Lang Shen, Wei Tong, Ye Zhao, Bicheng Wu, Peng Zhang, Jing Kan
Extensive experiments were conducted on three public EEG datasets: the Alzheimer's Disease and Frontotemporal Dementia (ADFTD) dataset, the Alzheimer's Patients' Relatives Association of Valladolid (APAVA) dataset, and the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. These datasets cover classification tasks related to Alzheimer's disease, frontotemporal dementia, and Parkinson's disease. ScaleSpecter achieved competitive and generally favorable performance on most key evaluation metrics. The ablation and visualization results further demonstrated the complementary contributions of multiscale temporal modeling, cross-scale interaction, and spectral modulation.
Alzheimer's disease (AD) is associated with progressive cognitive decline and altered brain functional activity, yet objective and interpretable electrophysiological indicators remain insufficiently established. Resting-state electroencephalography (EEG) offers a low-cost and clinically accessible candidate, provided that the analysis is validated at the subject level and remains interpretable. This study evaluated an interpretable resting-state EEG framework for distinguishing AD patients from healthy control (HC) subjects. A total of 63 participants (36 AD and 27 HC) from a publicly available dataset were included. Subject-level spectral and nonlinear complexity features were extracted from 19 preprocessed scalp channels; missing-value imputation, feature screening, redundancy pruning, scaling, and model fitting were carried out within each fold of leave-one-subject-out cross-validation. Four linear classifiers were compared, and Ridge Logistic regression was retained for out-of-fold SHAP interpretation because of its balanced hard-label performance and direct compatibility with Linear SHAP. Ridge Logistic regression achieved an exploratory AUC of 0.912 (accuracy = 0.857, sensitivity = 0.778, specificity = 0.963) under a non-nested validation design. Across 30 independently balanced epoch resamples, mean AUC was 0.887 ± 0.020; using all accepted epochs yielded AUC = 0.917. SHAP analysis indicated that the classifier drew jointly on posterior α activity, frontal and temporal θ power, the θ/α ratio, and slow/fast ratio features. A classifier-independent microstate analysis revealed reduced putative Class B occurrence, increased putative Class C and Class D duration, and six FDR-corrected off-diagonal transition differences in AD; the three temporal effects persisted across repeated K = 4 initializations and matched K = 3-6 solutions. These findings suggest that resting-state EEG can provide non-invasive and interpretable information about AD-related functional alterations, pending validation in larger, independent cohorts.