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UID:pretalx-juliacon-2026-LJVPDR@pretalx.com
DTSTART;TZID=CET:20260813T111500
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DESCRIPTION:EEG topoplots are a central visualization tool in computational
  neuroscience and biological signal analysis. However\, they typically dis
 play only mean effects while omitting uncertainty arising from subjects\, 
 trials\, and model variability. Our qualitative user study with domain exp
 erts shows that researchers consider uncertainty visualization essential f
 or interpretation\, yet report lacking appropriate tools and established m
 ethods to implement it in practice.\n\nIn this talk\, I present ten uncert
 ainty visualization prototypes developed in UnfoldMakie\, a Julia-based ec
 osystem for regression-based EEG analysis. Several approaches\, such as bi
 variate and value-suppressing topoplots\, introduce entirely new visualiza
 tion strategies. Some are already available\, while others are in active d
 evelopment.\n\nWe are currently conducting a quantitative user study to sy
 stematically assess which of these plots most effectively support accuracy
  and interpretability in typical EEG analysis tasks. By empirically compar
 ing these designs\, we aim to identify best practices rather than proposin
 g yet another visualization variant.\n\nAs tool developers\, we argue that
  enabling appropriate uncertainty representations is a responsibility: wit
 hout accessible methods\, researchers lack the means to communicate variab
 ility\, which directly impacts research integrity and reproducibility in c
 omputational biology.
DTSTAMP:20260502T113843Z
LOCATION:Room 1
SUMMARY:Visualizing Uncertainty in EEG Topoplots: New Approaches in UnfoldM
 akie - Vladimir Mikheev
URL:https://pretalx.com/juliacon-2026/talk/LJVPDR/
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