Black Hole-Inspired Horizon Model for Neural Signal Dynamics

TL;DR

This paper introduces a novel horizon-inspired model for EEG signals, linking entropy measures and wave dynamics to better understand neural oscillations and their underlying physical principles.

Contribution

It presents a new theoretical framework that models EEG signals as wave-like phenomena constrained by an effective horizon, connecting entropy and spectral signatures.

Findings

EEG signals can be modeled using horizon-inspired wave equations.

Spectral entropy correlates with amplitude scaling of neural oscillations.

The model generates testable predictions about neural signal dynamics.

Abstract

Electroencephalographic (EEG) signals provide macroscopic observables of complex neural dynamics. We introduce a horizon-inspired framework in which measured EEG signals are modeled as projections of a complex wave-like representation constrained by an effective boundary analogous to an event horizon. In this formulation the signal amplitude obeys a renormalization-group scaling relation while EEG spectral entropy parameterizes the accessibility of observable modes. The resulting solutions generate oscillatory structures whose geometry and spectral signatures can be explored through signal analysis and sonification. This mapping between entropy-based neural observables and wave-like signal representations provides a physically motivated framework linking entropy measures, scale-dependent dynamics, and observable neural oscillations, and suggests testable connections between spectral entropy and the amplitude scaling of EEG modes.