Assessment of sleep quality in clinical practice remains constrained by tools that are either subjective or impractical for routine use. Self-report instruments, such as the Pittsburgh Sleep Quality Index (PSQI) and sleep diaries, are vulnerable to recall bias and demonstrate limited correspondence with objective neurophysiological measures. Polysomnography (PSG), while the gold standard, is costly, labor-intensive, and disruptive to natural sleep, rendering it unsuitable for longitudinal or outpatient monitoring. Recent advances in quantitative electroencephalography (qEEG), event-related potential (ERP) analysis, and source localization have created opportunities to bridge this gap. These methods enable scalable, objective evaluation of neurophysiological processes underpinning sleep architecture, cognitive function, and emotional regulation. This review synthesizes emerging evidence that EEG-derived biomarkers (e.g., spectral power ratios (delta/alpha, theta/ beta), cross-frequency coupling, and ERP dynamics) can serve as sensitive indices of sleep quality and neural health. We highlight the BrainView platform as an exemplar of this translational shift. BrainView is an FDA-cleared system combining qEEG, ERP, source localization, and normative comparison to quantify brain function non-invasively and longitudinally. By integrating neurophysiological data with actigraphy, self-report, and behavioral outcomes, EEG-based platforms such as BrainView may redefine how clinicians and researchers monitor sleep, treatment response, and neurocognitive resilience.