An Attention Network with Continuous Wavelet Convolution and Graph Embedding

ZHANG Lu; LI Ming-ai

doi:10.12263/DZXB.20250435

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An Attention Network with Continuous Wavelet Convolution and Graph Embedding

PAPERS | 更新时间：2026-04-24

- An Attention Network with Continuous Wavelet Convolution and Graph Embedding
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4337-4348(2025)
- 作者机构：
  
  北京工业大学信息科学技术学院，北京 100124
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62173010)
- DOI：10.12263/DZXB.20250435
  CLC： TP391;
- Received：28 May 2025，
  
  Accepted：10 December 2025，
  
  Published：25 December 2025
- 稿件说明：
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张璐, 李明爱. 一种融合连续小波卷积与图嵌入的注意力网络[J]. 电子学报, 2025, 53(12): 4337-4348.

ZHANG Lu, LI Ming-ai. An Attention Network with Continuous Wavelet Convolution and Graph Embedding[J]. Acta Electronica Sinica, 2025, 53(12): 4337-4348.
张璐, 李明爱. 一种融合连续小波卷积与图嵌入的注意力网络[J]. 电子学报, 2025, 53(12): 4337-4348. DOI：10.12263/DZXB.20250435

ZHANG Lu, LI Ming-ai. An Attention Network with Continuous Wavelet Convolution and Graph Embedding[J]. Acta Electronica Sinica, 2025, 53(12): 4337-4348. DOI：10.12263/DZXB.20250435

摘要

基于深度学习模型解码运动想象脑电信号（Motor Imagery ElectroEncephaloGram，MI-EEG）是脑机接口（Brain-Computer Interface，BCI）技术领域的热点研究问题之一.针对MI-EEG的时频特点和个体差异性，众多研究对其进行时频分析并将其时频表示广泛用于MI-EEG解码.然而，现有方法大多忽略了MI-EEG的多电极空间分布特性，未能很好地挖掘和利用不同电极间的拓扑关系，从而影响了特征信息的完整性，限制了解码性能的改善.为自适应学习多电极MI-EEG间的拓扑信息，并有效增强其时频空特征信息，本文提出一种融合连续小波卷积与图嵌入的注意力网络（Attention Network with Continuous Wavelet Convolution and Graph Embedding，CWC-GEAN）.该网络包含5个模块：多分支连续小波卷积模块（Multi-branch Continuous Wavelet Convolution Module，MCWCM）、多分支动态图嵌入模块（Multi-branch dynamic Graph Embedding Module，MGEM）、多分支特征通道注意力模块（Multi-branch Feature Channel Attention Module，MFCAM）、多分支特征通道-时间注意力模块（Multi-branch Feature Channel-Time Attention Module，MFCTAM）及特征融合与分类模块（Feature Fusion and Classification Block，FFCB）.首先，将原始多电极MI-EEG信号输入多分支连续小波卷积模块，在4个分支中分别基于4个子频带（1~8 Hz、9~16 Hz、17~24 Hz、25~32 Hz）进行连续小波卷积，通过对尺度因子的动态学习获得最优多尺度频-空-时特征表示；其次，基于互信息构建包含电极间拓扑信息的先验邻接矩阵，并经由MGEM，从不同子频带对先验邻接矩阵进行自适应学习调整，并嵌入对应分支频-空-时特征表示中，得到蕴含电极间拓扑信息的图结构特征；再次，由MFCAM和MFCTAM针对各分支图结构特征进一步提取深层特征，并相继完成特征通道注意力向量和特征通道-时间注意力矩阵的自动获取与特征加权，得到多分支判别性特征；最后，由FFCB对多分支判别性特征进行融合，得到最终的分类结果.本文基于公开的BCI Competition IV 2a数据集和High-Gamma Dataset数据集对CWC-GEAN进行性能评估，平均分类准确率分别为85.45%和95.09%，平均Kappa值分别为0.806和0.934.结果表明，CWC-GEAN具有自适应学习、捕捉MI-EEG时频信息和电极拓扑信息，以及增强时-频-空特征的能力，且展现出较好的模型鲁棒性和分类结果的一致性，相对流行方法具有一定性能优势.

Abstract

Decoding motor imagery electroencephalogram (MI-EEG) signals based on deep learning models is one of the hot research topics in the field of brain-computer interface (BCI) technology. Aiming at the time-frequency characteristics and individual differences of MI-EEG

numerous studies have conducted time-frequency analysis on MI-EEG and widely applied its time-frequency representations to MI-EEG decoding. However

most existing methods ignore the spatial distribution characteristics of multi-electrode MI-EEG and fail to fully explore and utilize the topological relationships between different electrodes

thereby affecting the integrity of feature information and limiting the further improvement of decoding performance. To adaptively learn the topological information between multi-electrode MI-EEG and effectively enhance its time-frequency-spatial feature information

this paper proposes an attention network with continuous wavelet convolution and graph embedding (CWC-GEAN). The network consists of five modules: a multi-branch continuous wavelet convolution module (MCWCM)

a multi-branch dynamic graph embedding module (MGEM)

a multi-branch feature channel attention module (MFCAM)

a multi-branch feature channel-time attention module (MFCTAM)

and a feature fusion and classification block (FFCB). First

the original multi-electrode MI-EEG signals are input into the MCWCM

where continuous wavelet convolution is performed based on four sub-bands(1 Hz to 8 Hz

9 Hz to 16 Hz

17 Hz to 24 Hz

25 Hz to 32 Hz) in four branches respectively

and the optimal multi-scale frequency-spatial-temporal feature representations are obtained through dynamic learning of scale factors. Then

a prior adjacency matrix containing topological information between electrodes is constructed based on mutual information

and the prior adjacency matrix is adaptively learned and adjusted from different sub-bands via the MGEM

which is embedded into the frequency-spatial-temporal feature representations of corresponding branches to obtain graph structure features containing topological information between electrodes. Furthermore

the MFCAM and the MFCTAM further extract deep features from the graph structure features of each branch

and successively complete the automatic acquisition of feature channel attention vectors and feature channel-time attention matrices as well as feature weighting to obtain multi-branch discriminative features. Finally

the FFCB fuses the multi-branch discriminative features to obtain the final classification results. In this paper

the performance of CWC-GEAN is evaluated based on the public BCI Competition IV 2a dataset and High-Gamma Dataset

with average classification accuracies of 85.45% and 95.09%

and average Kappa values of 0.806 and 0.934

respectively. The results show that CWC-GEAN has the ability to adaptively learn and capture the time-frequency information and electrode topological information of MI-EEG

as well as enhance time-frequency-spatial features

and exhibits good model robustness and consistency of classification results

with certain performance advantages over popular methods.

关键词

Keywords

references

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