现代电子技术2025，Vol.48Issue(16)：67-74,8.DOI:10.16652/j.issn.1004-373x.2025.16.012
基于MCSANet网络的运动想象脑电分类
Motor imagery EEG classification based on MCSANet
摘要
Abstract
In order to solve the problem of insufficient feature mining and insufficient utilization in decoding electroencephalography(EEG)signals by means of the traditional deep learning method,a deep learning model,MCSANet,is proposed,which combines the parallel multi-scale temporal convolution with sliding window technology and attention mechanism.The parallel multi-scale temporal convolution is used to effectively capture the temporal characteristics and spatial characteristics of EEG signals at different time scales.The sliding window slicing technology is used to divide the feature sequences and increase the number of sequence samples.The weights of each part of the sequence samples are assigned and fused by means of the multi-head self-attention mechanism,which can further highlight more key features.The fully connected layer and the SoftMax layer are used to work together,so as to perform in-depth learning and accurate classification for the captured features.In order to validate the performance of the model,an exhaustive experimental analysis was performed on the BCICIV-2a dataset.The experimental results show that the average classification accuracy of all subjects is as high as 81.69%,which verifies the effectiveness of the proposed method in mining the deep potential features of EEG and improving the classification performance of motor imagery EEG.关键词
脑机接口/脑电信号/并行多尺度时间卷积/滑动窗口切片技术/多头自注意力机制/消融实验Key words
braincomputer interface/EEG signal/parallel multi-scale temporal convolution/sliding window slicing technology/multi-head self-attention mechanism/ablation experiment分类
信息技术与安全科学引用本文复制引用
杜江,毕峰..基于MCSANet网络的运动想象脑电分类[J].现代电子技术,2025,48(16):67-74,8.基金项目
辽宁省教育厅高校基本科研项目(LJKMZ20221756) （LJKMZ20221756）
辽宁省科技计划联合计划项目:脑机接口系统中特征脑电信号的提取与分类关键技术研究(2024JH2/102600133) （2024JH2/102600133）
