Evaluation of entropy features and classifier performance in person authentication using resting-state EEG.

Introduction: Resting-state electroencephalogram (EEG) presents a promising biometric modality due to its inherent liveness detection and resistance to spoofing, addressing critical vulnerabilities in conventional systems. However, its deployment faces fundamental trade-offs among accuracy, robustness, and hardware efficiency, particularly concerning optimal electrode configuration, discriminative feature extraction, and classifier generalization. Methods: To address these challenges, this study systematically evaluates thirteen entropy measures-including spectral entropy (SpEn), refined composite multiscale entropy, fuzzy entropy, and sample entropy (SaEn) etc.-alongside six classifiers (Quadratic Discriminant Analysis (QDA), Random Forests and Support Vector Machines etc.) for person authentication. Using 32-channel EEG recordings from 26 healthy participants under rigorous leave-one-out cross-validation (LOOCV), we quantified the impact of electrode selection and feature-classifier pairing. Results: Key findings demonstrate: QDA classifier achieved peak performance of 96.8% accuracy using 30 electrodes. Critically, a streamlined 9-electrode portable configuration retained 96.1% accuracy, demonstrating robust performance with reduced hardware requirements. SpEn measure exhibited superior biometric discriminability compared with other entropy measures, exceeding SaEn by 13.8 percentage points. Conclusion: These results advance the design of portable EEG biometric devices while highlighting entropy features' scalability.