EEG signals are projections — electrical activity at the scalp is a blurred, summed version of cortical dynamics happening millimeters below. The relationship between what you measure and what generates the measurement is governed by physics: volume conduction, electrode impedance, spatial filtering. Interpreting EEG has always meant working backward from the projection to infer the source.
Canessa (arXiv:2603.22297) proposes a different interpretive framework: treat the EEG signal as bounded by an effective event horizon. Not a metaphor borrowed casually from physics, but a formal analogy. The signal amplitude follows a renormalization-group scaling relation — the same mathematical structure that describes how physical quantities change across scales in statistical mechanics and quantum field theory.
In this framework, spectral entropy of the EEG determines the number of accessible observable modes. Higher entropy means more modes contribute to the signal; lower entropy means fewer modes dominate. The “horizon” is the boundary beyond which modes become inaccessible to measurement — not because they don't exist, but because the measurement apparatus can't resolve them.
The framework generates testable predictions: specific relationships between spectral entropy and amplitude scaling of EEG frequency bands. The oscillatory structures that emerge from the model are analyzable through standard signal processing and sonification techniques, connecting the abstract physics to practical neurophysiology.
The through-claim: EEG spectral entropy isn't just a measure of signal complexity — it determines the observable boundary of neural dynamics. Below a certain entropy, modes that exist in the cortex become invisible at the scalp. The measurement has a horizon, and the horizon is set by the entropy of what you're measuring.