from representing complex spatio-spectro-temporal patterns as well as inter-subject variability. In this work, we propose novel EEG-oriented self-supervised learning methods to discover complex and diverse patterns of spatio-spectral characteristics and spatio-temporal dynamics. Combined with the proposed self-supervised representation learning, we also devise a feature normalization strategy to resolve an inter-subject variability problem via clustering. We demonstrated the validity of the proposed framework on three publicly available datasets by comparing with state-of-the-art methods.

Original languageEnglish
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450392365
Publication statusPublished - 2022 Oct 17
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: 2022 Oct 172022 Oct 21

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government under Grant 2017-0-00451 (Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Grant 2019-0-00079 (Department of Artificial Intelligence, Korea University).

Publisher Copyright:
© 2022 ACM.


  • electroencephalogram
  • self-supervision
  • subject-independent

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)


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