Brain-Computer Interfacing: More than the sum of its parts

14 May 2013

Reinhold Scherer Graz University of Technology, Austria

The performance of non-invasive electroencephalogram-based (EEG) brain–computer interfaces (BCIs) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio of the EEG, which limit the bandwidth and hence the available applications. Optimization of both individual components of BCIs and the interrelationship between them is crucial to enhance bandwidth. In other words, neuroscientific knowledge and machine learning need to be optimized by considering concepts from human–computer interaction research and usability. In this talk, I will discuss the big challenges in the field and review ongoing relevant research in our lab that addresses several important issues for BCIs based on the detection of transient changes in oscillatory EEG activity.



Reinhold Scherer received the M.S. and Ph.D. degrees in computer science from the Graz University of Technology, Graz, Austria, in 2001 and 2008, respectively. From 2008 to 2010 he was a postdoctoral research fellow in the Department of Computer Science and Engineering at the University of Washington, Seattle, USA. Currently, he is Assistant Professor at the Graz University of Technology, Graz, Austria, and deputy head of the Institute for Knowledge Discovery at the Graz University of Technology. He is technical manager at the Institute for Neurological Rehabilitation and Research affiliated with the rehabilitation center Judendorf-Strassengel, Judendorf-Strassengel, Austria. His research interest includes direct brain-computer interfacing based on EEG and ECoG signals, statistical and adaptive signal processing, and functional brain mapping and robotics-mediated rehabilitation.