TY - JOUR
T1 - GCD-JFSE
T2 - Graph-based class-domain knowledge joint feature selection and ensemble learning for EEG-based emotion recognition
AU - Luo, Gang
AU - Han, Yutong
AU - Xie, Weichu
AU - Tian, Fuze
AU - Zhu, Lixian
AU - Qian, Kun
AU - Li, Xiaowei
AU - Sun, Shuting
AU - Hu, Bin
N1 - Publisher Copyright:
© 2024
PY - 2025/1/30
Y1 - 2025/1/30
N2 - 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.
AB - 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.
KW - Affective computing
KW - Cross-domain emotion recognition
KW - EEG
KW - Feature selection
UR - http://www.scopus.com/inward/record.url?scp=85211240328&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2024.112770
DO - 10.1016/j.knosys.2024.112770
M3 - Article
AN - SCOPUS:85211240328
SN - 0950-7051
VL - 309
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 112770
ER -