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.