Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition

doi:10.1109/TCYB.2019.2904052

	Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition
	Li, Jinpeng1,2 ; Qiu, Shuang1,2 ; Shen, Yuan-Yuan2,3 ; Liu, Cheng-Lin2,3,4 ; He, Huiguang1,2,4
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2020-07-01
卷号	50 期号:7 页码:3281-3293
通讯作者	He, Huiguang(huiguang.he@ia.ac.cn)
摘要	Electroencephalogram (EEG) has been widely used in emotion recognition due to its high temporal resolution and reliability. Since the individual differences of EEG are large, the emotion recognition models could not be shared across persons, and we need to collect new labeled data to train personal models for new users. In some applications, we hope to acquire models for new persons as fast as possible, and reduce the demand for the labeled data amount. To achieve this goal, we propose a multisource transfer learning method, where existing persons are sources, and the new person is the target. The target data are divided into calibration sessions for training and subsequent sessions for test. The first stage of the method is source selection aimed at locating appropriate sources. The second is style transfer mapping, which reduces the EEG differences between the target and each source. We use few labeled data in the calibration sessions to conduct source selection and style transfer. Finally, we integrate the source models to recognize emotions in the subsequent sessions. The experimental results show that the three-category classification accuracy on benchmark SEED improves by 12.72% comparing with the nontransfer method. Our method facilitates the fast deployment of emotion recognition models by reducing the reliance on the labeled data amount, which has practical significance especially in fast-deployment scenarios.
关键词	Brain modeling Electroencephalography Emotion recognition Data models Training Calibration Training data Brain-computer interface emotion recognition transfer learning (TL)
DOI	10.1109/TCYB.2019.2904052
关键词[WOS]	DIFFERENTIAL ENTROPY FEATURE ; BRAIN
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[91520202] ; National Natural Science Foundation of China[81701785] ; Chinese Academy of Sciences (CAS) Scientific Equipment Development Project[YJKYYQ20170050] ; Beijing Municipal Science and Technology Commission[Z181100008918010] ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of CAS
项目资助者	National Natural Science Foundation of China ; Chinese Academy of Sciences (CAS) Scientific Equipment Development Project ; Beijing Municipal Science and Technology Commission ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of CAS
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000544035300035
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：198[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/173211/40054
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, Inst Automat, Res Ctr Braininspired Intelligence, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.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,Shen, Yuan-Yuan,et al. Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(7):3281-3293.
APA	Li, Jinpeng,Qiu, Shuang,Shen, Yuan-Yuan,Liu, Cheng-Lin,&He, Huiguang.(2020).Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition.IEEE TRANSACTIONS ON CYBERNETICS,50(7),3281-3293.
MLA	Li, Jinpeng,et al."Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition".IEEE TRANSACTIONS ON CYBERNETICS 50.7(2020):3281-3293.