EEGMatch: Learning With Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition

• Published in: IEEE transaction on neural networks and learning systems Vol. 36; no. 7; pp. 12991 - 13005
• Main Authors: Zhou, Rushuang, Ye, Weishan, Zhang, Zhiguo, Luo, Yanyang, Zhang, Li, Li, Linling, Huang, Gan, Dong, Yining, Zhang, Yuan-Ting, Liang, Zhen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2025
• Subjects: Adaptation models; Algorithms; Brain modeling; Data augmentation; Data models; Databases, Factual; Electroencephalography; Electroencephalography (EEG); Electroencephalography - methods; Emotion recognition; Emotions - classification; Emotions - physiology; Feature extraction; Humans; multidomain adaptation; Neural Networks, Computer; Pattern Recognition, Automated - methods; Semisupervised learning; Supervised Machine Learning; Training; Transfer learning

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this article, we propose a novel semisupervised tra...