文献类型：期刊文献

英文题名：Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces

作者：Jin, Jing[1,2];Chen, Weijie[1];Xu, Ren[3];Liang, Wei[1];Wu, Xiao[1];He, Xinjie[1];Wang, Xingyu[1];Cichocki, Andrzej[4,5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China;[3]G Tec Med Engn GmbH, A-4521 Schiedlberg, Austria;[4]Nicolaus Copernicus Univ, Dept Informat, PL-87100 Torun, Poland;[5]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Wako 3510198, Japan

年份：2025

卷号：29

期号：1

起止页码：198

外文期刊名：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

收录：;EI(收录号：20244117168146);WOS:【SCI-EXPANDED(收录号:WOS:001392851400042)】；

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

语种：英文

外文关键词：Motor imagery; convolution neural net- work; feature fusion; EEG classification; EEG classification; EEG classification

摘要：Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83.62% and 89.26%, respectively. On the laboratory dataset, MSTFNet achieves 86.68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66.31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.