基于多尺度空洞卷积网络的运动想象脑电信号分类模型OA

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%.