TY - GEN
T1 - A Unified Deep Learning-Based EEG Biometric Authentication System for Cross-Session Scenarios
AU - Gong, Yijing
AU - Wang, Min
AU - Zhang, Yu
AU - Zhang, Wenjie
AU - Pang, Shuchao
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant No. 62206128. Corresponding authors: Min Wang and Shuchao Pang.
Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Advancements in technology have heightened concerns over personal privacy and security. Electroencephalogram (EEG) signals, valued for their unique and non-forgeable characteristics, have garnered increasing interest for biometric verification. Yet challenges persist in real-world applications, including poor performance in cross-session recognition, lack of generalizability, and narrow focus on specific EEG elicitation protocols. In this paper, we propose a deep learning-based EEG biometric verification system. Our approach introduces advancements in feature extraction: starting with Fast Fourier Transform (FFT) for converting signals to frequency domain, followed by feature mining through a convolutional autoencoder. User verification is accomplished using a Convolutional Neural Network (CNN), known for its superior performance in data mining and classification tasks. In addition, to evaluate the generalizability of the proposed method, extensive experiments are carried out with EEG data collected under seven distinct signal elicitation protocols and over two different recording sessions. Results highlight the stability and reliability of the our method cross diverse scenarios. Comparative analysis with state-of-the-art approaches for EEG biometrics shows that our method excels in robust feature extraction, resulting in better verification performance.
AB - Advancements in technology have heightened concerns over personal privacy and security. Electroencephalogram (EEG) signals, valued for their unique and non-forgeable characteristics, have garnered increasing interest for biometric verification. Yet challenges persist in real-world applications, including poor performance in cross-session recognition, lack of generalizability, and narrow focus on specific EEG elicitation protocols. In this paper, we propose a deep learning-based EEG biometric verification system. Our approach introduces advancements in feature extraction: starting with Fast Fourier Transform (FFT) for converting signals to frequency domain, followed by feature mining through a convolutional autoencoder. User verification is accomplished using a Convolutional Neural Network (CNN), known for its superior performance in data mining and classification tasks. In addition, to evaluate the generalizability of the proposed method, extensive experiments are carried out with EEG data collected under seven distinct signal elicitation protocols and over two different recording sessions. Results highlight the stability and reliability of the our method cross diverse scenarios. Comparative analysis with state-of-the-art approaches for EEG biometrics shows that our method excels in robust feature extraction, resulting in better verification performance.
KW - Biometric Recognition
KW - Convolutional Autoencoder
KW - Convolutional Neural Network
KW - Deep learning
KW - EEG
UR - http://www.scopus.com/inward/record.url?scp=85213385837&partnerID=8YFLogxK
UR - https://adma2024.github.io/
U2 - 10.1007/978-981-96-0840-9_4
DO - 10.1007/978-981-96-0840-9_4
M3 - Conference contribution
AN - SCOPUS:85213385837
SN - 9789819608393
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 48
EP - 62
BT - Advanced Data Mining and Applications - 20th International Conference, ADMA 2024, Proceedings
A2 - Sheng, Quan Z.
A2 - Zhang, Xuyun
A2 - Wu, Jia
A2 - Ma, Congbo
A2 - Dobbie, Gill
A2 - Jiang, Jing
A2 - Zhang, Wei Emma
A2 - Manolopoulos, Yannis
A2 - Mansoor, Wathiq
PB - Springer
T2 - 20th International Conference on Advanced Data Mining Applications, ADMA 2024
Y2 - 3 December 2024 through 5 December 2024
ER -