Abstract A fundamental question for consciousness science is what changes in the brain when phenomenal experience arises or restructures. Current EEG metrics of consciousness predominantly measure temporal signal complexity, but face a systematic confound: broadband amplitude increases during propofol sedation and slow-wave sleep paradoxically inflate complexity measures, misranking states that are phenomenologically distinct. We introduce η (spatial efficiency), defined as the ratio of organised oscillatory power to total oscillatory power across five frequency bands: η = ||Ψ|| / ||A||, where Ψ is the Effective Power Vector (amplitude weighted by spatial pattern stability) and A is the total amplitude vector. Because η normalises by amplitude, it is immune to the confound that defeats temporal complexity measures. We validate η across four independent EEG datasets. In two propofol sedation datasets (N=21 and N=20), η achieves AUC=0.988–1.000 for awake versus sedated classification, dramatically outperforming Lempel-Ziv Complexity (AUC=0.612–0.755), which increases paradoxically under light sedation. In a polysomnographic sleep dataset (N=7), η follows the gradient Wake > REM > N2 > N3 with r=−1.000 in every subject across NREM stages, and correctly places REM above N3 (d=+1.78, p=0.016) — a dissociation that operationalises the presence of phenomenal content during dreaming. In a reversal learning dataset (N=22), η falls transiently when conscious subjects undergo cognitive restructuring (d=−2.58, p<0.001), demonstrating sensitivity to intra-conscious phenomenal transitions that consciousness-level metrics cannot detect. We interpret these results within multiple theoretical frameworks and argue that spatial efficiency constitutes a new class of EEG consciousness metric — one that tracks not merely whether a subject is conscious, but how organised their phenomenal state is. Keywords: consciousness; EEG; spatial organisation; sleep; propofol sedation; thermodynamics; Lempel-Ziv complexity; integrated information theory; global workspace theory
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Alastair Waterman (Thu,) studied this question.
www.synapsesocial.com/papers/69c7722a8bbfbc51511e26ea — DOI: https://doi.org/10.5281/zenodo.19233201
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Alastair Waterman
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