2026-08-13 –, Room 1
EEG topoplots are a central visualization tool in computational neuroscience and biological signal analysis. However, they typically display only mean effects while omitting uncertainty arising from subjects, trials, and model variability. Our qualitative user study with domain experts shows that researchers consider uncertainty visualization essential for interpretation, yet report lacking appropriate tools and established methods to implement it in practice.
In this talk, I present ten uncertainty visualization prototypes developed in UnfoldMakie, a Julia-based ecosystem for regression-based EEG analysis. Several approaches, such as bivariate and value-suppressing topoplots, introduce entirely new visualization strategies. Some are already available, while others are in active development.
We are currently conducting a quantitative user study to systematically assess which of these plots most effectively support accuracy and interpretability in typical EEG analysis tasks. By empirically comparing these designs, we aim to identify best practices rather than proposing yet another visualization variant.
As tool developers, we argue that enabling appropriate uncertainty representations is a responsibility: without accessible methods, researchers lack the means to communicate variability, which directly impacts research integrity and reproducibility in computational biology.
I work at University of Stuttgart and do visualizations for neuroscience.