New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning
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” »
