数据采集与处理2026，Vol.41Issue(4)：995-1009,15.DOI:10.16337/j.1004-9037.2026.04.006
基于多尺度空洞卷积网络的运动想象脑电信号分类模型
Motor Imagery EEG Signal Classification Based on Multi-Scale Dilated Convolutional Network
摘要
Abstract
Most motor imagery-based brain-computer interface studies rely on single-scale feature extraction methods,which use convolutional or recurrent structures with fixed receptive fields and struggle to comprehensively capture the temporal characteristics of electroencephalogram(EEG)signals.Aiming at the above problem,we propose a multi-scale dilated convolutional network(MSDCN)model for motor imagery EEG signal classification.The proposed model first extracts spatiotemporal features through two layers of one-dimensional convolution,then enhances its ability to model both short-term dynamic variations and long-term dependencies using multi-scale dilated convolutions.Additionally,a squeeze-and-excitation(SE)module is integrated to learn channel-wise feature importance,highlighting key feature channels and improving classification performance.Experimental results show that on the BCI CompetitionⅣ 2a dataset,the model achieves an accuracy of 84.1%for within-subject experiments and 69.1%for cross-subject experiments,and on the BCI Competition Ⅳ 2b dataset,the within-subject accuracy reaches 89.8%.关键词
脑电信号/运动想象/多尺度/空洞卷积Key words
electroencephalogram(EEG)signal/motor imagery(MI)/multi-scale/dilated convolution分类
医药卫生引用本文复制引用
张学军,董宣..基于多尺度空洞卷积网络的运动想象脑电信号分类模型[J].数据采集与处理,2026,41(4):995-1009,15.基金项目
江苏省研究生科研与实践创新计划项目(KYCX24_1162). Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX24_1162). （KYCX24_1162）
