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

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