The focus of my research is building a stable brain-computer interface for end-stage (CLIS) ALS patients. To this end, I'm pursuing a number of paths:
--In collaboration with the Universitätsklinikum Tübingen we are undertaking a study of resting-state EEG recordings in ALS patients to attempt to understand how the disease affects the recordable electrophysiology
--Increases in the amount and variety of BCI data being recorded in our lab and others places a new emphasis on transfer learning as a method for increasing BCI effectiveness across subjects. I am interested in applying new techniques from the machine-learning literature to BCI as well as studying what sort of techniques can be developed that take advantage of the unique nature of brain-based data for classification or regression.
--In a similar vein, signal denoising techniques can also benefit from the heterogeneity of sources, something which has so far been underutilized in domains such as ICA.
These paths will, hopefully, culminate in a long-term implanted recording device for an end-stage ALS patient in which we can synthesize these findings to finally allow them to speak again.
Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 131-136, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)
Unser Ziel ist es, die Prinzipien von Wahrnehmen, Lernen und Handeln in autonomen Systemen zu verstehen, die mit komplexen Umgebungen interagieren. Das Verständnis wollen wir nutzen, um künstliche intelligente Systeme zu entwickeln.