@inproceedings{58544709eaa34f8fa9fccd85db8c1da5,
title = "EEG-Based Biometrics for User Identification Using Deep Learning Method",
abstract = "Electroencephalogram (EEG) based biometric system is a relatively secure system that could be integrated into our daily lives replacing the existing biometric security systems that use fingerprints or facial features. In this paper, EEGNet has been used as a deep learning tool to build a biometric system by training the models using resting state raw EEG data. The dataset consists of 25 participants and the data was collected for 20 sessions over 10 weeks. The user identification accuracy obtained is 0.903 ± 0.090 and 0.923 ± 0.071 using 300 and 600 seconds of training data respectively. The effect of template aging had also been studied and found that the performance using 600 seconds for training dropped from 0.923 to 0.899 after one session and down to 0.847 after 14 sessions, which is around a 7 -week interval between training and testing of models.",
keywords = "Biometrics, Deep Learning, EEGNet, Raw EEG",
author = "Humaira Nisar and Cheong, {Jia Yet} and Yap, {Vooi Voon}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 8th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2024 ; Conference date: 03-09-2024 Through 05-09-2024",
year = "2024",
month = sep,
day = "3",
doi = "10.1109/ICSIPA62061.2024.10686270",
language = "English",
isbn = "9798350352375",
series = "IEEE International Conference on Signal and Image Processing Applications",
publisher = "IEEE Press",
booktitle = "2024 IEEE 8th International Conference on Signal and Image Processing Applications (ICSIPA)",
address = "United States of America",
}