CASIA OpenIR
Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG
Fan, Chen-Chen1,2; Yang, Hongjun1; Hou, Zeng-Guang1,2,3; Ni, Zhen-Liang1,2; Chen, Sheng1,2; Fang, Zhijie1,2
Source PublicationCOGNITIVE NEURODYNAMICS
ISSN1871-4080
2020-11-10
Pages9
Corresponding AuthorHou, Zeng-Guang(zengguang.hou@ia.ac.cn)
AbstractDeep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet. It contains a newly designed attention module: 3D-AM, which is used to learn the attention weights of EEG channels, time points, and feature maps. It provides a way to automatically learn the electrode and time selection. QNet uses a dual branch structure to fuse bilinear vectors for classification. It performs four, three, and two classes on the EEG Motor Movement/Imagery Dataset. The average cross-validation accuracy of 65.82%, 74.75%, and 82.88% was obtained, which are 7.24%, 4.93%, and 2.45% outperforms than the state-of-the-art, respectively. The article also visualizes the attention weights learned by QNet and shows its possible application for electrode channel selection.
KeywordEEG Motor imagery Convolutional neural network Bilinear vectors Attention mechanism
DOI10.1007/s11571-020-09649-8
WOS KeywordSINGLE-TRIAL EEG ; CLASSIFICATION
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018YFC2001700] ; National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[U1913601] ; Beijing Natural Science Foundation[L172050] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Science
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000588280200001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41759
Collection中国科学院自动化研究所
Corresponding AuthorHou, Zeng-Guang
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Fan, Chen-Chen,Yang, Hongjun,Hou, Zeng-Guang,et al. Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG[J]. COGNITIVE NEURODYNAMICS,2020:9.
APA Fan, Chen-Chen,Yang, Hongjun,Hou, Zeng-Guang,Ni, Zhen-Liang,Chen, Sheng,&Fang, Zhijie.(2020).Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG.COGNITIVE NEURODYNAMICS,9.
MLA Fan, Chen-Chen,et al."Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG".COGNITIVE NEURODYNAMICS (2020):9.
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