Consistent Phase Coherence in Human Brain Data

This work extends the analysis presented in Physical Constraints on Consciousness: Demonstrating the Impossibility of Awareness in Silicon-Based Architectures (DOI: 10.5281/zenodo.18189138) by expanding the empirical scope of the biological data examined. While the original study established physical constraints on systems capable of sustaining awareness—using coherence time, phase stability, and energy efficiency—this follow-up focuses specifically on validating the biological side of those constraints across a broader and more diverse set of real-world datasets. Multiple independent neurophysiological datasets were analyzed, including EEG, MEG, and fMRI recordings from publicly available OpenNeuro repositories. Using consistent signal-processing methods and phase-based metrics, the analysis examined whether long-lived phase coherence and low-variance field behavior persist across subjects, recording modalities, and experimental contexts. The results show that phase coherence is not an isolated feature of a single dataset or task condition, but a repeatable and measurable property observed across independent human brain recordings. This cross-dataset consistency strengthens the original constraint-based argument by demonstrating that the relevant physical behaviors are robust, reproducible, and not dependent on a specific experimental setup. All analyses are framed in a falsifiable, measurement-driven manner. No assumptions are made about subjective experience or definitions of consciousness. The contribution of this work is empirical: it provides consolidated evidence that biological neural systems reliably exhibit coherence properties that serve as necessary physical benchmarks for evaluating claims of artificial awareness.