TY - GEN
T1 - EEG EMOTION RECOGNITION BASED ON DYNAMICAL GRAPH ATTENTION NETWORK
AU - Guo, Yi
AU - Tang, Chao
AU - Wu, Hao
AU - Chen, Badong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Emotion recognition based on electroencephalography (EEG) signals is one of the current research challenges in this field. In order to learn the optimal graph structure information for each subject, we propose a dynamic graph attention neural network model. The model utilizes a graph attention neural network as a feature learner, dynamically learning channel connections, and enriching feature representations between channels through global attention. To verify the effectiveness of the proposed method, we conducted experiments on the publicly available emotion recognition dataset SEED. The experimental results show that the average accuracy and standard deviation of the 15 subjects are 94.6% and 4.98%, respectively. The results indicate that our proposed dynamic graphical attention neural network outperforms existing methods.
AB - Emotion recognition based on electroencephalography (EEG) signals is one of the current research challenges in this field. In order to learn the optimal graph structure information for each subject, we propose a dynamic graph attention neural network model. The model utilizes a graph attention neural network as a feature learner, dynamically learning channel connections, and enriching feature representations between channels through global attention. To verify the effectiveness of the proposed method, we conducted experiments on the publicly available emotion recognition dataset SEED. The experimental results show that the average accuracy and standard deviation of the 15 subjects are 94.6% and 4.98%, respectively. The results indicate that our proposed dynamic graphical attention neural network outperforms existing methods.
KW - Electroencephalography (EEG)
KW - Emotion recognition
KW - Graph attention neural network
KW - Graph structure
UR - https://www.scopus.com/pages/publications/85195415714
U2 - 10.1109/ICASSP48485.2024.10447925
DO - 10.1109/ICASSP48485.2024.10447925
M3 - 会议稿件
AN - SCOPUS:85195415714
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1921
EP - 1925
BT - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Y2 - 14 April 2024 through 19 April 2024
ER -