文献类型：期刊文献

英文题名：Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module

作者：Wu, Runze[1];Jin, Jing[1,6];Daly, Ian[2];Wang, Xingyu[1];Cichocki, Andrzej[3,4,5]

机构：[1]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, 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]RIKEN Brain Sci Inst, Lab Adv Brain Signal Proc, Wako 3510198, Japan;[4]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[5]Nicolaus Copernicus Univ, Dept Informat, PL-87100 Torun, Poland;[6]East China Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China

年份：2023

卷号：31

起止页码：3075

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20232914403985);WOS:【SCI-EXPANDED(收录号:WOS:001041977800001)】；

基金：This work was supported in part by the Scientific and Technological Innovation (STI) 2030-Major Projects under Grant 2022ZD0208900; in part by the National Natural Science Foundation of China under Grant 62176090; in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; in part by the Shuguang Project supported by the Shanghai Municipal Education Commission and the Shanghai Education Development Foundation under Grant 19SG25; in part by the Polish National Science Center under Grant UMO-2016/20/W/NZ4/00354; in part by the National Government Guided Special Funds for Local Science and Technology Development (Shenzhen, China) under Grant 2021Szvup043; and in part by the Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key Research and Development Plan Industry Foresight and Key Core Technologies) under Grant BE2022064-1.

语种：英文

外文关键词：Motor imagery; EEG; multi-scale convolu-tion; convolution neural network; attention module

摘要：Motor imagery (MI) is a popular paradigm for controlling electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to accurately classify MI-related EEG activity. Recently, the development of deep learning has begun to draw increasing attention in the BCI research community because it does not need to use sophisticated signal preprocessing and can automatically extract features. In this paper, we propose a deep learning model for use in MI-based BCI systems. Our model makes use of a convolutional neural network based on a multi-scale and channel-temporal attention module (CTAM), which called MSCTANN. The multi-scale module is able to extract a large number of features, while the attention module includes both a channel attention module and a temporal attention module, which together allow the model to focus attention on the most important features extracted from the data. The multi-scale module and the attention module are connected by a residual module, which avoids the degradation of the network. Our network model is built from these three core modules, which combine to improve the recognition ability of the network for EEG signals. Our experimental results on three datasets (BCI competition IV 2a, III IIIa and IV 1) show that our proposed method has better performance than other state-of-the-art methods, with accuracy rates of 80.6%, 83.56% and 79.84%. Our model has stable performance in decoding EEG signals and achieves efficient classification performance while using fewer network parameters than other comparable state-of-the-art methods.