Gernot R Muller-Putz

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 paper develops an online brain-computer interface to detect incongruity in augmented reality by combining eye-tracking and EEG, achieving 70% balanced accuracy in real-time classification.

This study examines EEG responses to incongruent information in an AR environment, finding N400 and P600 components with increased latencies for non-symbolic stimuli and successful decoding using multiple methods.

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.

The paper evaluates four visual cues to determine their impact on brain signals during movement tasks, finding that Rotation and Reference cues minimize variability and improve classifier accuracy for brain-computer interfaces.