中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 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

文献类型：期刊论文

作者Miao, Yifan2,3,4; Jiang, Wanqing2,3,4; Su, Nuo1; Shan, Jun1; Jiang, Tianzi2,3,4; Zuo, Nianming2,3,4
刊名IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
出版日期2023-12-01
卷号27期号:12页码:5767-5778
ISSN号2168-2194
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
DOI10.1109/JBHI.2023.3315974
通讯作者Zuo, Nianming(sjun@stu.ouc.edu.cn)
英文摘要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.
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]
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001147165700008
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/55476]  
专题脑图谱与类脑智能实验室
通讯作者Zuo, Nianming
作者单位1.Ocean Univ China, Coll Comp Sci & Technol, Qingdao 266100, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。