OSCR

ION-Sim: A Novel Open-Source Simulation Framework for Intraoperative Neurophysiological Monitoring.

Code ↔ Paper

29 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 29 matches
  1. [1] § Appendix A › Appendix A.5. CAP Module ↔ capIOM.py, lines 224–282 · score 0.79 · 10–1500 Hz, 5–3000 Hz, Spline Interpolation, Bandpass filters, Smooth, CAP
  2. [2] § 2. Materials and Methods › 2.2. Modules and Educational Scenario › 2.2.1. Physiological Modules ↔ Virtual_patient/baepIOM_EN.py, lines 1–36 · score 0.69 · Evoked Potential, acoustic, Condensation, Rarefaction, contralateral, bilateral
  3. [3] § 2. Materials and Methods › 2.2. Modules and Educational Scenario › 2.2.1. Physiological Modules ↔ baepIOM_EN.py, lines 1–35 · score 0.69 · Evoked Potential, acoustic, Condensation, Rarefaction, contralateral, bilateral
  4. [4] § Appendix A › Appendix A.8. ECoG Module ↔ utils_funcIOM.py, lines 60–109 · score 0.68 · Fast Fourier Transform, fast_normalized_cross_correlation, FFT
  5. [5] § Appendix A › Appendix A.6. CMAP Module ↔ cmapIOM.py, lines 199–263 · score 0.67 · tccm_spread_ms, Central Jitter, corticospinal, MUAP, clinical, temporal
  6. [6] § Appendix A › Appendix A.7. EEG Module ↔ Virtual_patient/eegAuxIOM4.py, lines 143–287 · score 0.66 · slow drifts, bandpass filter, 1.6 Hz, 35 Hz, ECG, 25 Hz
  7. [7] § Appendix A › Appendix A.7. EEG Module ↔ eegAuxIOM4.py, lines 143–287 · score 0.66 · slow drifts, bandpass filter, 1.6 Hz, 35 Hz, ECG, 25 Hz
  8. [8] § Appendix A › Appendix A.1. MEP Module ↔ Virtual_patient/client_student_monitorIOM.py, lines 69–176 · score 0.65 · cranial nerves, lower limbs, upper limbs, monitoring, training, MEP
  9. [9] § Appendix A › Appendix A.6. CMAP Module ↔ simulaMepIOM2.py, lines 138–218 · score 0.65 · Compound Muscle Action, muscle response, motor pathways, Summation, summing, MEP
  10. [10] § Appendix A › Appendix A.5. CAP Module ↔ N20-P25model.py, lines 35–74 · score 0.62 · Ricker Wavelet, Mexican Hat, Model
  11. [11] § Appendix A › Appendix A.2. BAEP Module ↔ Virtual_patient/baepIOM_EN.py, lines 784–872 · score 0.60 · 5–6.5 ms, scipy.signal.find_peaks, III, BAEP, windows, latencies
  12. [12] § 2. Materials and Methods › 2.2. Modules and Educational Scenario › 2.2.1. Physiological Modules ↔ anesthesia_IOM_EN.py, lines 97–139 · score 0.59 · SpO2, RPM, NIBP, HR, vital, height
  13. [13] § Appendix A › Appendix A.7. EEG Module ↔ Virtual_patient/eegAuxIOM4.py, lines 939–983 · score 0.58 · Power Spectral Density, theta, delta, bands, beta, alpha
  14. [14] § Appendix A › Appendix A.7. EEG Module ↔ eegAuxIOM4.py, lines 936–980 · score 0.58 · Power Spectral Density, theta, delta, bands, beta, alpha
  15. [15] § Appendix A › Appendix A.5. CAP Module ↔ capIOM.py, lines 308–431 · score 0.58 · CAPWorker, Compound Action Potential, IOM, graphical, thread, GUI
  16. [16] § Appendix A › Appendix A.1. MEP Module ↔ dwaveIOM2.py, lines 220–279 · score 0.57 · Train Pulses, Inter Stimulus, ISI, trace, Interval, Artifacts
  17. [17] § Appendix A › Appendix A.7. EEG Module ↔ Virtual_patient/eegAuxIOM4.py, lines 143–287 · score 0.54 · EOG Blinks, EOG template, artifacts, channels, EEG, simulator
  18. [18] § Appendix A › Appendix A.7. EEG Module ↔ eegAuxIOM4.py, lines 143–287 · score 0.54 · EOG Blinks, EOG template, artifacts, channels, EEG, simulator
  19. [19] § Appendix A › Appendix A.8. ECoG Module ↔ Arx_modelQtEn8.py, lines 1828–1977 · score 0.53 · Circular Buffer, run_simulation_step, Logic
  20. [20] § Appendix A › Appendix A.8. ECoG Module ↔ utilities_extra/Arx_modelQtEn8.py, lines 1828–1977 · score 0.53 · Circular Buffer, run_simulation_step, Logic
  21. [21] § Appendix A › Appendix A.1. MEP Module ↔ sepIOMmeg.py, lines 642–780 · score 0.53 · AI Tutor, AI Agent, feedback, logged, database, simulation
  22. [22] § Appendix A › Appendix A.1. MEP Module ↔ vp_tutorIOM2.py, lines 23–140 · score 0.52 · AI Tutor, AI Agent, logged, database, simulation, Module
  23. [23] § Appendix B ↔ Virtual_patient/eegAuxIOM4.py, lines 939–983 · score 0.52 · frequency bands, theta, delta, beta, power, alpha
  24. [24] § Appendix A › Appendix A.1. MEP Module ↔ Virtual_patient/mepIOM_EN2.py, lines 788–933 · score 0.51 · facilitation_map, amplitude scale, Pulses, Train, stimulator, MEP
  25. [25] § Appendix A › Appendix A.1. MEP Module ↔ mepIOM_EN2.py, lines 788–933 · score 0.51 · facilitation_map, amplitude scale, Pulses, Train, stimulator, MEP
  26. [26] § Appendix B ↔ eegAuxIOM4.py, lines 936–980 · score 0.50 · frequency bands, theta, delta, beta, power, alpha
  27. [27] § Appendix A › Appendix A.1. MEP Module ↔ simulaMepIOM2.py, lines 138–218 · score 0.50 · Compound Muscle Action, muscle response, waveforms, stimulation, MEP, simulation
  28. [28] § Appendix A › Appendix A.7. EEG Module ↔ Virtual_patient/eegAuxIOM4.py, lines 45–122 · score 0.50 · mne.io.read_raw_cnt, Neuroscan, EEG
  29. [29] § Appendix A › Appendix A.7. EEG Module ↔ eegAuxIOM4.py, lines 45–122 · score 0.50 · mne.io.read_raw_cnt, Neuroscan, EEG

Paper

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The authors' code

Python · 997 lines · 40 KB · no license · 5 matches

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It can be read at the source: Virtual_patient/eegAuxIOM4.py.

Overview

Authors: Rosmary Blanco1,2, Riccardo Budai3
ORCID iDs: Riccardo Budai
  1. Sano Centre for Computational Medicine, 30-054 Krakow, Poland
  2. Amsterdam UMC Location University of Amsterdam, 1012 WP Amsterdam, The Netherlands
  3. University Hospital S.M. della Misericordia, 33100 Udine, Italy
Institutions: Amsterdam UMC Location University of Amsterdam (Netherlands); Sano – Centre for Computational Personalised Medicine International Research Foundation; Ospedale Santa Maria della Misericordia di Udine (Italy)
Journal: Brain sciences, volume 16, issue 7, article 680
Dates: received 13 April 2026; accepted 24 June 2026; published online 28 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/brainsci16070680 · PMID 42512455 · PMCID PMC13406592 · OpenAlex W7166692677
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality)
Methods: Evoked potentials, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: medical education, simulation, intraoperative neurophysiological monitoring (IONM), computational neuroscience, somatosensory-evoked potentials (SSEP), motor-evoked potentials (MEP), visual-evoked potential (VEP), compound action potential (CAP), cortico-cortical-evoked potentials (CCEP)
Topic: Intraoperative Neuromonitoring and Anesthetic Effects (Surgery, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 40 references in the paper

Abstract

Highlights: What are the main findings?

This study presents ION-Sim, a novel, open-source framework designed to simulate intraoperative neurophysiological monitoring (IONM) signals and complex clinical scenarios for educational purposes.

The framework integrates multiple physiological modules (including EEG, EMG, MEP, and SEP) with an advanced Learning Manager capable of dynamically injecting targeted clinical anomalies.

ION-Sim is featured with a Tutor modality to interact with surgical scenarios by modifying their characteristics or creating new ones tailored to specific learning goals.

What are the implications of the main findings?

ION-Sim facilitates the supervised learning of intraoperative neurophysiology through a completely hardware-agnostic approach.

By releasing the software under an open-source license (GPLv3), the project democratizes access to high-quality neurophysiological training and facilitates the standardized assessment of skill acquisition across the global clinical community.

Abstract: The educational pathway for expertise in intraoperative neurophysiological monitoring (IONM) is complex and lengthy, requiring a solid foundation in neuroscience, neurophysiology, and neuroanatomy. It also demands direct familiarity with a broad range of neurosurgical scenarios, including supratentorial, infratentorial, and spinal procedures, gained through exposure to at least ten distinct surgical approaches. Intraoperative neurophysiology must be tailored to each patient’s preoperative assessments. It relies on a variety of methods to collect, analyze, and report neurophysiological signals that are relevant to the surgical procedure. Despite its importance, there remains a substantial shortage of training tools designed to support realistic practice and skill development. To address this gap, we developed a comprehensive framework (ION-Sim) that integrates all laboratory testing modalities and adapts them to the operating room environment. ION_sim supports the simulation and analysis of spontaneous EEG and EMG activity, a wide range of evoked potentials, and intraoperative stimulus–response testing protocols. The framework provides a unified environment for practicing, testing, and validating the core neurophysiological procedures employed during neurosurgical interventions. In addition, it incorporates a robust data-management architecture, maintaining a database with system setups, user profiles, educational performance metrics, and automatically generating reports. This structure enables the longitudinal tracking of objective skill acquisition and facilitates standardized assessments of trainee progress. ION_Sim is distributed both as a ready-to-use application, suitable for direct integration into teaching and training programs, and as a modular scientific library. Through its dedicated APIs, users can design customized configurations, create novel simulation scenarios, and extend the platform to support additional research or educational objectives. It is available upon request for educational purposes and is open-source and released under the GNU General Public License, ensuring transparency, reproducibility, and long-term accessibility for the scientific and clinical communities.

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 29 matches between paragraphs and lines of code.

riccardo-budai/ION_Simula

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c44abb6d4351cd4905c166aa61ae7a62df6e8e56, 21 April 2026
Languages: Python (100)
Size: 1,271 files, 100 scripts
Software Heritage: not archived
Found in: “3. Software Implementation and Availability”
Holds: tests, documentation
Not found: README, license file, CITATION.cff, environment file, continuous integration
Tools: NumPy (69 files), Matplotlib (25 files), MNE-Python (23 files), SciPy (23 files), pandas (8 files), h5py (3 files), PyTorch (3 files), seaborn (3 files), NEURON (2 files), Nilearn (2 files), statsmodels (2 files), NiBabel (1 file), PyWavelets (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
100 files, not copied: shown from their source

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At the source: github.com/riccardo-budai/ION_Simula

3. Software Implementation and Availability

The simulation framework is implemented in Python 3.12 and was chosen for its extensive ecosystem of scientific computing libraries and its robust cross-platform compatibility, released as an open-source scientific library hosted on GitHub.com. It exposes a robust set of Application Programming Interfaces (APIs) designed for seamless integration within Python or C++ development environments. The ION-Sim system home screen is shown in Figure A1.

Technology Stack: The architecture leverages a modular stack of specialized libraries.

Computational Backend: NumPy and SciPy constitute the core engine for signal processing, matrix operations, and the mathematical modeling of physiological responses.

Graphical User Interface (GUI): The frontend is developed using PySide6 (the official Python binding for the Qt framework), ensuring a responsive and native look-and-feel across different operating systems.

Real-Time Visualization: High-performance signal rendering—essential for simulating continuous EEG/EMG streams and evoked potentials—is managed by the “pyqtgraph” library, which is optimized for fast data plotting.

User Interface Design: The UI is engineered to streamline user interactions by abstracting the underlying database complexities. By automating session management and pre-loading scenario configurations, the system minimizes the setup burden, allowing the user to focus on the educational task.

Licensing and Availability: This is to foster collaboration and accessibility within the scientific community; the source code is released under the GNU General Public License v3 (GPLv3). The complete repository, including documentation and installation instructions, is publicly hosted on GitHub [https://github.com/riccardo-budai/ION_Simula Accessed on 22 May 2026].

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

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;
  • 100 scripts, each with its path and the digest of its content;
  • 29 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

No dataset and no data link were found in the paper.

Data Availability Statement

No new data were created or analyzed in this study. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 9 keywords, 6 references.

Cite

This paper

Blanco, R., & Budai, R. (2026). ION-Sim: A Novel Open-Source Simulation Framework for Intraoperative Neurophysiological Monitoring. Brain sciences, 16(7), 680. https://doi.org/10.3390/brainsci16070680

BibTeX

@article{blanco2026ion,
author = {Blanco, Rosmary and Budai, Riccardo},
title = {{ION-Sim: A Novel Open-Source Simulation Framework for Intraoperative Neurophysiological Monitoring}},
journal = {Brain sciences},
year = {2026},
month = jun,
volume = {16},
number = {7},
pages = {680},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2076-3425},
doi = {10.3390/brainsci16070680},
url = {https://doi.org/10.3390/brainsci16070680},
pmid = {42512455},
pmcid = {PMC13406592}
}

RIS

TY - JOUR
AU - Blanco, Rosmary
AU - Budai, Riccardo
TI - ION-Sim: A Novel Open-Source Simulation Framework for Intraoperative Neurophysiological Monitoring
T2 - Brain sciences
J2 - Brain Sci
PY - 2026
DA - 2026/06/28
VL - 16
IS - 7
SP - 680
SN - 2076-3425
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/brainsci16070680
UR - https://doi.org/10.3390/brainsci16070680
LA - en
ER -

CSL-JSON

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