QT EEG Analysis Tool and Empirical Study on Human EEG Data

This preprint presents QT EEG, a deterministic, mathematics-based EEG analysis tool designed to quantify large-scale organization in multichannel human EEG recordings. The instrument operates through a fixed computational pipeline and does not rely on artificial intelligence, machine learning, training, or statistical inference. Applied to a publicly available human EEG dataset (OpenNeuro DS005620), KQ demonstrates a robust empirical ability to differentiate experimentally defined states of consciousness, including wakefulness, pharmacological sedation, and recovery. These distinctions emerge without supervision, labels, thresholds, or state-specific tuning, and are expressed through stable shifts in the statistical structure and temporal dynamics of KQ values. The preprint focuses on the instrument itself: its operational behavior, window-level dynamics, subject-level variability, and phase-resolved empirical patterns. Rather than proposing a theoretical model or clinical biomarker, the work documents KQ as a reproducible signal-analysis tool whose outputs consistently reflect global changes in EEG organization associated with altered conscious states. All results are generated directly from the implemented pipeline and are accompanied by numerical artifacts to support auditability and reproducibility.