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

Area of Science:

• Neural Engineering
• Machine Learning Applications

Background:

• Machine learning (ML) is increasingly vital in neural engineering for pattern recognition.
• Ensuring the validity and reproducibility of ML methods is critical due to past retractions from misuse.
• Challenges in neural engineering model validation include limited subjects, non-independent samples, and high heterogeneity.

Purpose of the Study:

• Propose the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist.
• Promote transparent, reproducible, and valid application of ML in neural engineering.
• Address unique challenges in neural engineering model validation.

Main Methods:

• Developed the first version of the NERVE-ML checklist.
• Highlighted unique validation challenges in neural engineering.
• Used case studies to demonstrate the impact of different validation approaches.

Main Results:

• Improper validation can lead to flawed studies and overclaimed conclusions.
• The NERVE-ML checklist provides guidelines for reproducible and valid ML in neural engineering.
• Case studies showed divergent conclusions based on validation methods.

Conclusions:

• The NERVE-ML checklist aims to enhance the quality and impact of neural engineering research.
• Proper validation and scoping of conclusions are essential for ML's contribution to the field.
• Implementing NERVE-ML will foster a future of reliable ML in neural engineering.