Interpretable Spectral Features for Cross-Session EEG Biometric Identification and Verification

Electroencephalogram (EEG)-based biometric sensing provides a promising pathway for secure and user-specific human authentication, but practical deployment remains limited by cross-session non-stationarity and potentially optimistic evaluation protocols caused by window- or event-level leakage. This study developed a cross-session EEG biometric sensing framework using interpretable spectral features and target-session calibration, with trial-level non-overlapping data partitioning to prevent overlap between training, calibration, and blind-test sets. Multi-channel EEG events were segmented into non-overlapping 2 s windows and aggregated into event-level samples to ensure strict isolation among training, validation, calibration, and blind testing subsets. Power spectral density, differential entropy, and log-variance features were evaluated using support vector machine, linear discriminant analysis, logistic regression, random forest, and a compact convolutional neural network designed for EEG decoding (EEGNet). Both closed-set identification and biometric verification were assessed using Rank-N accuracy, cumulative match characteristic curves, macro-F1, equal error rate, area under the curve, and true acceptance rate (TAR) at fixed false acceptance rate (FAR) levels. In the in-house cross-day dataset, limited target-session calibration improved Rank-1 accuracy from 44.82% to 98.70% for support vector machine (SVM) and from 19.93% to 99.94% for linear discriminant analysis (LDA). SVM with differential entropy achieved an equal error rate (EER) of 1.04 ± 0.60%, area under the receiver operating characteristic curve (AUC) of 0.9985 ± 0.0008, and TAR@FAR = 0.1% of 98.44 ± 1.06%. External validation on the public multi-session motor imagery dataset confirmed the calibration benefit. These results demonstrate strong closed-set identification and score-level verification performance under a cohort-wide, calibration-assisted cross-session experimental protocol.