Evolutionary Ensemble Learning for EEG-Based Cross-Subject Emotion Recognition

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 7; pp. 3872 - 3881
• Main Authors: Zhang, Hanzhong, Zuo, Tienyu, Chen, Zhiyang, Wang, Xin, Sun, Poly Z.H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adult; Algorithms; Biological neural networks; Brain modeling; Cross-subject transfer learning; Data models; Databases, Factual; EEG; Effectiveness; electro- encephalogram; Electroencephalography; Electroencephalography - methods; Emotion recognition; Emotional factors; Emotions; Emotions - physiology; Ensemble learning; Evolutionary algorithms; Feature extraction; Female; Humans; Machine Learning; Male; Medical research; Neural networks; Neural Networks, Computer; Pattern Recognition, Automated - methods; Signal Processing, Computer-Assisted; Temporal resolution
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3384816

Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions.