[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117368-en":3,"doc-seo-117368-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117368,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The NERVE-ML checklist - Ensuring Machine Learning Validity and Reproducibility in Neural Engineering","Machine learning’s ability to capture intricate patterns has made it central to neural engineering research, yet validation and reproducibility have not always met rigorous standards. Recent retractions across fields show that misuse of ML methods and ML validation procedures can produce flawed or overclaimed conclusions that persist in the scientific record. The NERVE-ML checklist is proposed as a first-version framework addressing key challenges in neural engineering model validation, with case studies showing how guided validation supports transparent, reproducible, and valid scientific outcomes.","J. Neural Eng. 22 (2025) 021002 [https://doi.org/10.1088/1741-2552/adbfbd](https://doi.org/10.1088/1741-2552/adbfbd)  \nJournal of Neural Engineering  \nTOPICAL REVIEW  \n   The NERVE-ML (neural engineering reproducibility and validity  \nOPEN ACCESS essentials for machine learning) checklist: ensuring machine RECEIVED16 October 2024 learning advances neural engineering∗  \nREVISED  \n10 February 2025 David E Carlson1,2, ∗∗􀁂, Ricardo Chavarriaga3􀁂, Yiling Liu4􀁂, Fabien Lotte5,6􀁂 and Bao-Liang Lu7,8􀁂 AC12CEPTED FMarchP2BLICATION 1 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States of America  \n2 Department of Computer Science, Department of Civil and Environmental Engineering, Duke University, Durham, NC, United States PUBLISHED of America  \n27 March 2025 3 Centre for Artificial Intelligence, School of Engineering, Zurich University of Applied Sciences ZHAW, Winterthur, Switzerland  \n  4 Program in Computational Biology and Bioinformatics, Duke University School of Medicine, Durham, NC, United States of America Original content from 5 Inria Center at the University of Bordeaux, Talence 33405, France  \nthis work maybe used 6 LaBRI (CNRS/University Bordeaux/Bordeaux INP), Talence 33405, France  \nunCrr ttiveCrmmmsoonfsthe 7 Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong Attribution 4 .0 licence. University, Shanghai 200240, People’s Republic of China  \nAny further distribution 8 RuiJin-Mihoyo Laboratory, Clinical Neuroscience Center, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai of this work must 200020, People’s Republic of China  \nmaintain attribution to ∗∗Author to whom any correspondence should be addressed.  \nthe author(s) and the title  \nof the work, journal [E-mail: david.carlson@duke.edu](E-mail: david.carlson@duke.edu)[ ](E-mail: david.carlson@duke.edu)citation and DOI.  \nKeywords: machine learning, reproducibility, validation, checklist  \nAbstract  \nObjective. 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 challengesand 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.  \n∗  \nAuthors are lis","cbCaitNKnGn54TRO","https://ap.wps.com/l/cbCaitNKnGn54TRO","pdf",2524932,1,29,"English","en",105,"# Introduction\n## Motivation and need for validity and reproducibility\n# NERVE-ML checklist overview\n## Key challenges in model validation for neural engineering\n## Case studies linking validation choices to divergent conclusions\n# Main results and significance\n## Guidelines to support reproducible and valid scientific conclusions","[{\"question\":\"Why is validity and reproducibility critical in neural engineering machine learning research?\",\"answer\":\"As ML becomes more integrated into neural engineering, it is essential to prevent incorrect conclusions from flawed or misapplied validation and to ensure that research advances reliably. The document highlights retractions and persistent incorrect papers as consequences of inadequate validation.\"},{\"question\":\"What unique challenges in neural engineering model validation does NERVE-ML address?\",\"answer\":\"It focuses on challenges such as limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. These factors can distort validation outcomes if not handled appropriately.\"},{\"question\":\"How do the case studies support the need for the NERVE-ML checklist?\",\"answer\":\"The case studies show that different validation approaches can lead to divergent scientific conclusions and that improper validation can produce flawed studies or overclaimed results. The checklist provides guidance to select procedures that support valid and reproducible outcomes.\"}]","The NERVE-ML checklist - Ensuring Machine Learning Validity and Reproducibility in Neural Engineering | PDF",1785675411,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-nerve-ml-checklist-ensuring-machine-learning-validity-and-reproducibility-in-neural-engineering","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-nerve-ml-checklist-ensuring-machine-learning-validity-and-reproducibility-in-neural-engineering/117368/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is validity and reproducibility critical in neural engineering machine learning research?","Question",{"text":75,"@type":76},"As ML becomes more integrated into neural engineering, it is essential to prevent incorrect conclusions from flawed or misapplied validation and to ensure that research advances reliably. The document highlights retractions and persistent incorrect papers as consequences of inadequate validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What unique challenges in neural engineering model validation does NERVE-ML address?",{"text":80,"@type":76},"It focuses on challenges such as limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. These factors can distort validation outcomes if not handled appropriately.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the case studies support the need for the NERVE-ML checklist?",{"text":84,"@type":76},"The case studies show that different validation approaches can lead to divergent scientific conclusions and that improper validation can produce flawed studies or overclaimed results. 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