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Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity

文献类型：期刊论文

作者Li, Jinpeng1,2,3,5; Qiu, Shuang1,2,5; Du, Changde1,2,5; Wang, Yixin1,2,5; He, Huiguang1,4,5
刊名IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
出版日期2020-06-01
卷号12期号:2页码:344-353
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
ISSN号2379-8920
DOI10.1109/TCDS.2019.2949306
通讯作者He, Huiguang(huiguang.he@ia.ac.cn)
英文摘要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.
WOS关键词DIFFERENTIAL ENTROPY FEATURE ; DEPRESSION ; BRAIN
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]
WOS研究方向Computer Science ; Robotics ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000542972700021
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences (CAS) International Collaboration Key Project ; Strategic Priority Research Program of CAS
源URL[http://ir.ia.ac.cn/handle/173211/39951]  
专题类脑智能研究中心_神经计算及脑机交互
通讯作者He, Huiguang
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Univ Chinese Acad Sci, Ningbo Hwa Mei Hosp, Ningbo 315010, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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