TY - GEN
T1 - Emotion recognition from physiological signals using multi-hypergraph neural networks
AU - Zhu, Junjie
AU - Zhao, Xibin
AU - Hu, Han
AU - Gao, Yue
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Emotion recognition from physiological signals is an effective way to discern the inner state of users. Existing works are lack in the exploration of latent correlation among multiple physiological signals and relationship among different subjects. To tackle this issue, we propose to recognize emotion from physiological signals using multi-hypergraph neural networks (MHGNN). In this method, the correlation among different subjects is formulated in the multi-hypergraph structure, where each type of physiological signal is used to generate one hypergraph. In each hypergraph, the hyperedges are used to represent the connections among the vertices (subject, stimuli). Thus, the emotion recognition task is modeled as classifying each vertex in the multi-hypergraph. Experimental results and comparisons with the state-of-the-art methods in the DEAP dataset demonstrate the superior performance of our method. The comparative experiments based on available biological knowledge verify that MHGNN can depict the real biological response process in a much more precise way.
AB - Emotion recognition from physiological signals is an effective way to discern the inner state of users. Existing works are lack in the exploration of latent correlation among multiple physiological signals and relationship among different subjects. To tackle this issue, we propose to recognize emotion from physiological signals using multi-hypergraph neural networks (MHGNN). In this method, the correlation among different subjects is formulated in the multi-hypergraph structure, where each type of physiological signal is used to generate one hypergraph. In each hypergraph, the hyperedges are used to represent the connections among the vertices (subject, stimuli). Thus, the emotion recognition task is modeled as classifying each vertex in the multi-hypergraph. Experimental results and comparisons with the state-of-the-art methods in the DEAP dataset demonstrate the superior performance of our method. The comparative experiments based on available biological knowledge verify that MHGNN can depict the real biological response process in a much more precise way.
KW - Emotion recognition
KW - Multi-hypergraph neural networks
KW - Multi-modal fusion
KW - Physiological signals
UR - http://www.scopus.com/inward/record.url?scp=85070955268&partnerID=8YFLogxK
U2 - 10.1109/ICME.2019.00111
DO - 10.1109/ICME.2019.00111
M3 - Conference contribution
AN - SCOPUS:85070955268
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - 610
EP - 615
BT - Proceedings - 2019 IEEE International Conference on Multimedia and Expo, ICME 2019
PB - IEEE Computer Society
T2 - 2019 IEEE International Conference on Multimedia and Expo, ICME 2019
Y2 - 8 July 2019 through 12 July 2019
ER -