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.
Supported by National-level Computer and Information Technology Experimental Teaching Demonstration Center (0800120010) and National Innovation and Entrepreneurship Program for Undergraduates of Tongji University 2022 (Practice and Research of Artificial Intelligence Algorithm for Emotion Recognition in Chinese Traditional Music).
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Zhao, M. et al. (2023). Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognition. In: Chen, Y., et al. Clinical Image-Based Procedures. CLIP 2022. Lecture Notes in Computer Science, vol 13746. Springer, Cham. https://doi.org/10.1007/978-3-031-23179-7_8
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