基于多尺度滑窗注意力时序卷积网络的脑电信号分类

doi:10.3785/j.issn.1008-973X.2026.02.015

浙江大学学报(工学版)

2026, Vol. 60

Issue (2): 370-378 DOI: 10.3785/j.issn.1008-973X.2026.02.015

计算机技术与控制工程

基于多尺度滑窗注意力时序卷积网络的脑电信号分类

李宪华1,2(

),杜鹏飞3,宋韬4,邱洵3,蔡钰3

1. 安徽理工大学第一附属医院，安徽淮南 232001
2. 安徽理工大学机电工程学院，安徽淮南 232001
3. 安徽理工大学人工智能学院，安徽淮南 232001
4. 上海大学机电工程与自动化学院，上海 200444

EEG signal classification based on multi-scale sliding-window attention temporal convolutional networks

Xianhua LI1,2(

),Pengfei DU3,Tao SONG4,Xun QIU3,Yu CAI3

1. The First Hospital of Anhui University of Science and Technology, Huainan 232001, China
2. School of Mechatronics Engineering, Anhui University of Science and Technology, Huainan 232001, China
3. School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China
4. School of Electrical and Mechanical Engineering, Shanghai University, Shanghai 200444, China

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摘要：

为了提升运动想象脑电（MI-EEG）信号的分类精度，提出多尺度滑窗注意力时序卷积网络（MSWATCN），充分挖掘MI-EEG信号的时空信息. 结合多尺度双流分组卷积、滑动窗口多头注意力机制和窗口化时间卷积模块，实现对MI-EEG信号复杂时空特性的精准解码. 利用多尺度卷积模块提取信号的底层时空特征，通过滑动窗口注意力机制聚焦局部关键特征，突出对分类任务重要的信息. 窗口化时间卷积模块通过建模时间序列中的长期依赖关系，增强模型处理时序信息的能力. 实验结果表明，MSWATCN在BCI Competition IV 2a和2b数据集上的分类准确率和一致性优于对比网络和基准模型.

关键词： 运动想象; 多尺度卷积; 多头注意力机制; 滑动窗口; 时序卷积网络

Abstract:

A multi-scale sliding-window attention temporal convolutional network (MSWATCN) was proposed to fully exploit the spatio-temporal information of motor imagery electroencephalography (MI-EEG) signals for enhanced classification accuracy. Accurate decoding of the complex spatio-temporal characteristics of MI-EEG signals was achieved by combining multiscale two-stream group convolution, a sliding-window multi-head attention mechanism, and a windowed temporal convolution module. The underlying spatio-temporal features of the signal were first extracted using the multi-scale convolution module, followed by a focus on local key features through the sliding-window attention mechanism to highlight information crucial for classification. The windowed temporal convolution module was employed to model long-term dependencies in the time series, whereby the model’s ability to encode sequential information was significantly improved. Experimental results showed that MSWATCN outperformed all comparison networks and benchmark models in terms of classification accuracy and consistency on the BCI Competition IV 2a and 2b datasets.

Key words: motion imagery multi-scale convolution multi-head attention mechanism sliding window temporal convolutional network

收稿日期: 2025-02-25 出版日期: 2026-02-03

CLC:

TP 391

基金资助: 安徽省重点研究与开发计划项目（2022i01020015）；安徽理工大学医学专项项目（YZ2023H2B013）.

作者简介: 李宪华（1980—），男，教授，从事机器人技术、脑-机接口研究. orcid.org/0000-0002-0524-2469. E-mail：xhli01@163.com

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引用本文:

李宪华,杜鹏飞,宋韬,邱洵,蔡钰. 基于多尺度滑窗注意力时序卷积网络的脑电信号分类[J]. 浙江大学学报(工学版), 2026, 60(2): 370-378.

Xianhua LI,Pengfei DU,Tao SONG,Xun QIU,Yu CAI. EEG signal classification based on multi-scale sliding-window attention temporal convolutional networks. Journal of ZheJiang University (Engineering Science), 2026, 60(2): 370-378.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2026.02.015 或 https://www.zjujournals.com/eng/CN/Y2026/V60/I2/370

图 1 多尺度滑窗注意力时序卷积网络的整体架构图

图 2 多尺度双流分组卷积模块

图 3 多头注意力模块结构

图 4 时间卷积网络模块

表 1 不同分类方法在BCI Competition IV 2a数据集上的分类准确率

表 2 不同分类方法在BCI Competition IV 2b数据集上的分类准确率

图 5 多尺度卷积结构

图 6 BCI Competition IV 2a数据集中不同模块的t-SNE特征可视化结果

图 7 BCI Competition IV 2b数据集中不同模块的t-SNE特征可视化结果

图 8 滑动窗口数量与分类准确率的关系分析

表 3 多尺度滑窗注意力时序卷积网络的模块消融实验

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