Quantifying the Dynamics of Consciousness using Hierarchical Integration, Organised Complexity and Metastability

A theory-neutral framework quantifies consciousness-related neural dynamics using three properties: hierarchical integration, cross-frequency complexity, and metastability. Implemented in a generative model of synthetic EEG, the composite index reliably separates high-consciousness states (psychedelic, wakeful) from impaired or non-conscious states (dreaming, non-REM sleep, minimally conscious, anaesthetised, seizure-like) across single trials and Monte Carlo ensembles. Validation with real EEG from the Sleep-EDF dataset alongside matched synthetic EEG shows that synthetic data recapitulate the empirical ordering and magnitude of the index across Wake, N2, and REM sleep, indicating the index captures stable and biologically meaningful distinctions. The approach offers a principled tool for quantifying consciousness-related neural organisation applicable to biological and artificial systems.

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Abstract

Quantifying the neural signatures of consciousness remains a major challenge in neuroscience and AI. Although many theories link consciousness to rich, multiscale, and flexible neural organisation, robust quantitative measures are still lacking. This paper presents a theory-neutral framework that characterises consciousness-related dynamics through three properties: hierarchical integration (H), cross-frequency complexity (D), and metastability (M). Candidate subsystems are identified using predictive information, temporal complexity, and state-space exploration to distinguish structured from unstructured activity. We provide mathematical definitions for all components and implement the framework in a generative model of synthetic EEG, simulating nine brain states ranging from psychedelic and wakeful to dreaming, non-REM sleep, minimally conscious, anaesthetised, and seizure-like regimes. Across single trials and Monte Carlo ensembles, the composite index reliably separates high-consciousness from impaired or non-conscious states. We further validate the framework using real EEG from the Sleep-EDF dataset alongside matched synthetic EEG designed to reproduce state-dependent oscillatory structure. Across Wake, N2, and REM sleep, synthetic data recapitulate the empirical ordering and magnitude of the index, indicating that the index captures stable and biologically meaningful distinctions. This approach provides a principled and empirically grounded tool for quantifying consciousness-related neural organisation with potential applications to both biological and artificial systems.