Different Algorithms (Might) Uncover Different Patterns: A Brain-Age
  Prediction Case Study
Tobias Ettling Sari Saba-Sadiya Gemma Roig
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.