This notebook implements a mathematical framework for measuring consciousness from brain signals (EEG). It combines three fundamental aspects of neural dynamics into a single metric (Ψ) that quantifies the "quality" of consciousness.
Ψ captures consciousness through three key dimensions:
- Hierarchical Temporal Integration (H_eff) - How brain rhythms are organised across timescales
- Organized Complexity (D) - Phase-amplitude coupling and information richness
- Metastability (M) - The brain's dynamic flexibility while maintaining coherence
Higher Ψ scores indicate richer, more organised consciousness dynamics (waking consciousness)
Lower Ψ scores suggest reduced consciousness (deep sleep, anaesthesia, disorders)
- Ugail-Howard-Consciousness-Index.ipynb - (The mathematical framework)
- Ugail-Howard-Conciousness-Index_State_Simulator.ipynb - (physiologically motivated simulator for nine canonical conscious states)
- Ugail-Howard-Conciousness-Index_Validation.ipynb - (full Monte Carlo, ablation, sensitivity, and Sleep-EDF empirical validation pipeline).
- Researchers studying consciousness, anaesthesia, sleep, or neurological disorders
- Neuroscientists analysing EEG or electrophysiological recordings
- Students learning computational approaches to consciousness measurement
- Data scientists interested in advanced brain signal analysis
Cite: H. Ugail and N. Howard, “Quantifying the dynamics of consciousness using hierarchical integration, organised complexity and metastability,” arXiv preprint arXiv:2512.10972, Dec. 2025. More details can be found in the paper: https://arxiv.org/abs/2512.10972
The code in this repository is released under the MIT License.