A Unified Deep Learning-Based EEG Biometric Authentication System for Cross-Session Scenarios

Yijing Gong, Min Wang, Yu Zhang, Wenjie Zhang, Shuchao Pang

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 20th International Conference, ADMA 2024, Proceedings
EditorsQuan Z. Sheng, Xuyun Zhang, Jia Wu, Congbo Ma, Gill Dobbie, Jing Jiang, Wei Emma Zhang, Yannis Manolopoulos, Wathiq Mansoor
PublisherSpringer
Pages48-62
Number of pages15
ISBN (Print)9789819608393
DOIs
Publication statusPublished - 2025
Event20th International Conference on Advanced Data Mining Applications, ADMA 2024 - Sydney, Australia
Duration: 3 Dec 20245 Dec 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15390 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Advanced Data Mining Applications, ADMA 2024
Country/TerritoryAustralia
CitySydney
Period3/12/245/12/24

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