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This paper presents an EEG-based user authentication system using Event-Related Potentials (ERPs) to distinguish legitimate users from impostors. Utilizing a publicly available EEG dataset, we implemented a comprehensive data processing pipeline, which included advanced preprocessing and feature extraction techniques. Multiple state-of-the-art machine learning classifiers, such as CatBoost and XGBoost, were evaluated to assess their effectiveness in user authentication. The results showed a very low average Equal Error Rate (EER) of 2.53%. Our study emphasizes the strength of the P300 and N400 responses in biometric authentication and demonstrates the potential of advanced ensemble classifiers in improving system accuracy. This research contributes to the development of EEG-based authentication and lays the groundwork for future studies aiming to create secure and practical biometric systems.
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This content will become publicly available on May 26, 2026
Cognitive and Memory-Driven EEG-Based Authentication: A Multi-Session Approach to Secure Biometric Systems
Biometric authentication systems face significant challenges due to the vulnerability of traditional methods like passwords and fingerprints to theft or imitation. Electroencephalography (EEG)-based authentication presents a promising alternative by using unique brainwave patterns. This study introduces a novel EEG-based authentication system that utilizes cognitive and memory-related stimuli to elicit distinct brainwave responses. By incorporating multi-session data collection, the system effectively accounts for temporal variability. Additionally, advanced feature extraction techniques capture spatial, temporal, and spectral characteristics, enhancing authentication accuracy. A comprehensive feature engineering pipeline is employed, evaluating various classifiers across different stimuli types. Findings reveal that memory-related tasks, particularly word recognition, consistently generate the most reliable EEG responses. Among the classifiers tested, Logistic Regression demonstrates the highest effectiveness. The system achieves robust performance across multiple sessions, demonstrating its potential for practical real-world deployment. These findings lay a solid foundation for advancing EEG-based biometric authentication, paving the way for more secure and practical implementations in both research and applied settings.
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- Award ID(s):
- 2340997
- PAR ID:
- 10627232
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-5341-8
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Location:
- Tampa/Clearwater, FL, USA
- Sponsoring Org:
- National Science Foundation
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