Interpretable machine-learning for depression classification based on a three-component EEG marker.

Electroencephalography (EEG)-based depression classification requires interpretable machine-learning approaches and validation strategies that avoid subject-level information leakage. This single-center pilot study developed and internally evaluated a marker-based interpretable machine-learning framework using eight-channel resting-state EEG. After quality control, 48 participants were included, comprising 23 clinician-diagnosed major depressive disorder (MDD) patients recruited at Zhejiang Provincial Tongde Hospital and 25 healthy controls (HCs). Subject-level spectral, entropy/complexity, asymmetry, and coherence-based connectivity features were extracted from pre-processed EEG epochs. Exploratory feature analysis was used to define a three-component EEG marker comprising fronto-posterior beta- and gamma-band coherence heterogeneity and F8-F7 beta-band asymmetry variability. The resulting marker was evaluated using classical classifiers under strict subject-wise leave-one-subject-out validation and compared with EEGNet and 1D-CNN baselines under the same subject-wise protocol. Among the evaluated classical models, RBF-SVM achieved the highest subject-level discrimination in the primary native-reference 2-s analysis, with an AUC of 0.910, accuracy of 85.42%, sensitivity of 82.61%, and specificity of 88.00%. The primary result used the native A1/A2 acquisition reference and non-overlapping 2-s epochs, consistent with segmentation used in previous resting-state EEG depression studies. SHAP analysis indicated that fronto-posterior gamma-band coherence heterogeneity and F8-F7 beta-band asymmetry variability were the dominant contributors to the RBF-SVM decision function. These findings support the exploratory value of compact EEG markers for interpretable internal discrimination in data-limited eight-channel settings and motivate validation in independent external cohorts.