文献类型：期刊文献

英文题名：Multi-Domain Dynamic Weighting Network for Motor Imagery Decoding

作者：Wang, Chongfeng[1];Allison, Brendan Z.[2];Wu, Xiao[1];Li, Junxian[1];Zhao, Ruiyu[1];Chen, Weijie[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4];Jin, Jing[1,5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Calif San Diego, Dept Cognit Sci, San Diego, CA 92093 USA;[3]Polish Acad Sci, Syst Res Inst, 6 Newelska St, PL-01447 Warsaw, Poland;[4]Nicolaus Copernicus Univ, Jurija Gagarina 11, PL-87100 Torun, Poland;[5]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

年份：2026

卷号：36

期号：04

外文期刊名：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

收录：;EI(收录号：20260720079711);WOS:【SCI-EXPANDED(收录号:WOS:001650998600001)】；

基金：This work was supported by the Grant National Natural Science Foundation of China under Grant 62176090 and STI 2030-major projects 2022ZD0208900; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX. This research is also supported by the Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key research and development plan industry foresight fundamental research fund for the central universities JKH01241605 and key core technologies) under Grant BE2022064-1.

语种：英文

外文关键词：EEG classification; motor imagery; feature fusion; multi-domain dynamic weighted network

摘要：In motor imagery (MI)-based brain-computer interfaces (BCIs), convolutional neural networks (CNNs) are widely employed to decode electroencephalogram (EEG) signals. However, due to their fixed kernel sizes and uniform attention to features, CNNs struggle to fully capture the time-frequency features of EEG signals. To address this limitation, this paper proposes the Multi-Domain Dynamic Weighted Network (MD-DWNet), which integrates multimodal complementary feature information across time, frequency, and spatial domains through a branch structure to enhance decoding performance. Specifically, MD-DWNet combines multi-band filtering, spatial convolution, and temporal variance calculation to extract spatial-spectral features, while a dual-scale CNN captures local spatiotemporal features at different time scales. A dynamic global filter is designed to optimize fused features, improving the adaptive modeling capability for dynamic changes in frequency band energy. A lightweight mixed attention mechanism selectively enhances salient channel and spatial features. The dual-branch joint loss function adaptively balances contributions through a task uncertainty mechanism, thereby enhancing optimization efficiency and generalization capability. Experimental results on the BCI Competition IV 2a, IV 2b, OpenBMI, and a self-collected laboratory dataset demonstrate that MD-DWNet achieves classification accuracies of 83.86%, 88.67%, 75.25% and 84.85%, respectively, outperforming several advanced methods and validating its superior performance in MI signal decoding.