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NeuroTest: a benchtop testbed for evaluating sensing-capable electrophysiology and neurostimulation systems.

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Paper

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The authors' code

C/C++ header · 28 lines · 543 B · other

  1. #ifndef IIR_filter_h
  2. #define IIR_filter_h
  4. #include "Arduino.h"
  5. #include "constants.h"
  7. class IIR_filter
  8. {
  9. public:
  10. IIR_filter();
  12. /**
  13. * @brief Applies a second order section iir filter to an input data sample
  14. *
  15. * TODO: write long
  16. *
  17. * @param input_data: Current input sample to filter
  18. * @param coef: Coefficients of a BP filter
  19. *
  20. * @return The first value in the y history buffer
  21. */
  22. double IIR_SOS(float input_data, BP_coeffs coef);
  24. private:
  25. double _x_history[3];
  26. double _y_history[3];
  27. };
  28. #endif

IIRFilter.h at commit 227ca3a, under other · at the source

Overview

Authors: Morrow JK1, H Cho2, M Benjaber3, Denison TJ3, Widge AS1, Herron JA2
ORCID iDs: Morrow JK, H Cho, M Benjaber, Denison TJ, Widge AS, Herron JA
  1. Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, Unites States of America
  2. Department of Neurological Surgery, University of Washington, Seattle, WA, Unites States of America
  3. Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
Institutions: University of Minnesota (United States); University of Washington (United States); University of Oxford (United Kingdom)
Journal: Journal of neural engineering, volume 23, issue 6, article 066003
Dates: received 16 December 2025; accepted 22 October 2026; published online 4 November 2026; in print 1 December 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1088/1741-2552/aeb82d · PMID 42913699 · PMCID PMC13733271 · OpenAlex W7223198250
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.19.677167
Status: code verified (how the code is found and verified)
Categories: human (organism), methods / tools (subfield)
Methods: Preprocessing, Connectivity, Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: deep brain stimulation, electrophysiology, neuromodulation
MeSH: Electrodes, Implanted*, Implantable Neurostimulators*, Animals, Equipment Design, Humans, Reproducibility of Results (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (UH3 NS121565); NIHR Oxford Biomedical Research Centre (NIHR203316); National Institue of Mental Health (R01 MH123634); Medical Research Council (MC_UU_00003/3)
Citations: not cited yet (Europe PMC); 136 references in the paper

Abstract

Objective. The development of increasingly complex implantable neuromodulation devices requires extensive benchtop validation and testing prior to in vivo implementation. Our goal is to enable this in vitro testing by creating a benchtop system that is accessible, easy-to-use, and functions reliably across academic and industry environments, reducing discordant results that arise from discordant approaches. Furthermore, such a system paves the way for standardizing benchtop characterization of future implantable systems by establishing a repeatable and comparable methodology for validating these devices and their capabilities. Approach. We describe the NeuroTest Board (NTB), a low-cost, open-source device that combines a low-noise current source waveform generator with a high-precision data acquisition system. We provide information on its architecture, operation, and performance as an initial benchmark of the potential capabilities of the system. Main results. We demonstrate the use of the NTB to assess electrophysiology systems in two separate applications. In one case, we assess a commonly used laboratory system (the RHD2132 digitizing headstage and Open Ephys data acquisition system), while the second case characterizes an investigational neuromodulation device in development for human use (the CorTec Brain InterChange). Following this initial sensing characterization, we explore stimulation-related artifacts that would hamper in vivo data collection. With the NTB, we develop and evaluate artifact mitigation strategies for each system, thus demonstrating an example application of this benchtop testbed. Significance. The ability to test neuromodulation devices against a common benchmark will enable faster development of novel therapeutics by increasing inter-institutional reliability and decreasing troubleshooting time.

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

Repository

Its files are read in the Code ↔ Paper reader above.

uw-herron-lab/NeuroTest-Board

License: other
State: the link answers, verified on 7 November 2026
Evidence: files inventoried
Commit: 227ca3acd7591292238136e6debc36cd8e9b0d5f, 19 October 2026
Languages: C/C++ (5), C++ (4)
Size: 27 files, 9 scripts
Software Heritage: not archived
Found in: “Supplemental material”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 7 November 2026: the link answers
  • 7 November 2026: the link answers
11 files
At the source: github.com/uw-herron-lab/NeuroTest-Board

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;
  • 9 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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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

The data that support the findings of this study are openly available at the following URL/DOI: https://github.com/uw-herron-lab/NeuroTest-Board [136].

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

Versions

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Version 1, 7 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 6 MeSH terms, 4 funders, 110 references.

Cite

This paper

JK, M., Cho, H., Benjaber, M., TJ, D., AS, W., & JA, H. (2026). NeuroTest: a benchtop testbed for evaluating sensing-capable electrophysiology and neurostimulation systems. Journal of neural engineering, 23(6), 066003. https://doi.org/10.1088/1741-2552/aeb82d

BibTeX

@article{jk2026neurotest,
author = {JK, Morrow and Cho, H and Benjaber, M and TJ, Denison and AS, Widge and JA, Herron},
title = {{NeuroTest: a benchtop testbed for evaluating sensing-capable electrophysiology and neurostimulation systems}},
journal = {Journal of neural engineering},
year = {2026},
month = nov,
volume = {23},
number = {6},
pages = {066003},
publisher = {IOP Publishing},
issn = {1741-2560},
doi = {10.1088/1741-2552/aeb82d},
url = {https://doi.org/10.1088/1741-2552/aeb82d},
pmid = {42913699},
pmcid = {PMC13733271}
}

RIS

TY - JOUR
AU - JK, Morrow
AU - Cho, H
AU - Benjaber, M
AU - TJ, Denison
AU - AS, Widge
AU - JA, Herron
TI - NeuroTest: a benchtop testbed for evaluating sensing-capable electrophysiology and neurostimulation systems
T2 - Journal of neural engineering
J2 - J Neural Eng
PY - 2026
DA - 2026/11/04
VL - 23
IS - 6
SP - 066003
SN - 1741-2560
PB - IOP Publishing
DO - 10.1088/1741-2552/aeb82d
UR - https://doi.org/10.1088/1741-2552/aeb82d
LA - en
ER -

CSL-JSON

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"id": "10.1088/1741-2552/aeb82d",
"type": "article-journal",
"title": "NeuroTest: a benchtop testbed for evaluating sensing-capable electrophysiology and neurostimulation systems",
"container-title": "Journal of neural engineering",
"author": [
{
"family": "JK",
"given": "Morrow"
},
{
"family": "Cho",
"given": "H"
},
{
"family": "Benjaber",
"given": "M"
},
{
"family": "TJ",
"given": "Denison"
},
{
"family": "AS",
"given": "Widge"
},
{
"family": "JA",
"given": "Herron"
}
],
"container-title-short": "J Neural Eng",
"volume": "23",
"issue": "6",
"page": "066003",
"DOI": "10.1088/1741-2552/aeb82d",
"PMID": "42913699",
"PMCID": "PMC13733271",
"ISSN": "1741-2560",
"publisher": "IOP Publishing",
"URL": "https://doi.org/10.1088/1741-2552/aeb82d",
"language": "en",
"issued": {
"date-parts": [
[
2026,
11,
4
]
]
}
}

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