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.
|Title of host publication||CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|Publication status||Published - 2022 Oct 17|
|Event||31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States|
Duration: 2022 Oct 17 → 2022 Oct 21
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||31st ACM International Conference on Information and Knowledge Management, CIKM 2022|
|Period||22/10/17 → 22/10/21|
Bibliographical noteFunding 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).
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ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Decision Sciences(all)