Knowledge Commons of Institute of Automation,CAS
Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity | |
Fu, Kaicheng1,2; Du, Changde1; Wang, Shengpei1; He, Huiguang1,2 | |
发表期刊 | IEEE Transactions on Neural Networks and Learning Systems |
ISSN | 2162-237X |
2022-11-08 | |
页码 | 1-15 |
摘要 | Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method. |
关键词 | Fine-grained Emotion Decoding Multi-view Learning Multi-label Learning Variational Autoencoder Product of Experts |
DOI | 10.1109/TNNLS.2022.3217767 |
关键词[WOS] | REPRESENTATION ; PARCELLATION ; CATEGORIES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2021ZD0201503] ; National Natural Science Foundation of China[62206284] ; National Natural Science Foundation of China[61976209] ; National Natural Science Foundation of China[61906188] ; Beijing Natural Science Foundation[J210010] ; Beijing Natural Science Foundation[7222311] ; Strategic Priority Research Program of CAS[XDB32040200] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of CAS |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000881956100001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50715 |
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.Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fu, Kaicheng,Du, Changde,Wang, Shengpei,et al. Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity[J]. IEEE Transactions on Neural Networks and Learning Systems,2022:1-15. |
APA | Fu, Kaicheng,Du, Changde,Wang, Shengpei,&He, Huiguang.(2022).Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.IEEE Transactions on Neural Networks and Learning Systems,1-15. |
MLA | Fu, Kaicheng,et al."Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity".IEEE Transactions on Neural Networks and Learning Systems (2022):1-15. |
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