Assessing Robustness of EEG Representations under Data-shifts via Latent Space and Uncertainty Analysis. (arXiv:2209.11233v1 [eess.SP])

The recent availability of large datasets in bio-medicine has inspired the
development of representation learning methods for multiple healthcare
applications. Despite advances in predictive performance, the clinical utility
of such methods is limited when exposed to real-world data. Here we develop
model diagnostic measures to detect potential pitfalls during deployment
without assuming access to external data. Specifically, we focus on modeling
realistic data shifts in electrophysiological signals (EEGs) via data
transforms, and extend the conventional task-based evaluations with analyses of …

analysis arxiv data eeg robustness space uncertainty