Research on Epilepsy Classification and Recognition Based on Multi-Domain Feature Fusion via Multi-Head Cross-Attention Mechanism

CHANG Wen-wen; WANG Ya-jun; GUO Jin-cheng; SHU Kang; MA Yu

doi:10.12263/DZXB.20250911

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Research on Epilepsy Classification and Recognition Based on Multi-Domain Feature Fusion via Multi-Head Cross-Attention Mechanism

PAPERS | 更新时间：2026-04-24

- Research on Epilepsy Classification and Recognition Based on Multi-Domain Feature Fusion via Multi-Head Cross-Attention Mechanism
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4460-4473(2025)
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62366028;W2421090);Gansu Provincial Key Science and Technology Special Project(23ZDFA012);Natural Science Foundation of Gansu Province(24JRRA256)
- DOI：10.12263/DZXB.20250911
  CLC： TP391;
- Received：27 November 2025，
  
  Accepted：12 December 2025，
  
  Published：25 December 2025
- 稿件说明：
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常文文, 王亚俊, 郭晋成, 等. 基于多头交叉注意力机制融合多域特征的癫痫分类识别研究[J]. 电子学报, 2025, 53(12): 4460-4473.

CHANG Wen-wen, WANG Ya-jun, GUO Jin-cheng, et al. Research on Epilepsy Classification and Recognition Based on Multi-Domain Feature Fusion via Multi-Head Cross-Attention Mechanism[J]. Acta Electronica Sinica, 2025, 53(12): 4460-4473.
常文文, 王亚俊, 郭晋成, 等. 基于多头交叉注意力机制融合多域特征的癫痫分类识别研究[J]. 电子学报, 2025, 53(12): 4460-4473. DOI：10.12263/DZXB.20250911

CHANG Wen-wen, WANG Ya-jun, GUO Jin-cheng, et al. Research on Epilepsy Classification and Recognition Based on Multi-Domain Feature Fusion via Multi-Head Cross-Attention Mechanism[J]. Acta Electronica Sinica, 2025, 53(12): 4460-4473. DOI：10.12263/DZXB.20250911

摘要

针对癫痫脑电信号（ElectroEncephaloGram，EEG）发作前期（preictal）、发作期（ictal）和发作间期（interictal）三种状态下的检测和识别问题，提出了一种基于多头交叉注意力机制（Multi-Head Cross-Attention Mechanism，MHCA）融合脑电多域特征的分类识别模型.该模型通过连续小波变换（Continuous Wavelet Transform，CWT）对癫痫脑电信号生成的二维图像按照通道进行有序拼接，并利用浅层卷积神经网络（Convolutional Neural Network，CNN）对拼接后的时频图像进行特征提取以有效挖掘癫痫脑电信号的时频域特征.同时通过构建脑功能连接矩阵，刻画不同脑区之间的功能连接关系以捕捉癫痫发作过程中潜在的空域特征，最终采用MHCA实现时频特征与空域特征之间的全局交互与自适应融合，充分建模多维特征间的关联性与互补性，进而构建癫痫脑电信号在时域、频域与空域三个维度上的完整且统一的特征表征.实验结果表明，该模型在癫痫发作前期、发作期和发作间期的多被试分类中最高分类准确率可达92.49%，灵敏度可达92.48%，体现了其在跨被试场景下良好的泛化能力与稳定性；单被试分类中模型最高准确率为98.39%，灵敏度为98.04%，充分验证了该方法在个体化癫痫脑电识别任务中的有效性.消融实验最终也进一步证实了脑功能连接矩阵所表征的空域信息和多头交叉注意力机制在多域特征融合与判别特征增强中的关键作用，对模型的性能提升均具有正向贡献.本文针对癫痫脑电分类识别的有效性验证，不仅为临床脑电检测和识别提供了一种可靠且可行的技术手段，也为癫痫脑电信号中关键特征的提取、表征与建模提供了新的研究思路和方法参考.

Abstract

In this paper

we propose a classification and recognition model based on the multi-head cross-attention mechanism (MHCA) fusing EEG multi-domain features for the detection and recognition of epileptic electroencephalographic (EEG) signals in preictal

ictal and interictal states. The model is developed by sequentially splicing the two-dimensional images generated by continuous wavelet transform (CWT) of epileptic EEG signals according to the channels

and utilizing shallow convolutional neural network (CNN) to extract features from the spliced time-frequency images in order to effectively extract the time-frequency domain features of epileptic EEG signals. At the same time

the brain functional connectivity matrix is constructed to depict the functional connectivity between different brain regions to capture the potential spatial features during epileptic seizures

and finally

MHCA is used to realize the global interaction and adaptive fusion between time-frequency and spatial features to fully model the correlation and complementarity between multidimensional features

so as to construct a complete and unified feature characterization of epileptic EEG signals in the three dimensions of the time domain

the frequency domain

and the spatial domain. The experimental results show that the model can reach a maximum classification accuracy of 92.49% and a sensitivity of 92.48% in multi-subject classification of preictal

ictal and interictal phases

which reflects its good generalization ability and stability in cross-subject scenarios; and the model can reach a maximum accuracy of 98.39% and a sensitivity of 98.04% in single-subject classification

which fully verifies the efficacy of the method in the task of individualized epilepsy EEG recognition. The ablation experiments ultimately further confirmed the critical role of the spatial information represented by the brain functional connectivity matrix and the multi-head cross-attention mechanism in multi-domain feature fusion and discriminative feature enhancement

both of which positively contributed to the model's performance improvement. This paper validates the efficacy of epileptic EEG classification and recognition

which not only provides a reliable and feasible technical means for clinical EEG detection and recognition

but also provides new research ideas and methodological references for the extraction

characterization and modeling of key features in epileptic EEG signals.

关键词

Keywords

references

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