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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2026, Vol. 60 Issue (3): 633-642    DOI: 10.3785/j.issn.1008-973X.2026.03.019
    
Lightweight brainprint recognition algorithm based on spatio-temporal attention mechanism
Fang FANG(),Jun YAN,Hongxiang GUO,Yong WANG*()
College of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
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Abstract  

A lightweight convolutional neural network based on a spatiotemporal attention mechanism was proposed in order to address the problems of high model complexity, large number of required channels, and reliance on long-duration signals in existing brainprint recognition methods. A coordinate attention mechanism was introduced to enhance spatial feature extraction and highlight key channel information. The VOV-GSCSP module was used to replace the first layer convolution of EEGNet based on the EEGNet network in order to improve the feature expression ability of the model for EEG signals without significantly increasing the number of parameters. The lightweight temporal self-attention module was integrated to effectively capture the dependencies across time steps while keeping the model lightweight to enhance the temporal modeling ability and make the network have more discriminative power. The method was validated on the PhysioNet dataset of 109 people and the DEAP dataset of 32 people. The classification accuracies based on the three states of motor imagery, eyes open and eyes closed were improved by 18.55%, 23.61% and 25.79% in the 8-channel condition of the PhysioNet dataset and by 2.45% in the 5-channel condition of the DEAP dataset compared with the baseline EEGNet network. The number of parameters of the proposed model was only 0.29×106, which was lower than most of the existing depth models, and the recognition effect was better in the case of lower number of channels and shorter time period. The effectiveness and robustness of the method in the brain pattern recognition task were demonstrated.

Key words： electroencephalographic signal      biometrics      attention mechanism      lightweight convolutional neural network     
Received: 09 April 2025      Published: 04 February 2026
CLC:  TP 391  
  R 318  
Fund:  国家自然科学基金资助项目（61973283）.
Corresponding Authors: Yong WANG     E-mail: 2170869118@qq.com;yongwang_cug@163.com
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Fang FANG
Jun YAN
Hongxiang GUO
Yong WANG
Cite this article:

Fang FANG,Jun YAN,Hongxiang GUO,Yong WANG. Lightweight brainprint recognition algorithm based on spatio-temporal attention mechanism. Journal of ZheJiang University (Engineering Science), 2026, 60(3): 633-642.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.03.019     OR     https://www.zjujournals.com/eng/Y2026/V60/I3/633


基于时空注意力机制的轻量级脑纹识别算法

针对现有脑纹识别方法模型复杂、所需通道数多以及依赖长时间段信号等问题，提出基于时空注意力机制的轻量化卷积神经网络. 引入坐标注意力机制以增强空间特征提取能力，突出关键通道信息. 基于EEGNet网络，使用VOV-GSCSP模块替换EEGNet的第1层卷积，在不明显增加参数量的同时，提升模型对脑电信号的特征表达能力. 融合轻量级时间自注意力模块，在保持模型轻量化的同时，有效捕捉跨时间步的依赖关系，提升时序建模能力，使网络更具判别力. 利用该方法，在109人的PhysioNet数据集和32人的DEAP数据集上进行验证. 与基线EEGNet网络相比，在PhysioNet数据集的8通道条件下，基于运动想象、睁眼和闭眼3种状态的分类准确率分别提高了18.55%、23.61%、25.79%，在DEAP数据集5通道条件下的分类准确率提高了2.45%. 提出模型的参数量仅为0.29×106，低于大多数现有的深度模型，且在通道数更低、时间段更短的情况下识别效果更佳，证明了该方法在脑纹识别任务中的有效性和鲁棒性.


关键词： 脑电信号,  生物识别,  注意力机制,  轻量化卷积神经网络 
Fig.1 Overall network architecture
Fig.2 Structure of coordinate attention
Fig.3 Structure of VOV-GSCSP network
实验参数参数选择ACC±std
Physionet-MIPhysionet-EOPhysionet-ECDEAP
卷积核数(16,64)
(64,128)		99.43±0.15
99.97±0.09		98.13±0.17
99.45±0.15		97.32±0.41
99.01±0.30		100.00±0.02
100.00±0.00
激活函数ELU
SiLU
GeLU
ReLU		99.95±0.11
99.96±0.10
99.97±0.10
99.97±0.09		98.64±0.23
98.70±0.21
99.15±0.17
99.45±0.15		98.26±0.39
98.34±0.36
97.35±0.45
99.01±0.30		99.92±0.08
100.00±0.01
100.00±0.00
100.00±0.00
注意力GCT
CA		99.95±0.13
99.97±0.09		99.12±0.16
99.45±0.15		98.34±0.33
99.01±0.30		100.00±0.01
100.00±0.00
池化层AvgPool2d
MaxPool2d		99.97±0.10
99.97±0.09		98.94±0.18
99.45±0.15		98.59±0.33
99.01±0.30		100.00±0.00
100.00±0.00
Dropout0.25
0.5		99.91±0.12
99.97±0.09		99.06±0.16
99.45±0.15		98.77±0.32
99.01±0.30		100.00±0.01
100.00±0.00
Tab.1 Effect of different parameter on classification performance for different state data of two datasets %
slot/sN(ACC±std)/%slot/sN(ACC±std)/%
4389.07±1.401364.57±2.25
4493.54±1.281471.99±1.77
4898.34±0.411891.67±1.31
41599.30±0.2511598.52±0.61
41699.75±0.2211698.63±0.53
41999.81±0.2111998.86±0.32
43299.90±0.2013299.48±0.24
46499.97±0.0216499.97±0.09
Tab.2 Physionet-MI performance under different setting
Fig.4 t-SNE visualization of Physionet-MI data with different parameter
数据集slot/sN(ACC±std)/%(EER±std)/%Np/106
Physionet-EO23
8
16
64		90.60±2.50
96.47±1.45
98.70±0.73
99.45±0.15		0.179±0.091
0.014±0.010
0.002±0.008
0.000±0.001		0.29
0.29
0.30
0.33
Physionet-EC23
8
16
64		90.16±2.49
96.74±1.63
97.53±1.06
99.01±0.30		0.137±0.070
0.011±0.010
0.003±0.009
0.000±0.001		0.29
0.29
0.30
0.33
DEAP105
32		99.98±0.06
100.00±0.00		0.000±0.000
0.000±0.000		0.28
0.29
Tab.3 Test accuracy of different dataset with different parameter
模型(ACC±std)/%
Physionet-MIPhysionet-EOPhysionet-ECDEAP
EEGNet79.79±2.4972.86±3.2170.95±3.4597.53±0.25
VEEGNet80.83±2.2174.18±3.0372.66±3.2299.89±0.20
CA+EEGNet81.00±2.0675.53±2.8973.28±2.9599.93±0.17
EEGNet+LTAE96.01±0.8989.28±1.9690.21±2.0199.54±0.18
CA+VEEGNet90.54±1.5280.76±2.4575.37±2.7899.93±0.15
VEEGNet+LTAE98.25±0.4894.85±1.5594.85±1.7299.97±0.08
CA+EEGNet+LTAE98.18±0.5291.63±1.7292.11±1.8999.96±0.09
Proposed98.34±0.4196.47±1.4596.74±1.6399.98±0.06
Tab.4 Comparison of result of ablation experiment
方法slot/sNACC/%EER/%Np/106
COH_CNN[21]
COH_CNN[21]		1
1		19
15		98.22
97.74		—
—		—
—
CNN-LSTM[8]1
1		16
64		99.58
99.58		0.410
0.410		1927.50
—
多任务对抗学习[22]16499.20——
BGWO-SVM[23]12394.13——
EPI-CGAN[24]16499.02——
PCA+SVM[25]26499.91——
DNN[26]46497.81——
GCT-EEGNet[27]13298.900.0040.17
ResNet18[28]16499.59—11.25
提出方法
提出方法
提出方法
提出方法
提出方法		1
1
1
1
1		15
16
19
32
64		98.52
98.63
98.86
99.48
99.97		0.001
0.001
0.001
0.001
0.000		0.29
0.29
0.29
0.30
0.32
Tab.5 Comparison of motor imagery data for Physionet dataset
方法slot/sNEOECNp/106
ACC/%EER/%ACC/%EER/%
连通性网络[11]126496.904.40092.606.500—
CNN-LSTM[29]
CNN-LSTM[29]
CNN-LSTM[29]
CNN-LSTM[29]		4
8
12
16		64
64
64
64		95.00
96.20
98.00
92.50		—
—
—
—		95.33
97.00
99.95
93.20		—
—
—
—		—
—
—
—
COR+GCN[13]
COR+GCN[13]
COR+GCN[13]		1
1
1		64
40
16		98.56
97.13
53.41		—
—
—		—
—
—		—
—
—		—
—
—
FDF+SVM_RBF[30]106497.22————
RF[31]26498.16—97.30——
SVM[31]26497.64—96.02——
MCL+马氏距离分类器[32]106499.406.33098.8010.500—
CNN+数据增强[9]1264———0.190300.82
PLV+Gamma[12]
PLV+Gamma[12]		4
4		56
21		99.40
96.00		—
—		—
—		—
—		—
—
ESTformer[33]106494.61———33.70
Autoencoder-CNN[34]86499.45—99.89——
提出方法
提出方法
提出方法
提出方法		2
2
2
2		3
8
16
64		90.60
96.47
98.70
99.45		0.179
0.014
0.002
0.000		90.16
96.74
97.53
99.01		0.137
0.011
0.005
0.000		0.29
0.29
0.30
0.33
Tab.6 Comparison of resting state data for Physionet dataset
方法slot/sNACC/%Np/106
CNN-GRU[35]
CNN-GRU[35]		10
10		5
32		99.17
99.90		1.89
—
MLP[36]
MLP[36]		1
3		32
32		95.00
99.00		—
—
EPI-CGAN[24]123299.88—
提出方法
提出方法		10
10		5
32		99.98
100.00		0.28
0.29
Tab.7 Comparison of DEAP dataset
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