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 (GBCIC 2017), pages: 131-136, (Editors: Gernot R. Müller-Putz, David Steyrl, Selina C. Wriessnegger, Reinhold Scherer), Verlag der Technischen Universität Graz, 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