GCD-JFSE: Graph-based class-domain knowledge joint feature selection and ensemble learning for EEG-based emotion recognition

• Published in: Knowledge-based systems Vol. 309; p. 112770
• Main Authors: Luo, Gang, Han, Yutong, Xie, Weichu, Tian, Fuze, Zhu, Lixian, Qian, Kun, Li, Xiaowei, Sun, Shuting, Hu, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30.01.2025
• Subjects: Affective computing; Cross-domain emotion recognition; EEG; Feature selection; Affective computing; Cross-domain emotion recognition; Feature selection; EEG
• ISSN: 0950-7051
• DOI: 10.1016/j.knosys.2024.112770

Feature selection has demonstrated strong performance in emotion recognition using intrasubject electroencephalography (EEG) data. However, it faces challenges due to individual differences and the nonstationarity of EEG signals in cross-subject and cross-session emotion recognition. Currently, research on incorporating domain information into feature selection for cross-domain (subject or session) emotion recognition remains limited. To address this issue, we propose a graph-based class-domain knowledge joint feature selection and ensemble learning approach. Firstly, an undirected, fully connected weighted graph is constructed to capture the relationship between features. Then, some metrics such as domain scatter, domain correlation, and domain standard deviation are introduced to guide feature selection. Subsequently, soft voting ensemble learning is employed to enhance recognition performance. To validate the effectiveness of our method, we conduct experiments on public datasets (SEED, SEED_IV, DREAMER), achieving accuracies of 78.67% on SEED, 58.98% on SEED_IV, 61.11% of valence and 72.46% of arousal on DREAMER in a cross-subject scenario. In the cross-session scenario, we obtain 87.11% on SEED and 60.74% on SEED_IV. The proposed method outperforms state-of-the-art approaches. This study not only expands the application of feature selection in emotion recognition but also provides a potential strategy to enhance the performance of real-world EEG-based emotion recognition applications.