Psychiatric and neurodevelopmental disorders are commonly studied using localized EEG biomarkers such as band-power differences or region-specific abnormalities. While such approaches have produced valuable insights, they often show limited consistency across datasets and diagnostic groups, suggesting that disorder-relevant signal may reside in the large-scale organization and regulation of neural activity rather than in isolated features. This work introduces a multi-axis spectral-dynamical architecture framework for analyzing resting-state EEG. The framework organizes EEG-derived features into three conceptual layers: Layer 1: Window-level spectral-dynamical descriptors capturing entropy-like structure, spectral peak organization, and temporal variation Layer 2: Cortical architecture features summarizing the spatial distribution of these measures across anatomically interpretable axes (e.g., frontal–posterior, left–right) Layer 3: Higher-order regulation features capturing temporal instability, condition-response (eyes-open vs eyes-closed), cross-feature coupling, composite indices, and multivariate deviation from healthy reference structure Across multiple public EEG datasets spanning healthy and clinical populations, we observe that resting EEG is dominated by a frontal–posterior architectural organization, but diagnostic differences are expressed more strongly in variance structure, temporal instability, coupling relationships, and condition-response behavior than in mean feature shifts alone. A multifeature panel-based approach yields a Normative Deviation Index (NDI) that quantifies deviation from healthy resting EEG organization and demonstrates strong discrimination between healthy and clinical populations (ROC-AUC > 0.82 in primary contrasts). In parallel, an architecture-oriented panel preserves interpretability of cortical organization and feature-family contributions. These results support a shift from isolated EEG biomarkers toward panel-based, multi-axis characterization of resting brain organization, in which psychiatric and neurodevelopmental conditions are understood as alterations in the structure, regulation, and stability of cortical dynamics. Notes This Zenodo release provides a conceptual and empirical overview of the framework along with figures and summary results. Detailed implementation logic, optimization procedures, and full feature construction pipelines are not included in this version. Supporting materials may be shared for academic discussion upon request.