Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition

• Published in: IEEE access Vol. 13; pp. 94822 - 94833
• Main Authors: Deng, Xin, Hong, Xinyi, Ai, LiJiao, Li, Xingchen, Li, Chenhui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Brain modeling; brain-computer interface; Data augmentation; Data models; Datasets; domain adaptation; Electrodes; electroencephalogram (EEG); Electroencephalography; Emotion recognition; Emotions; Feature extraction; Machine learning; Modules; Reinforcement learning; Telecommunications; Training data; Transfer learning

In recent years, transfer learning methods have found widespread applications in emotion recognition, especially in domains with limited labeled data. The significant distribution disparity between source and target domains in transfer learning often leads to detrimental negative transfer phenomena....