Dynamic Domain Adaptation for Class-Aware Cross-Subject and Cross-Session EEG Emotion Recognition

doi:10.1109/JBHI.2022.3210158

	Dynamic Domain Adaptation for Class-Aware Cross-Subject and Cross-Session EEG Emotion Recognition
	Li, Zhunan1,2 ; Zhu, Enwei 1,2; Jin, Ming 1,2; Fan, Cunhang3 ; He, Huiguang4 ; Cai, Ting 1,2; Li, Jinpeng1,2
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
	2022-12-01
卷号	26 期号:12 页码:5964-5973
通讯作者	Cai, Ting(caiting@ucas.ac.cn)
摘要	It is vital to develop general models that can be shared across subjects and sessions in the real-world deployment of electroencephalogram (EEG) emotion recognition systems. Many prior studies have exploited domain adaptation algorithms to alleviate the inter-subject and inter-session discrepancies of EEG distributions. However, these methods only aligned the global domain divergence, but overlooked the local domain divergence with respect to each emotion category. This degenerates the emotion-discriminating ability of the domain invariant features. In this paper, we argue that aligning the EEG data within the same emotion categories is important for generalizable and discriminative features. Hence, we propose the dynamic domain adaptation (DDA) algorithm where the global and local divergences are disposed by minimizing the global domain discrepancy and local subdomain discrepancy, respectively. To tackle the absence of emotion labels in the target domain, we introduce a dynamic training strategy where the model focuses on optimizing the global domain discrepancy in the early training steps, and then gradually switches to the local subdomain discrepancy. The DDA algorithm is formally implemented as an unsupervised version and a semi-supervised version for different experimental settings. Based on the coarse-to-fine alignment, our model achieves the average peak accuracy of 91.08%, 92.89% on SEED, and 81.58%, 80.82% on SEED-IV in the cross-subject and cross-session scenarios, respectively.
关键词	Brain-computer interface emotion recognition transfer learning domain adaptation
DOI	10.1109/JBHI.2022.3210158
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62106248] ; Zhejiang Provincial Natural Science Foundation of China[LQ20F030013] ; Ningbo Public Service Technology Foundation, China[202002N3181] ; Ningbo Public Service Technology Foundation, China[2021S152] ; Ningbo Science and Technology Service Industry Demonstration Project, China[2020F041] ; Medical Scientific Research Foundation of Zhejiang Province, China[2021KY1028]
项目资助者	National Natural Science Foundation of China ; Zhejiang Provincial Natural Science Foundation of China ; Ningbo Public Service Technology Foundation, China ; Ningbo Science and Technology Service Industry Demonstration Project, China ; Medical Scientific Research Foundation of Zhejiang Province, China
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号	WOS:000894943300020
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：13[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/173211/50983
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	Cai, Ting
作者单位	1.Univ Chinese Acad Sci, HwaMei Hosp, Ningbo 101408, Zhejiang, Peoples R China 2.Univ Chinese Acad Sci, Ningbo Inst Life & Hlth Ind, Ningbo 101408, Zhejiang, Peoples R China 3.Anhui Univ, Sch Comp Sci & Technol, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230093, Anhui, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100045, Peoples R China
推荐引用方式 GB/T 7714	Li, Zhunan,Zhu, Enwei,Jin, Ming,et al. Dynamic Domain Adaptation for Class-Aware Cross-Subject and Cross-Session EEG Emotion Recognition[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2022,26(12):5964-5973.
APA	Li, Zhunan.,Zhu, Enwei.,Jin, Ming.,Fan, Cunhang.,He, Huiguang.,...&Li, Jinpeng.(2022).Dynamic Domain Adaptation for Class-Aware Cross-Subject and Cross-Session EEG Emotion Recognition.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,26(12),5964-5973.
MLA	Li, Zhunan,et al."Dynamic Domain Adaptation for Class-Aware Cross-Subject and Cross-Session EEG Emotion Recognition".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26.12(2022):5964-5973.