iLab: From Brain to Machine and Back

Aline Xavier Fidêncio; Felix Grün; Christian Klaes; Ioannis Iossifidis Non-invasive BCIs are promising assistive technologies, but real-world EEG is non-stationary—often causing performance to degrade over time. One route to robustness is to adapt the system online using feedback signals. In this work we study the limits of a closed-loop BCI framework that uses error-related potentials … Read More “New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning” »

Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis How can we tell whether a neural network is learning generalizable structure versus memorizing rare patterns? In this paper, we study distributional signatures inside networks trained under memorization-heavy regimes. Our starting point is the idea that memorization corresponds to learning “rare” input features—an effect that can … Read More “New paper: Distributional Properties of ReLU-Activations in Neural Networks That Learn by Memorization” »