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The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering.

Publication ,  Journal Article
Carlson, DE; Chavarriaga, R; Liu, Y; Lotte, F; Lu, B-L
Published in: Journal of neural engineering
March 2025

Objective.Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering.Approach.We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering.Main results.Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions.Significance.By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research.

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Published In

Journal of neural engineering

DOI

EISSN

1741-2552

ISSN

1741-2560

Publication Date

March 2025

Volume

22

Issue

2

Related Subject Headings

  • Reproducibility of Results
  • Machine Learning
  • Humans
  • Checklist
  • Biomedical Engineering
  • 4003 Biomedical engineering
  • 3209 Neurosciences
  • 1109 Neurosciences
  • 1103 Clinical Sciences
  • 0903 Biomedical Engineering
 

Citation

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Carlson, D. E., Chavarriaga, R., Liu, Y., Lotte, F., & Lu, B.-L. (2025). The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering. Journal of Neural Engineering, 22(2). https://doi.org/10.1088/1741-2552/adbfbd
Carlson, David E., Ricardo Chavarriaga, Yiling Liu, Fabien Lotte, and Bao-Liang Lu. “The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering.Journal of Neural Engineering 22, no. 2 (March 2025). https://doi.org/10.1088/1741-2552/adbfbd.
Carlson, David E., et al. “The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering.Journal of Neural Engineering, vol. 22, no. 2, Mar. 2025. Epmc, doi:10.1088/1741-2552/adbfbd.
Journal cover image

Published In

Journal of neural engineering

DOI

EISSN

1741-2552

ISSN

1741-2560

Publication Date

March 2025

Volume

22

Issue

2

Related Subject Headings

  • Reproducibility of Results
  • Machine Learning
  • Humans
  • Checklist
  • Biomedical Engineering
  • 4003 Biomedical engineering
  • 3209 Neurosciences
  • 1109 Neurosciences
  • 1103 Clinical Sciences
  • 0903 Biomedical Engineering