Zhao, M, Pang, L, Lu, Y et al. (4 more authors) (2023) Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognition. In: Lecture Notes in Computer Science. Clinical Image-Based Procedures 11th Workshop, CLIP 2022, Held in Conjunction with MICCAI 2022, 18 Sep 2022, Singapore. Springer , pp. 72-81. ISBN 9783031231780
Abstract
How to integrate data in different feature spaces and distributions is a research hotspot in EEG-based emotion recognition. A novel source-domain adaptation strategy based on initial distribution differences for EEG emotion recognition is proposed, which selects several source domains that are most similar to the target domain for domain adaptation. Compared to the ‘source-target pair’ domain adaptation method using all source domains, this method improves accuracy by up to 10% and reduces computation time by up to 43% based on the SEED-III and SEED-IV datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Domain adaptation; EEG; Emotion recognition; Transfer learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Mar 2023 15:11 |
Last Modified: | 05 Apr 2023 10:35 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-031-23179-7_8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197254 |