Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition | |
Li, Jinpeng3,4; Qiu, Shuang3,4; Shen, Yuan-Yuan2,3; Liu, Cheng-Lin1,2,3; He, Huiguang1,3,4 | |
刊名 | IEEE TRANSACTIONS ON CYBERNETICS |
2020-07-01 | |
卷号 | 50期号:7页码:3281-3293 |
关键词 | Brain modeling Electroencephalography Emotion recognition Data models Training Calibration Training data Brain-computer interface emotion recognition transfer learning (TL) |
ISSN号 | 2168-2267 |
DOI | 10.1109/TCYB.2019.2904052 |
通讯作者 | He, Huiguang(huiguang.he@ia.ac.cn) |
英文摘要 | 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. |
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 |
WOS关键词 | DIFFERENTIAL ENTROPY FEATURE ; BRAIN |
WOS研究方向 | Automation & Control Systems ; Computer Science |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000544035300035 |
资助机构 | 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 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40053] |
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, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, Res Ctr Braininspired Intelligence, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
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. |
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