Self-organized criticality enables conscious integration through brain-body resonance

TL;DR

This study shows that conscious integration depends on self-organized criticality maintained by brain-body resonance, with physiological signals playing a crucial role in neural synchronization and information encoding.

Contribution

It provides evidence that physiological signals support critical brain dynamics and challenges conventional data preprocessing that removes these signals.

Findings

Physiological signals are essential for neural synchronization.

Heavy-tailed avalanche dynamics indicate near-criticality in raw data.

Critical dynamics enable holographic information encoding.

Abstract

The "binding problem" of how distributed neural activity unifies into conscious experience has remained an open challenge since its articulation in 1890. We present evidence that conscious integration relies on self-organized criticality maintained by brain-body resonance, placing human cognition within the universality class of critical systems. Using 64-channel EEG data, we demonstrate that conventional preprocessing inadvertently eliminates the very integrative dynamics it seeks to measure. Removing physiological signals conventionally treated as "artifacts" drastically reduces the shared variance between global phase synchronization and stimulus-evoked amplitude, an effect highly specific to physiological components. We trace this to a fundamental brain-body resonance at 78 milliseconds that establishes zero-lag synchronization driven by robust bidirectional causality. Crucially, raw data exhibits heavy-tailed avalanche dynamics indicative of a near-critical regime, whereas conventionally cleaned data definitively rejects power-law distributions, signaling an artificial shift to subcriticality. Finally, we show these critical dynamics enable holographic information encoding, evidenced by a significant emergence of spatial interference patterns post-resonance. Together, these findings indicate that physiological signals actively and selectively support the coupling between large-scale neural coordination and event-related processing.