Propofol Sedation EEG: Topological Fingerprint Disruption by Loss of Consciousness - Negative Space Encoding (NSE) Empirical Validation Protocol

Negative Space Encoding (NSE) is a theoretical framework proposing that information in complex systems is encoded not solely in patterns of activity, but in structured absences — topological constraints on the system's phase space. In neural systems, NSE predicts that conscious states are characterized by subject-specific forbidden regions in phase space: areas that the brain's trajectory systematically avoids. These forbidden regions are theorized to be constitutive of individual identity rather than incidental to it. The formal framework and empirical predictions of NSE are developed in two publications: Negative Space Encoding: A Unifying Framework (DOI: 10.5281/zenodo.18664837) and Oscillatory Hierarchy as Progressive Negative Space Encoding (DOI: 10.5281/zenodo.18701947). The present analysis constitutes the second empirical validation study, complementing prior results on the ds004148 resting-state EEG dataset. The central prediction tested in this study is the Consciousness Modulation prediction (NSE-C): the topological fingerprint of a subject's neural trajectory — measurable via persistent homology — should be stable across states within conscious wakefulness, but should be significantly disrupted when consciousness is pharmacologically suppressed. If confirmed, this provides evidence that the forbidden-region structure of phase space is not merely a stable neural signature but is constitutively linked to conscious state. The NSE-C prediction was confirmed for all three frequency bands (Table 2). The Wasserstein distance between awake recordings of the same subject was significantly smaller than the distance between awake and sedated recordings of the same subject, in every frequency band tested. The NSE-C result is the primary finding of this study. The topological fingerprint of each subject's neural phase-space trajectory — quantified as Wasserstein distance between H1 persistence diagrams — is significantly more similar within wakefulness (EC vs EO) than across the wakefulness/sedation boundary (awake vs sed). This holds across all three frequency bands with very large effect sizes.