OSCR

A three-component dynamical index of consciousness-related neural organisation.

Code ↔ Paper

25 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 25 matches
  1. [1] § Generative model and data generation › Simulation of EEG-like signals ↔ Ugail-Howard-Conciousness-Index_State_Simulator.ipynb, lines 321–410 · score 0.80 · 13–30 Hz, 8–13 Hz, 30–80 Hz, mixing, 1–4 Hz, 4–8 Hz
  2. [2] § Generative model and data generation › Simulation of EEG-like signals ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 356–445 · score 0.80 · 13–30 Hz, 8–13 Hz, 30–80 Hz, mixing, 1–4 Hz, 4–8 Hz
  3. [3] § Mathematical framework › Organised cross-frequency complexity ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1239–1304 · score 0.80 · shift surrogates, Tort MI, theta phase, gamma amplitude, Sleep EDF, bins
  4. [4] § Generative model and data generation › Simulated states ↔ Ugail-Howard-Conciousness-Index_State_Simulator.ipynb, lines 416–492 · score 0.78 · reduced alpha, Dreaming states, sleep states, Minimally conscious states, REM sleep, delta
  5. [5] § Generative model and data generation › Simulated states ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 451–527 · score 0.78 · reduced alpha, Dreaming states, sleep states, Minimally conscious states, REM sleep, delta
  6. [6] § Mathematical framework › Organised cross-frequency complexity ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 192–260 · score 0.75 · phase bins, uniform reference, amplitude guard, Tort MI, Phase amplitude, divergence
  7. [7] § Mathematical framework › Scale-free temporal organisation ( ) ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 263–361 · score 0.73 · DFA scaling exponent, range normalised triangular, scale free temporal, fallback, tuning, Kuramoto
  8. [8] § Results › Validation of using real EEG ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1340–1385 · score 0.70 · Friedman omnibus, pairwise Wilcoxon, rank biserial, Bonferroni correction, Validation
  9. [9] § Mathematical framework › Scale-free temporal organisation ( ) ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 124–189 · score 0.69 · DFA scaling exponent, cumulative sum, windows, stationary, detrended, persistent
  10. [10] § Generative model and data generation › Simulated states ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 402–461 · score 0.66 · cross channel phase, slow drift, theta gamma, coupling, noise, PAC
  11. [11] § Results › Monte carlo distributions across states ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 625–678 · score 0.65 · Kruskal Wallis omnibus, adjacent state, task engaged, minimally conscious, Cliff, Dreaming
  12. [12] § Mathematical framework › Organised cross-frequency complexity ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 192–260 · score 0.64 · binary sequence, Lempel Ziv complexity, amplitude envelope
  13. [13] § Results › Validation of using real EEG ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1786–1871 · score 0.64 · Empirical benchmarking heatmap, spectral slope, alpha power, Sleep EDF, LZC, row
  14. [14] § Generative model and data generation › Simulation of EEG-like signals ↔ Ugail-Howard-Conciousness-Index_State_Simulator.ipynb, lines 321–410 · score 0.64 · 0.5–4 Hz, 30–80 Hz, ictal, Hilbert, filtering, scored
  15. [15] § Results › Validation of using real EEG ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1340–1385 · score 0.63 · Friedman omnibus, Pairwise Wilcoxon, rank biserial, Bonferroni, Validation
  16. [16] § Generative model and data generation › Simulation of EEG-like signals ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 356–445 · score 0.63 · 0.5–4 Hz, 30–80 Hz, ictal, Hilbert, filtering, scored
  17. [17] § Results › Ablation study ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 263–361 · score 0.61 · DFA scaling exponents, organisation score, scale free temporal, phase amplitude, cross frequency, Kuramoto
  18. [18] § Results › Validation of using real EEG ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1786–1871 · score 0.60 · single metric, spectral slope, alpha power, LZC, benchmark, bootstrap
  19. [19] § Results › Validation of using real EEG ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1511–1635 · score 0.58 · Stratified epoch sampling, capped, night, Sleep EDF, real EEG, validation
  20. [20] § Mathematical framework › Scale-free temporal organisation ( ) ↔ Ugail-Howard-Consciousness-Index.ipynb, lines 124–189 · score 0.58 · detrended fluctuation, long range correlations, sum, temporal, DFA
  21. [21] § Results › Sensitivity analysis ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1035–1174 · score 0.57 · Hyperparameter sensitivity, adjacent state pairs, pair AUC, sweeps, worst
  22. [22] § Results › Component discriminability in synthetic data ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1035–1174 · score 0.57 · state discrimination, adjacent state pair, Task engaged, Minimally Conscious, hierarchy, AUC
  23. [23] § Results › Monte carlo convergence ↔ Ugail-Howard-Consciousness-Index_Validation_updated.ipynb, lines 1177–1236 · score 0.56 · Monte Carlo convergence, median deviation, maximum deviation
  24. [24] § Mathematical framework › Scale-free temporal organisation ( ) ↔ Ugail-Howard-Conciousness-Index_State_Simulator.ipynb, lines 117–199 · score 0.56 · DFA scaling exponent, cumulative sum, stationary, persistent, signal
  25. [25] § Generative model and data generation › Simulated states ↔ Ugail-Howard-Conciousness-Index_State_Simulator.ipynb, lines 416–492 · score 0.50 · Task engaged states, prominent, moderate, beta, coupling, simulate

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 1,872 lines · 1.1 MB · no license · 13 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: Ugail-Howard-Consciousness-Index_Validation_updated.ipynb.

Overview

Authors: Hassan Ugail1, Newton Howard2
ORCID iDs: Hassan Ugail
  1. Centre for Visual Computing and Intelligent Systems, University of Bradford,Bradford, UK
  2. School of Individualized Study, Rochester Institute of Technology,Rochester, USA
Institutions: University of Bradford (United Kingdom); Rochester Institute of Technology (United States)
Journal: Biological cybernetics, volume 120, issue 3-4, article 20
Dates: received 23 November 2025; accepted 22 June 2026; published online 13 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00422-026-01049-1 · PMID 42439951 · PMCID PMC13364888 · OpenAlex W7168188724
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Complexity, Preprocessing, Evoked potentials
MeSH: Brain*, Consciousness*, Models, Neurological*, Electroencephalography, Humans, Sleep, Wakefulness (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Quantifying consciousness from brain activity remains a major challenge in neuroscience and clinical practice. Many existing EEG measures focus on a single feature of neural activity, such as complexity, synchrony, or spectral structure, but no single feature appears sufficient across different brain states. We introduce a composite dynamical framework that combines three complementary properties of brain activity, i.e., scale-free temporal organisation, cross-frequency organisation, and metastable flexibility in large-scale synchronisation. These components are normalised and combined into a single index designed to capture organised dynamical complexity rather than raw signal complexity alone. We test the framework in both synthetic and empirical settings. In a generative model of nine EEG-like brain states, including wakefulness, dreaming, anaesthesia, non-conscious states, and seizure states, the index separates the synthetic conscious and non-conscious classes without overlap and remains stable across ablation, sensitivity, and Monte Carlo analyses. We then apply the framework to two-channel Sleep-EDF recordings from 30 healthy adults, where it provides a proof-of-principle subject-level separation of wakefulness from N2 and REM sleep. The framework is dynamical-systems-inspired and is not committed to any single theory of consciousness, making it compatible with a range of theoretical perspectives. With further validation, the framework may be applicable across multichannel brain recordings, including anaesthesia, disorders of consciousness, and basic consciousness-research settings.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 25 matches between paragraphs and lines of code.

ugail/Index-for-Consciousness-Dynamics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 89ad301bf5b457920b457ff4d58f96c41512299c, 13 July 2026
Languages: Jupyter (3)
Size: 4 files, 3 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), pandas (3 files), SciPy (3 files), NetworkX (2 files), MNE-Python (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 89ad301, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/ugail/Index-for-Consciousness-Dynamics

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 25 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

The code to compute the consciousness index Ψ Ψ, including the feature pipeline, the synthetic EEG generator, and validation scripts, is publicly available in a dedicated GitHub repository: https://github.com/ugail/Index-for-Consciousness-Dynamics. The real EEG recordings used for validation were obtained from the publicly available Sleep-EDF “Expanded” dataset, hosted on PhysioNet (https://physionet.org/content/sleep-edfx/). This dataset is distributed under the Open PhysioNet License. For full reproducibility and independent verification, the code for all experiments, including the ablation, sensitivity, Monte Carlo, and subject-level Sleep-EDF validation analyses, is available in the GitHub repository. The released code includes the full state-wise simulator configuration used for all synthetic analyses, including band weights, coupling constants, PAC strengths, noise parameters, slow-drift settings, and seizure-burst parameters.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: — → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 7 MeSH terms, 50 references.

Cite

This paper

Ugail, H., & Howard, N. (2026). A three-component dynamical index of consciousness-related neural organisation. Biological cybernetics, 120(3-4), 20. https://doi.org/10.1007/s00422-026-01049-1

BibTeX

@article{ugail2026three,
author = {Ugail, Hassan and Howard, Newton},
title = {{A three-component dynamical index of consciousness-related neural organisation}},
journal = {Biological cybernetics},
year = {2026},
month = jul,
volume = {120},
number = {3-4},
pages = {20},
publisher = {Springer Science+Business Media},
issn = {0340-1200},
doi = {10.1007/s00422-026-01049-1},
url = {https://doi.org/10.1007/s00422-026-01049-1},
pmid = {42439951},
pmcid = {PMC13364888}
}

RIS

TY - JOUR
AU - Ugail, Hassan
AU - Howard, Newton
TI - A three-component dynamical index of consciousness-related neural organisation
T2 - Biological cybernetics
J2 - Biol Cybern
PY - 2026
DA - 2026/07/13
VL - 120
IS - 3-4
SP - 20
SN - 0340-1200
PB - Springer Science+Business Media
DO - 10.1007/s00422-026-01049-1
UR - https://doi.org/10.1007/s00422-026-01049-1
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s00422-026-01049-1",
"type": "article-journal",
"title": "A three-component dynamical index of consciousness-related neural organisation",
"container-title": "Biological cybernetics",
"author": [
{
"family": "Ugail",
"given": "Hassan"
},
{
"family": "Howard",
"given": "Newton"
}
],
"container-title-short": "Biol Cybern",
"volume": "120",
"issue": "3-4",
"page": "20",
"DOI": "10.1007/s00422-026-01049-1",
"PMID": "42439951",
"PMCID": "PMC13364888",
"ISSN": "0340-1200",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s00422-026-01049-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
13
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.celrep.2026.117782 [code]
Thermodynamics of consciousness: Non-equilibrium brain dynamics track conscious states.
Journal: Cell reports
In common: pandas, SciPy, Matplotlib, 1 other tool, EEG, cognitive, 6 references
[2] doi:10.1038/s41597-026-07350-9 [code]
An open multi-center MEG-EEG dataset for studying conscious visual perception.
Journal: Scientific data
In common: MNE-Python, scikit-learn, pandas, 3 other tools, EEG, 4 references
[3] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: MNE-Python, NetworkX, scikit-learn, 4 other tools, cognitive, 3 references
[4] doi:10.3389/fncom.2026.1786996 [code]
Schumann-anchored golden ratio organization of human neural oscillations.
Journal: Frontiers in computational neuroscience
In common: MNE-Python, NetworkX, scikit-learn, 4 other tools, EEG, 2 references
[5] doi:10.1371/journal.pone.0348005 [code]
THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.
Journal: PloS one
In common: NetworkX, scikit-learn, pandas, 3 other tools, 3 references
[6] doi:10.7554/elife.100605 [code]
Age-related changes in ‘cortical’ 1/f dynamics are linked to cardiac activity
Journal: —
In common: MNE-Python, NetworkX, scikit-learn, 4 other tools, 2 references
[7] doi:10.3390/s26103065 [code]
Subject-Wise Depression Screening from Eight-Channel Resting-State EEG Using Asymmetry-Aware Spectral Features and Connectivity Ablation.
Journal: Sensors (Basel, Switzerland)
In common: scikit-learn, pandas, SciPy, 2 other tools, EEG, author Hassan Ugail
[8] doi:10.1371/journal.pone.0351872 [code]
Decoding visual object recognition from EEG signals.
Journal: PloS one
In common: MNE-Python, scikit-learn, pandas, 3 other tools, EEG, 2 references
[9] doi:10.1038/s41593-026-02285-1 [code]
Fixation duration on natural scenes is explained by memory encoding not processing demand.
Journal: Nature neuroscience
In common: MNE-Python, scikit-learn, pandas, 3 other tools, cognitive, 2 references
[10] doi:10.1038/s41597-026-07377-y [code]
An open-access multi-site fMRI dataset for investigating conscious visual perception.
Journal: Scientific data
In common: MNE-Python, scikit-learn, pandas, 3 other tools, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.