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Research on real-time security authentication method based on EEG data features

Bin Liu

Bin Liu

Xiamen Institute of TechnologyXiamen, China
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Liu, Bin
, 
Liming Cao

Liming Cao

Xiamen Institute of TechnologyXiamen, China
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Cao, Liming
 and 
Tianke Fang

Tianke Fang

Xiamen Institute of TechnologyXiamen, China
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Fang, Tianke
  
Mar 19, 2025
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Published Online: Mar 19, 2025
Received: Oct 19, 2024
Accepted: Jan 30, 2025
DOI: https://doi.org/10.2478/amns-2025-0500
Keywords
© 2025 Bin Liu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The structure of CNN
The structure of CNN

Figure 2.

LSTM Simple structure diagram
LSTM Simple structure diagram

Figure 3.

EEG identity authentication system structure diagram
EEG identity authentication system structure diagram

Figure 4.

Classification performance test of multi-domain general feature extraction method
Classification performance test of multi-domain general feature extraction method

Figure 5.

Attack test for intruders
Attack test for intruders

15Valid duration distribution of 15 users

user Effective time period distribution(ms)
1 378~988
2 405~793
3 305~810
4 393~796
5 415~805
6 405~791
7 353~803
8 415~798
9 400~920
10 345~864
11 373~799
12 410~820
13 395~886
14 390~902
15 312~843
Mean 410~795

Classification results from LDA and CNN

ACC(%) FAR (%) FRR (%)
user LDA CNN-LSTM LDA CNN-LSTM LDA CNN-LSTM
1 83.295 91.352 16.836 5.961 16.699 11.284
2 93.774 96.682 6.23 3.367 6.815 3.302
3 89.19 98.075 16.848 3.493 4.729 0.825
4 89.826 93.88 12.544 5.813 7.504 6.805
5 92.886 88.45 9.169 7.976 5.454 15.404
6 92.779 97.684 7.107 2.189 6.52 2.628
7 81.846 85.173 18.433 14.439 17.258 15.359
8 89.993 90.781 12.207 4.745 7.024 13.992
9 81.713 90.508 22.851 7.849 13.937 10.848
10 85.048 94.273 17.614 7.554 12.28 4.091
11 89.679 93.266 10.83 6.029 9.253 7.27
12 81.163 93.043 19.782 6.889 17.619 6.498
13 84.348 91.509 18.222 7.325 13.216 9.33
14 92.054 91.706 9.044 9.505 6.959 7.371
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