My interests are in machine learning for closed-loop human-machine interfaces. To this end, I'm pursuing a number of paths:
--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.
--Despite decades of work on decoding prosthesis movements from EMG signals, a robust prosthesis for home use is still beyond our grasp. I am interested in whether machine learning can be used either to help the user learn more reliable control patterns, or whether it can be used to refine a prosthesis's performance during natural use.
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)
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems