文献类型：期刊文献

英文题名：Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model

作者：Chen, Weijie[1];Daly, Ian[2];Chen, Yixin[1];Wu, Xiao[1];Liang, Wei[1];He, Xinjie[1];Wang, Xingyu[1];Cichocki, Andrzej[3,4];Jin, Jing[5,6,7]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, Essex, England;[3]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[4]Nicolaus Copernicus Univ, PL-87100 Torun, Poland;[5]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[6]Sch Math, Shanghai 200237, Peoples R China;[7]East China Univ Sci & Technol, ECUST Med Engn Integrat Innovat Ctr, Shanghai 200237, Peoples R China

年份：2026

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20261620517593);WOS:【SCI-EXPANDED(收录号:WOS:001737599600001)】；

基金：This work was supported in part by the Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project under Grant 2022ZD0208900, in part by the Grant National Natural Science Foundation of China under Grant 62176090, in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX, in part 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 and Key Core Technologies) under Grant JKH01241605 and Grant BE2022064-1, and in part by the Lingang Laboratory under Grant LGL8998.

语种：英文

外文关键词：Feeds; Antennas; Filtering; Filters; Band-pass filters; Feedback; Filter banks; Circuits and systems; Neurofeedback; Active filters; Brain computer interface (BCI); deep neural network; electroencephalogram; motor imagery (MI)

摘要：Motor imagery (MI) is a popular noninvasive brain computer interface (BCI) paradigm, yet its decoding accuracy remains hindered by the inherent nonstationarity and low signal-to-noise ratio of electroencephalogram (EEG) signals. Current decoding frameworks often fail to fully exploit the intricate spatial-temporal dependencies, leading to suboptimal feature representation and the omission of latent discriminative cues. To address these challenges, we introduce a deep neural network-powered multifaceted strategy (DPMS-Net) model, a novel approach that employs dynamic convolution to unearth effective discriminative cues across multiple dimensions, including the temporal, spatial, and frequency domains. This model synergizes channel and temporal attention mechanisms to adeptly capture the salient features of EEG signals across diverse spatial-temporal dimensions, thereby mitigating the risk of omitting critical information. Furthermore, we introduce a spectral-domain analysis component that unearths subtle oscillatory signatures hidden within the EEG spectrum, providing enriched evidence for classification. We evaluated the performance of DPMS-Net on two publicly available datasets and a self-collected dataset from stroke patients. On the BCI Competition IV 2a and BCI Competition IV 2b datasets, DPMS-Net achieved subject-dependent classification accuracies of 83.93% and 88.38%, respectively, alongside subject-independent classification accuracies of 65.88% and 76.01%. In the stroke patient dataset, DPMS-Net attained a subject-dependent classification accuracy of 67.67% and a subject-independent classification accuracy of 57.58%. Experimental results indicate that DPMS-Net possesses efficient decoding capabilities and robust stability, reflecting its potential for deployment in neurorehabilitation BCI systems.