Machine learning is a rapidly evolving field with a wide range of applications, including biological signal analysis, where novel algorithms often improve the state-of-the-art. However, robustness to algorithmic variability - measured by different algorithms, consistently uncovering similar findings - is seldom explored. In this paper we investigate whether established hypotheses in brain-age prediction from EEG research validate across algorithms. First, we surveyed literature and identified various features known to be informative for brain-age prediction. We employed diverse feature extraction techniques, processing steps, and models, and utilized the interpretative power of SHapley Additive exPlanations (SHAP) values to align our findings with the existing research in the field. Few of our models achieved state-of-the-art performance on the specific data-set we utilized. Moreover, analysis demonstrated that while most models do uncover similar patterns in the EEG signals, some variability could still be observed. Finally, a few prominent findings could only be validated using specific models. We conclude by suggesting remedies to the potential implications of this lack of robustness to model variability.