Abstract Background Electroencephalography (EEG) provides a low-cost, non-invasive view of millisecond-scale brain dynamics; however, its clinical value depends on reliable probabilities and deployment-aware evaluation, rather than accuracy alone. Objective This study aimed to deliver a simple EEG approach for Alzheimer’s Diagnosis (AD) that returns calibrated subject-level probabilities and reports pre-specified clinical operating points, all under subject-wise, leakage-free validation. Methods Resting-state, eyes-closed EEG from OpenNeuro ds004504 was pre-processed (resampled to 128 Hz, 0. 5–45 Hz band-pass, 50 Hz notch, average reference), segmented into 8-s epochs with 4-s overlap, and quality-controlled (20 valid epochs per subject). Wavelet Scattering Transform features were then extracted under two configurations (J=6/7, Q=8), pooled to lobar regions with mild weights, and aggregated into subject-level statistics. Feature learning used an ₁ -penalized logistic selector followed by an ₂ -regularized logistic classifier, with performance estimated via 5-fold GroupKFold to produce subject-wise out-of-fold (OOF) logits. Base probabilities were calibrated using isotonic regression, combined through linear ensembling, and then recalibrated. We summarized discrimination (AUC, PR-AUC), calibration (Brier score, ECE), threshold behavior, and clinically oriented operating points (Sens@Spec, Spec@Sens) ; uncertainty was quantified using bootstrap confidence intervals derived from OOF predictions. Results After quality control, 59 subjects (31 AD/28 HC; 6, 957 epochs) remained. The calibrated ensemble achieved an AUC of 0. 930 and a PR-AUC of 0. 931; the Brier score improved from 0. 107 to 0. 102, and the ECE decreased from 0. 051 to 0. 000. Bootstrap resampling (N=1000) yielded mean AUC =0. 932 (95% CI 0. 864, \, 0. 980). Thresholds t=0. 380 (F1-optimal) and t=0. 5 produced identical F₁=0. 839 and accuracy =0. 831. Clinically, t=0. 375 prioritized sensitivity (0. 935), whereas t=0. 667 prioritized specificity (1. 00; no false positives). Conclusions The lightweight, interpretable EEG workflow produced reliable, calibrated probabilities under subject-wise, leakage-free evaluation and supported explicit clinical operating points. While external, multi-center validation remains necessary, these findings support probability-aware EEG decision support for AD.
Shamsi et al. (Mon,) studied this question.