GRSR - a guideline for reporting studies results for machine learning applied to Electroencephalogram data

Abstract

Background: The last decade was marked by increased neuroscience research involving machine Learning (ML) and medical images such as functional magnetic resonance and electroencephalogram (EEG). There are many challenges in this research field, including the need for more data and a standard for presenting the results. Since ML models tend to be sensitive to the input data, different strategies for data acquisition, preprocessing, feature selection, and validation significantly impact the results achieved. Therefore, a significant variation while presenting the results makes it challenging to compare the results. Results: This work aims to tackle the lack of a standard model by presenting a guideline, conform Quadas-2, that covers the most critical data for studies to demonstrate when using EEG and ML for addressing neurological disorders. Conclusions: This guideline allows a structural presentation of the primary data of studies using ML applied to EEG, improving comparison between studies while also allowing fair comparisons.