What question did this study set out to answer?

The aim is to evaluate metrics from neural oscillation patterns to improve the measurement of phenomenal consciousness and sleep states.

March 28, 2026Open Access

Spatial Organisation of Neural Oscillations as a Multi-Dimensional Marker of Phenomenal State: Metrics, Datasets, Limitations, and a Roadmap Toward a Universal Baseline

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This investigation explores metrics of neural oscillation patterns to differentiate sleep stages and enhance consciousness measurement.

The aim is to evaluate metrics from neural oscillation patterns to improve the measurement of phenomenal consciousness and sleep states.
Systematic empirical investigation of metrics derived from spatial pattern stability of neural oscillations.
Validation across five EEG datasets related to various states including sedation and sleep.
Evaluation of seven distinct metrics, including inter-band synchrony and spatial efficiency.
C_fast-slow metric effectively distinguishes all sleep stages, including N2 from N3, without needing a baseline.
Universal reference baseline from resting-state EEG dataset proved inadequate due to individual variability.
Introduction of purely relational metrics as a potential solution for cross-dataset applicability.

Alastair Waterman (2026) studied this question.

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