Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-Based Emotion Recognition

作者：Yi Yang, Ze Wang, Yu Song, Ziyu Jia, Boyu Wang, Tzyy‐Ping Jung, Feng Wan · 发表于：IEEE Transactions on Affective Computing · 年份：2025 · DOI：10.1109/taffc.2025.3564272 · 被引用次数：18 · 研究领域：Emotion and Mood Recognition

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-conf...