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

David E Carlson, Ricardo Chavarriaga, Yiling Liu, Fabien Lotte, Bao-Liang Lu
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Abstract

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.

Neural - ml(机器学习的神经工程再现性和有效性要点)检查表:确保机器学习推进神经工程。
目的:机器学习捕捉复杂模式的能力使其在神经工程研究中至关重要。随着越来越多的使用,确保机器学习方法的有效性和可重复性至关重要。不幸的是,在实践中并非总是如此,因为最近由于滥用机器学习方法和验证程序,各个科学领域都出现了撤稿事件。为了解决这些问题,我们提出了第一个版本的神经工程机器学习可重复性和有效性要点(Neural - ml)清单,该框架旨在促进机器学习在神经工程中的透明、可重复和有效应用。& # xD;方法。我们强调了神经工程中模型验证的一些独特挑战,包括有限的受试者数量,重复或非独立样本以及高度受试者异质性的困难。通过详细的案例研究,我们展示了不同的验证方法如何导致不同的科学结论,强调了在neure - ml清单指导下选择适当程序的重要性。有效地应对这些挑战并正确界定科学结论,将确保机器学习有助于而不是阻碍神经工程的进步。我们的案例研究表明,不当的验证方法可能导致有缺陷的研究或夸大的科学结论,使科学论述复杂化。(neural - ml)清单通过提供指导方针,有效地解决了这些问题,以确保神经工程中的机器学习方法是可重复的,并得出有效的科学结论。& # xD;意义。通过有效地解决这些挑战,并在neural - ml检查表的指导下正确界定科学结论,我们的目标是帮助为机器学习可靠地提高神经工程研究的质量和影响的未来铺平道路。 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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