Markus R. Crell

This paper introduces a non-invasive BCI framework for continuous cursor control that supports active and no-movement states, demonstrating its effectiveness in target selection for healthy participants and motor-impaired individuals.

This study classifies handwritten letters from non-invasive EEG signals using direct classification and a two-step kinematic decoding approach, showing improved performance with the latter.