Vera Kaiser

TL;DR: A novel combination of tasks that could inspire BCI systems that are more accurate than conventional BCIs are introduced, especially for users who cannot attain accuracy adequate for effective communication.

TL;DR: This proof-of-concept study has demonstrated that with the support of hybrid FES systems consisting of FES and a semiactive orthosis, restoring hand, finger and elbow function is possible in a tetraplegic end-user, and supports the view that in high-level tetrailgic subjects, an initially moderate BCI performance cannot be improved by extensive training.

TL;DR: It is found that training with an MI-based BCI affects cortical activation patterns especially in users with low BCI performance, and this might be useful for clinical applications of BCI which aim at promoting and guiding neuroplasticity.

TL;DR: The post-movement beta rebound occurring after brisk feet movement was used to set up a classifier and this classifier was used in a cue-based motor imagery system, and a self-paced brain-switch based on brisk foot motor imagery was evaluated.

TL;DR: Eight different signal processing methods that aimed to improve classification accuracy were explored and showed that the improved methods described here yielded a statistically significant improvement over the initial data, suggesting that such a hybrid BCI is feasible.