{"type":"ScholarlyArticle","title":"A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition","authors":[{"name":"Yang Li","slug":"yang-li","given":"Yang","family":"Li","affiliation":"Key Laboratory of Child Development and Learning Science of Ministry of Education , Southeast University , China; School of Information Science and Engineering , Southeast University , China"},{"name":"Wenming Zheng","slug":"wenming-zheng","given":"Wenming","family":"Zheng","affiliation":"Key Laboratory of Child Development and Learning Science of Ministry of Education , Southeast University , China"},{"name":"Zhen Cui","slug":"zhen-cui","given":"Zhen","family":"Cui","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology , China"},{"name":"Tong Zhang","slug":"tong-zhang","given":"Tong","family":"Zhang","affiliation":"Key Laboratory of Child Development and Learning Science of Ministry of Education , Southeast University , China; School of Information Science and Engineering , Southeast University , China"},{"name":"Yuan Zong","slug":"yuan-zong","given":"Yuan","family":"Zong","affiliation":"Key Laboratory of Child Development and Learning Science of Ministry of Education , Southeast University , China"}],"abstract":"In this paper, we propose a novel neural network model, called bi-hemispheres domain adversarial neural network (BiDANN), for EEG emotion recognition. BiDANN is motivated by the neuroscience findings, i.e., the emotional brain's asymmetries between left and right hemispheres. The basic idea of BiDANN is to map the EEG feature data of both left and right hemispheres into discriminative feature spaces separately, in which the data representations can be classified easily. For further precisely predicting the class labels of testing data, we narrow the distribution shift between training and testing data by using a global and two local domain discriminators, which work adversarially to the classifier to encourage domain-invariant data representations to emerge. After that, the learned classifier from labeled training data can be applied to unlabeled testing data naturally. We conduct two experiments to verify the performance of our BiDANN model on SEED database. The experimental results show that the proposed model achieves the state-of-the-art performance.","doi":"10.24963/ijcai.2018/216","url":"https://ijcai.org/proceedings/2018/216","pdf":"https://ijcai.org/proceedings/2018/0216.pdf","markdown":"https://ijcai.org/proceedings/2018/216.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/216","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Humans and AI","track":"Main track","number":216,"pages":{"first":1561,"last":1567},"keywords":["Humans and AI: Human-Computer Interaction","Machine Learning Applications: Bio;Medicine"],"videos":[],"references":[{"position":1,"raw":"[Britton et al., 2006] Jennifer C Britton, K Luan Phan, Stephan F Taylor, Robert C Welsh, Kent C Berridge, and I Liberzon. 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