Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity

doi:10.1109/TCDS.2019.2949306

	Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity
	Li, Jinpeng1,2,3,4 ; Qiu, Shuang1,2,3 ; Du, Changde1,2,3 ; Wang, Yixin1,2,3 ; He, Huiguang1,2,5
发表期刊	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
ISSN	2379-8920
	2020-06-01
卷号	12 期号:2 页码:344-353
通讯作者	He, Huiguang(huiguang.he@ia.ac.cn)
摘要	Emotion recognition has many potential applications in the real world. Among the many emotion recognition methods, electroencephalogram (EEG) shows advantage in reliability and accuracy. However, the individual differences of EEG limit the generalization of emotion classifiers across subjects. Moreover, due to the nonstationary characteristic of EEG, the signals of one subject change over time, which is a challenge to acquire models that could work across sessions. In this article, we propose a novel domain adaptation method to generalize the emotion recognition models across subjects and sessions. We use neural networks to implement the emotion recognition models, which are optimized by minimizing the classification error on the source while making the source and the target similar in their latent representations. Considering the functional differences of the network layers, we use adversarial training to adapt the marginal distributions in the early layers and perform association reinforcement to adapt the conditional distributions in the last layers. In this way, we approximately adapt the joint distributions by simultaneously adapting marginal distributions and conditional distributions. The method is compared with multiple representatives and recent domain adaptation algorithms on benchmark SEED and DEAP for recognizing three and four affective states, respectively. The experimental results show that the proposed method reaches and outperforms the state of the arts.
关键词	Electroencephalography Brain modeling Emotion recognition Adaptation models Training Feature extraction Neural networks Domain adaptation electroencephalogram (EEG) emotion recognition neural network transfer learning
DOI	10.1109/TCDS.2019.2949306
关键词[WOS]	DIFFERENTIAL ENTROPY FEATURE ; DEPRESSION ; BRAIN
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61976209] ; National Natural Science Foundation of China[81701785] ; Chinese Academy of Sciences (CAS) International Collaboration Key Project ; Strategic Priority Research Program of CAS[XDB32040200]
项目资助者	National Natural Science Foundation of China ; Chinese Academy of Sciences (CAS) International Collaboration Key Project ; Strategic Priority Research Program of CAS
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS记录号	WOS:000542972700021
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	脑机接口
引用统计	被引频次：122[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39952
专题	脑图谱与类脑智能实验室_神经计算与脑机交互
通讯作者	He, Huiguang
作者单位	1.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Ningbo Hwa Mei Hosp, Ningbo 315010, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Li, Jinpeng,Qiu, Shuang,Du, Changde,et al. Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2020,12(2):344-353.
APA	Li, Jinpeng,Qiu, Shuang,Du, Changde,Wang, Yixin,&He, Huiguang.(2020).Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,12(2),344-353.
MLA	Li, Jinpeng,et al."Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 12.2(2020):344-353.