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Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity

文献类型：期刊论文

作者Fu, Kaicheng2,3; Du, Changde3; Wang, Shengpei3; He, Huiguang1,2,3
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-11-08
页码15
ISSN号2162-237X
关键词Decoding Brain modeling Functional magnetic resonance imaging Predictive models Emotion recognition Dimensionality reduction Pattern recognition Fine-grained emotion decoding multi-label learning multi-view learning product of experts (PoEs) variational autoencoder
DOI10.1109/TNNLS.2022.3217767
通讯作者He, Huiguang(huiguang.he@ia.ac.cn)
英文摘要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.
WOS关键词REPRESENTATION ; PARCELLATION ; CATEGORIES
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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000881956100001
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of CAS
源URL[http://ir.ia.ac.cn/handle/173211/50714]  
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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,2022:15.
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,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):15.

入库方式： OAI收割

来源：自动化研究所

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