Knowledge Commons of Institute of Automation，CAS
| 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
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| Source Publication | COGNITIVE NEURODYNAMICS
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| ISSN | 1871-4080 |
| 2020-11-10 | |
| Pages | 9 |
| Corresponding Author | Hou, Zeng-Guang(zengguang.hou@ia.ac.cn) |
| Abstract | Deep 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. |
| Keyword | EEG Motor imagery Convolutional neural network Bilinear vectors Attention mechanism |
| DOI | 10.1007/s11571-020-09649-8 |
| WOS Keyword | SINGLE-TRIAL EEG ; CLASSIFICATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National 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 Organization | National 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 Area | Neurosciences & Neurology |
| WOS Subject | Neurosciences |
| WOS ID | WOS:000588280200001 |
| Publisher | SPRINGER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/41759 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Hou, Zeng-Guang |
| Affiliation | 1.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 Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute 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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