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ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface

文献类型：期刊论文

作者Tao, Wei7,8; Wang, Ze5,6; Wong, Chi Man7,8; Jia, Ziyu4; Li, Chang3; Chen, Xun2; Chen, C. L. Philip1; Wan, Feng7,8
刊名IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
出版日期2024
卷号32页码:154-165
ISSN号1534-4320
关键词Convolutional neural networks (CNNs) motor imagery (MI) brain-computer interface (BCI) self-attention mechanism
DOI10.1109/TNSRE.2023.3342331
通讯作者Wan, Feng(fwan@um.edu.mo)
英文摘要Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain-computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG mu and beta rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.
WOS关键词EEG ; CLASSIFICATION ; TRANSFORMER
资助项目The Science and Technology Development Fund
WOS研究方向Engineering ; Rehabilitation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001144547700020
资助机构The Science and Technology Development Fund
源URL[http://ir.ia.ac.cn/handle/173211/55473]  
专题脑图谱与类脑智能实验室
通讯作者Wan, Feng
作者单位1.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 230027, Peoples R China
2.Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China
3.Hefei Univ Technol, Dept Biomed Engn, Hefei 230009, Peoples R China
4.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
5.Macau Univ Sci & Technol, Fac Innovat Engn, Resp Dis AI Lab Epidem Intelligence & Med Big Data, Taipa, Macau, Peoples R China
6.Macau Univ Sci & Technol, Fac Innovat Engn, Macao Ctr Math Sci, Taipa, Macau, Peoples R China
7.Univ Macau, Inst Collaborat Innovat, Taipa, Macau, Peoples R China
8.Univ Macau, Fac Sci & Technol, Ctr Cognit & Brain Sci, Ctr Artificial Intelligence & Robot,Dept Elect & C, Taipa, Macau, Peoples R China
推荐引用方式
GB/T 7714		 Tao, Wei,Wang, Ze,Wong, Chi Man,et al. ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2024,32:154-165.
APA Tao, Wei.,Wang, Ze.,Wong, Chi Man.,Jia, Ziyu.,Li, Chang.,...&Wan, Feng.(2024).ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,32,154-165.
MLA Tao, Wei,et al."ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 32(2024):154-165.

入库方式： OAI收割

来源：自动化研究所

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