Chae, Min Seong Rehman, Abdul Kim, Yeni Kim, Jaedeok Han, Yaeeun Park, Sangin Mun, Sungchul 2025-11-17T02:02:26Z 2025-11-17T02:02:26Z 2025-11-11 2025-09 https://pubs.kist.re.kr/handle/201004/153496 The growing demand for secure, immersive authentication in extended reality (XR) environments calls for neural interfaces that are both robust and user-friendly. This study introduces a novel and robust dual-modality EEG-based authentication framework that independently exploits: 1) steady-state visually evoked potentials (SSVEP) and 2) eye-blink-induced EEG responses as covert neural signatures. Both signals are recorded using a 64-channel EEG system seamlessly integrated with the Microsoft HoloLens 2 for immersive XR-based user evaluation. To mitigate visual fatigue while preserving signal fidelity, we replace conventional flicker stimuli with a 10 Hz grow-shrink visual design. We employ a modality-specific classification strategy, modeling SSVEP and eye-blink signals independently to retain their distinct neurophysiological characteristics. A multi-stage feature selection pipeline combines SHAP and Random Forest rankings, followed by logistic regression-based permutation importance to identify the top 10 discriminative features per modality. These features undergo statistical validation via non-parametric tests to ensure physiological plausibility and class separability. Classification is subsequently performed using four machine learning models-Random Forest, XGBoost, Support Vector Machine, and Logistic Regression-with Random Forest and XGBoost consistently yielding the highest performance. Evaluated across 20 participants using user-wise validation, our framework achieves over 99% accuracy and near-perfect ROC-AUC scores for both modalities, confirming strong discriminability between genuine and impostor attempts. Our results demonstrate that interpretable, fatigue-aware EEG features can deliver high authentication performance under XR conditions. The proposed system is lightweight and explicitly engineered for real-time deployment and spoof-resistance, making it well-suited for future XR-based defense, training, and industrial applications. English Institute of Electrical and Electronics Engineers Inc. Eye-Blink and SSVEP-Based Selective Attention Interface for XR User Authentication: An Explainable Neural Decoding and Machine Learning Approach to Reducing Visual Fatigue Article 10.1109/ACCESS.2025.3613355 1 IEEE Access, v.13, pp.176998 - 177018 IEEE Access 13 176998 177018 Y scie scopus 001596864800002 2-s2.0-105017390098 Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Telecommunications Article EEG BIOMETRICS Electroencephalography Authentication Visualization Protocols Brain modeling Biometrics Resists Fatigue Biomedical monitoring Feature extraction Biometric security EEG eye blink Explainable AI neural decoding SHAP SSVEP visual fatigue XR authentication.