Domain-Invariant Adaptive Graph Regularized Label Propagation for EEG-Based Emotion Recognition

• Published in: IEEE access Vol. 12; pp. 126774 - 126792
• Main Authors: Tao, Jianwen, Yan, Liangda, He, Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; adaptive graph Laplacian; Brain modeling; Data models; domain adaptation; EEG emotion recognition; Electroencephalography; Emotion recognition; Emotions; Feature recognition; Invariants; Knowledge management; Knowledge representation; label propagation; Labels; Machine learning; maximum mean discrepancy; Optimization; Propagation; Signal classification; Transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3454082

Emotion recognition holds significant potential for various real-world applications due to its reliability and precision. Nevertheless, variations in EEG patterns among individuals restrict the ability of emotion classifiers to generalize across different people. Furthermore, the non-stationary nature of EEG signals implies that a subject's data can vary over time, posing a challenge in developing models effective across multiple sessions. This paper introduces a novel domain adaptation (DA) method designed to generalize emotion recognition models across both individuals and sessions. Current mainstream DA methods primarily focus on learning discriminative domain-invariant feature (DIF) representations by integrating the "pseudo labels" of the target domain to enhance knowledge transfer. However, most approaches treat the optimization of domain-invariant features and the updating of target "pseudo labels" as two separate stages, making it challenging to achieve optimal learning performance. To address this, we propose a joint D omain- I nvariant feature learning and A daptive G raph regularized L abel P ropagation (DIAGLP) method for EEG-based emotion recognition. DIAGLP integrates semi-supervised knowledge adaptation and label propagation on EEG data, optimizing DIF representation and the EEG emotion recognition task within a single framework, thereby allowing mutual enhancement. Specifically, by incorporating the concept of soft labels, a domain joint distribution measurement model is established to simultaneously mitigate both marginal and conditional distribution disparities between different subjects/sessions. Additionally, an adaptive probability graph model is constructed to improve the robustness of EEG label propagation. Furthermore, a robust <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula>-norm is applied to the domain joint distribution measurement and inductive learning models, creating a unified objective optimization form. Compared to several representative domain adaptation methods, the proposed method demonstrated superior or comparable performance in cross-subject and cross-session EEG emotion recognition tasks.