OSCR

NeuroStat: An Open-Source EEG Connectivity Platform for Randomised Controlled Trials.

Code ↔ Paper

32 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 32 matches
  1. [1] § 2. Materials and Methods › 2.7. Validation Design › 2.7.2. Source-Space Simulation Benchmark ↔ scripts/validation/source_space_simulation.py, lines 1–73 · score 0.95 · Desikan Killiany parcels, Alpha activity, source activity, source signal, phase lag, MNE inverse
  2. [2] § 2. Materials and Methods › 2.7. Validation Design › 2.7.3. Source-Space Physiological Benchmark: PhysioNet EEGBCI Dataset and Procedure ↔ scripts/validation/physionet_validation.py, lines 1–50 · score 0.89 · EEG Motor Movement, Imagery Database, S001 S020, Eyes open, eyes closed, PhysioNet
  3. [3] § 2. Materials and Methods › 2.3. Source Localisation ↔ scripts/validation/source_space_simulation.py, lines 1–73 · score 0.87 · Desikan Killiany parcel, Source activity, inverse solution, MNE inverse, scalp EEG, Source space
  4. [4] § 2. Materials and Methods › 2.2. Automated Preprocessing Pipeline ↔ pli_pipeline.py, lines 94–116 · score 0.85 · Bad channel detection, ICA fitting, score thresholding, FastICA, peak, rejected
  5. [5] § 2. Materials and Methods › 2.7. Validation Design › 2.7.3. Source-Space Physiological Benchmark: PhysioNet EEGBCI Dataset and Procedure ↔ scripts/validation/physionet_source_space_iclabel.py, lines 1–70 · score 0.83 · Desikan Killiany, posterior alpha, Eyes open, eyes closed, PhysioNet, cuneus
  6. [6] § 2. Materials and Methods › 2.7. Validation Design › 2.7.1. Internal Validation: Simulated EEG ↔ scripts/validation/generate_simulated_eeg.py, lines 383–464 · score 0.82 · 20–100 Hz, temporal channels, bandpass filtered, EMG, bursts, min
  7. [7] § 2. Materials and Methods › 2.7. Validation Design › 2.7.2. Source-Space Simulation Benchmark ↔ scripts/validation/source_space_simulation.py, lines 335–423 · score 0.81 · distance matched, control edges, control PLI, random seeds, source space, alpha PLI
  8. [8] § 3. Results › 3.4. Source-Space Physiological Benchmark: PhysioNet EEGBCI ↔ pli_pipeline.py, lines 840–889 · score 0.79 · heart beat, channel noise, eye blink, ICLabel, classified, muscle
  9. [9] § 2. Materials and Methods › 2.4. Functional Connectivity Estimation ↔ app_gui.py, lines 1019–1100 · score 0.76 · 13–30 Hz, 8–13 Hz, 1–4 Hz, 4–8 Hz, frequency bands, delta
  10. [10] § Appendix A. NeuroStat Graphical User Interface Screenshots ↔ app_gui.py, lines 1019–1100 · score 0.75 · signal quality score, band power changes, variance reduction, SNR improvement, tab, Metrics
  11. [11] § Appendix A. NeuroStat Graphical User Interface Screenshots ↔ pli_pipeline.py, lines 967–1076 · score 0.74 · signal quality score, band power changes, variance reduction, SNR improvement, Metrics, component
  12. [12] § 2. Materials and Methods › 2.4. Functional Connectivity Estimation ↔ pli_pipeline.py, lines 128–141 · score 0.72 · 13–30 Hz, 8–13 Hz, 4–8 Hz, frequency bands, gamma, delta
  13. [13] § 2. Materials and Methods › 2.7. Validation Design › 2.7.3. Source-Space Physiological Benchmark: PhysioNet EEGBCI Dataset and Procedure ↔ scripts/validation/physionet_source_space_iclabel.py, lines 1–70 · score 0.72 · PhysioNet source space, Desikan Killiany, posterior alpha, cuneus, inferior, lingual
  14. [14] § 2. Materials and Methods › 2.6. Visual Outputs ↔ app_gui.py, lines 929–976 · score 0.70 · power spectral density, EEG traces, scalp topography, bar, metric, preprocessing
  15. [15] § 3. Results › 3.4. Source-Space Physiological Benchmark: PhysioNet EEGBCI ↔ scripts/validation/physionet_source_space_iclabel.py, lines 197–316 · score 0.68 · frontal transient, EC ICA, eyes open, eyes closed, source space, alpha PLI
  16. [16] § 3. Results › 3.2. Recovery of Known Connectivity ↔ scripts/validation/generate_simulated_eeg.py, lines 68–102 · score 0.67 · weak theta, moderate beta, strong alpha, expected PLI, connectivity pattern, coupling
  17. [17] § 2. Materials and Methods › 2.3. Source Localisation ↔ pli_pipeline.py, lines 1444–1499 · score 0.67 · inverse operator, depth, covariance, fsaverage, model, BEM
  18. [18] § 2. Materials and Methods › 2.7. Validation Design › 2.7.3. Source-Space Physiological Benchmark: PhysioNet EEGBCI Dataset and Procedure › Pipeline Modifications for Short Recordings ↔ scripts/validation/physionet_validation.py, lines 90–115 · score 0.66 · minute recordings, ICLabel brain, relaxed, disabled, ASR, threshold
  19. [19] § 3. Results › 3.3. Source-Space Simulation Recovery ↔ scripts/validation/source_space_simulation.py, lines 335–423 · score 0.65 · source space simulation, control edges, Desikan Killiany parcels, alpha PLI, inverse, anterior
  20. [20] § 3. Results › 3.1. Quality Verification of Simulated Data ↔ scripts/validation/quick_diagnostic.py, lines 155–260 · score 0.65 · sub matrix, frontal parietal, PLI matrix, Diagnostic, alpha band, expected PLI
  21. [21] § 3. Results › 3.3. Source-Space Simulation Recovery ↔ scripts/validation/source_space_simulation.py, lines 307–332 · score 0.61 · anterior control edges, known parcel pair, Source space simulation, Bars, reconstructed, Alpha
  22. [22] § 3. Results › 3.1. Quality Verification of Simulated Data ↔ scripts/validation/generate_simulated_eeg.py, lines 68–102 · score 0.59 · frontal parietal, strong alpha coupling, expected PLI, simulated, band, connectivity
  23. [23] § 3. Results › 3.2. Recovery of Known Connectivity ↔ scripts/validation/analyze_validation_results.py, lines 36–72 · score 0.59 · weak theta, moderate beta, strong alpha, heavy, Traditional, GEDAI
  24. [24] § 2. Materials and Methods › 2.6. Visual Outputs ↔ pli_pipeline.py, lines 1342–1377 · score 0.59 · noise reduction, RMS amplitude, Scalp topography, map, pipeline, EEG
  25. [25] § 2. Materials and Methods › 2.2. Automated Preprocessing Pipeline ↔ pli_pipeline.py, lines 94–116 · score 0.59 · bad channel detection, component rejection, FastICA, notch, brain, ASR
  26. [26] § 3. Results › 3.5. GEDAI Versus Traditional Preprocessing on Real EEG ↔ scripts/validation/physionet_method_comparison.py, lines 226–355 · score 0.58 · EO PLI, real EEG, eyes open, eyes closed, PhysioNet, alpha PLI
  27. [27] § 3. Results › 3.4. Source-Space Physiological Benchmark: PhysioNet EEGBCI ↔ scripts/validation/physionet_source_space_iclabel.py, lines 197–316 · score 0.57 · reversed subjects, eyes open, eyes closed, source space, Shapiro, alpha PLI
  28. [28] § 2. Materials and Methods › 2.7. Validation Design › 2.7.3. Source-Space Physiological Benchmark: PhysioNet EEGBCI Dataset and Procedure ↔ scripts/validation/analyze_validation_results.py, lines 75–118 · score 0.56 · Desikan Killiany, inferior, lingual, pericalcarine, precuneus, superior
  29. [29] § 2. Materials and Methods › 2.2. Automated Preprocessing Pipeline ↔ app_gui.py, lines 624–673 · score 0.55 · notch filtering, generalised eigenvalue decomposition, selection, brain, signal, GEDAI
  30. [30] § 2. Materials and Methods › 2.2. Automated Preprocessing Pipeline ↔ scripts/validation/physionet_validation.py, lines 90–115 · score 0.55 · bad channel, ICLabel, smaller, FastICA, bandpass, notch
  31. [31] § 2. Materials and Methods › 2.7. Validation Design › 2.7.3. Source-Space Physiological Benchmark: PhysioNet EEGBCI Dataset and Procedure › Pipeline Modifications for Short Recordings ↔ pli_pipeline.py, lines 840–889 · score 0.53 · ICLabel brain probability, rejection, threshold, pipeline, components, ICA
  32. [32] § 3. Results › 3.2. Recovery of Known Connectivity ↔ scripts/validation/analyze_validation_results.py, lines 36–72 · score 0.52 · weak theta, strong alpha, heavy, moderate, Traditional

Paper

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The authors' code

Python · 2,908 lines · 118 KB · no license · 8 matches

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It can be read at the source: pli_pipeline.py.

Overview

Authors: Usman Ghani1, Iftikhar Ahmad2, Shahbaz Pervez3, Seyed Ebrahim Hosseini3, Imran Khan Niazi1,4,5
ORCID iDs: Usman Ghani, Iftikhar Ahmad, Shahbaz Pervez, Seyed Ebrahim Hosseini, Imran Khan Niazi
  1. Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand; (U.G.); (I.K.N.)
  2. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  3. School of Applied IT, Whitecliffe, Auckland 1010, New Zealand; (S.P.); (S.E.H.)
  4. Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland 1010, New Zealand
  5. Centre for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
Institutions: New Zealand College of Chiropractic (New Zealand); King Abdulaziz University (Saudi Arabia); Auckland University of Technology (New Zealand); Aalborg University (Denmark)
Journal: Sensors (Basel, Switzerland), volume 26, issue 13, article 4019
Dates: received 27 May 2026; accepted 22 June 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/s26134019 · PMID 42451263 · PMCID PMC13364487 · OpenAlex W7165803648
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: electroencephalography, functional connectivity, phase lag index, randomised controlled trials, artefact removal, source localisation, open-source software, biomedical signal processing, clinical neurophysiology, Python
MeSH: Electroencephalography*, Randomized Controlled Trials as Topic*, Artifacts, Brain, Humans, Signal Processing, Computer-Assisted, Software (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: King Abdulaziz University (23425654756854)
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of 0.168 ± 0.017 PLI units and root mean squared error of 0.219 ± 0.045 (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; t(59) = 22.61, p < 0.001). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s d = 0.85, p = 0.001; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made.

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 32 matches between paragraphs and lines of code.

ghani097/NeuroStat-for-RCTs

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 23a19289f3e835ac7a00cb136e23108996007de8, 3 August 2026
Languages: Python (20), Shell (1)
Size: 87 files, 21 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, environment (requirements.txt), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: NumPy (12 files), MNE-Python (10 files), pandas (10 files), Matplotlib (9 files), SciPy (8 files), MNE-Connectivity (3 files), CuPy (2 files), ICLabel (1 file), MEEGkit (1 file), NetworkX (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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22 files, not copied: shown from their source

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At the source: github.com/ghani097/NeuroStat-for-RCTs

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

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  • 21 scripts, each with its path and the digest of its content;
  • 32 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data Availability Statement

The NeuroStat application source code, validation scripts, and sample outputs are available at https://github.com/ghani097/NeuroStat-for-RCTs (accessed on 21 June 2026). The PhysioNet EEGBCI dataset used for external validation is publicly available at https://physionet.org/content/eegmmidb/ (accessed on 21 June 2026). Further inquiries can be directed to the corresponding author.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 10 keywords, 7 MeSH terms, 1 funder, 38 references.

Cite

This paper

Ghani, U., Ahmad, I., Pervez, S., Hosseini, S. E., & Niazi, I. K. (2026). NeuroStat: An Open-Source EEG Connectivity Platform for Randomised Controlled Trials. Sensors (Basel, Switzerland), 26(13), 4019. https://doi.org/10.3390/s26134019

BibTeX

@article{ghani2026neurostat,
author = {Ghani, Usman and Ahmad, Iftikhar and Pervez, Shahbaz and Hosseini, Seyed Ebrahim and Niazi, Imran Khan},
title = {{NeuroStat: An Open-Source EEG Connectivity Platform for Randomised Controlled Trials}},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
month = jun,
volume = {26},
number = {13},
pages = {4019},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1424-8220},
doi = {10.3390/s26134019},
url = {https://doi.org/10.3390/s26134019},
pmid = {42451263},
pmcid = {PMC13364487}
}

RIS

TY - JOUR
AU - Ghani, Usman
AU - Ahmad, Iftikhar
AU - Pervez, Shahbaz
AU - Hosseini, Seyed Ebrahim
AU - Niazi, Imran Khan
TI - NeuroStat: An Open-Source EEG Connectivity Platform for Randomised Controlled Trials
T2 - Sensors (Basel, Switzerland)
J2 - Sensors (Basel)
PY - 2026
DA - 2026/06/24
VL - 26
IS - 13
SP - 4019
SN - 1424-8220
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/s26134019
UR - https://doi.org/10.3390/s26134019
LA - en
ER -

CSL-JSON

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

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