MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification

doi:10.1109/JBHI.2023.3315974

CASIA OpenIR > 脑图谱与类脑智能实验室

	MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification
	Miao, Yifan 1,2,3; Jiang, Wanqing1,2,3 ; Su, Nuo 4; Shan, Jun 4; Jiang, Tianzi1,2,3 ; Zuo, Nianming1,2,3
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
	2023-12-01
卷号	27 期号:12 页码:5767-5778
通讯作者	Zuo, Nianming(sjun@stu.ouc.edu.cn)
摘要	Traditional individual identification methods, such as face and fingerprint recognition, carry the risk of personal information leakage. The uniqueness and privacy of electroencephalograms (EEG) and the popularization of EEG acquisition devices have intensified research on EEG-based individual identification in recent years. However, most existing work uses EEG signals from a single session or emotion, ignoring large differences between domains. As EEG signals do not satisfy the traditional deep learning assumption that training and test sets are independently and identically distributed, it is difficult for trained models to maintain good classification performance for new sessions or new emotions. In this article, an individual identification method, called Multi-Loss Domain Adaptor (MLDA), is proposed to deal with the differences between marginal and conditional distributions elicited by different domains. The proposed method consists of four parts: a) Feature extractor, which uses deep neural networks to extract deep features from EEG data; b) Label predictor, which uses full-layer networks to predict subject labels; c) Marginal distribution adaptation, which uses maximum mean discrepancy (MMD) to reduce marginal distribution differences; d) Associative domain adaptation, which adapts to conditional distribution differences. Using the MLDA method, the cross-session and cross-emotion EEG-based individual identification problem is addressed by reducing the influence of time and emotion. Experimental results confirmed that the method outperforms other state-of-the-art approaches.
关键词	Electroencephalography Feature extraction Task analysis Support vector machines Recording Motion pictures Brain modeling EEG biometric across mental states across time deep learning domain adaptation
DOI	10.1109/JBHI.2023.3315974
收录类别	SCI
语种	英语
资助项目	Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project[2021ZD0200200] ; National Natural Science Foundation of China[61971420] ; Science Frontier Program of the Chinese Academy of Sciences[QYZDJ-SSW-SMC019]
项目资助者	Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project ; National Natural Science Foundation of China ; Science Frontier Program of the Chinese Academy of Sciences
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:001147165700008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55477
专题	脑图谱与类脑智能实验室
通讯作者	Zuo, Nianming
作者单位	1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 4.Ocean Univ China, Coll Comp Sci & Technol, Qingdao 266100, Peoples R China
第一作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
通讯作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Miao, Yifan,Jiang, Wanqing,Su, Nuo,et al. MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2023,27(12):5767-5778.
APA	Miao, Yifan,Jiang, Wanqing,Su, Nuo,Shan, Jun,Jiang, Tianzi,&Zuo, Nianming.(2023).MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,27(12),5767-5778.
MLA	Miao, Yifan,et al."MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 27.12(2023):5767-5778.