Tomasz M. Rutkowski, Ph.D.

tomek

Tomasz 'Tomek' Rutkowski

Assistant Professor
Multimedia Lab, Computer Science Department, School of Informatics and Tara Life Science Center,
Faculty of Engineering, Information & Systems,
University of Tsukuba, Japan

 

 

Brief Biography

Tomasz M. Rutkowski received his M.Sc. in Electronics and Ph.D. in Telecommunications and Acoustics from Wroclaw University of Technology, Poland, in 1994 and 2002, respectively. He received postdoctoral training at the Multimedia Laboratory, Kyoto University, and in 2005-2010 he worked as a research scientist at RIKEN Brain Science Institute, Japan. Currently he serves as Assistant Professor at the University of Tsukuba, and as a visiting scientist at RIKEN Brain Science Institute. Professor Rutkowski’s research interests include computational neuroscience, especially brain-computer interfacing technologies, computational modeling of brain processes, neurobiological signal and information processing, multimedia interfaces and interactive technology design. He is a senior member of IEEE, a member of the Society for Neuroscience, and the Asia-Pacific Signal and Information Processing Association (APSIPA). He is a member of the Editorial Board of Frontiers in Fractal Physiology and serves as a reviewer for PLOS One, IEEE TNNLS, IEEE TSMC – Part B, Cognitive Neurodynamics, and the Journal of Neural Engineering.

Spatial Auditory and Tactile BCI in Application to Vehicular Robot Control - Research Supported by SCOPE Grant in Japan

The talk will summarize recent developments in my lab that explore the extent to which spatial auditory and tactile stimuli can serve as a platform for a brain computer interface (BCI) that could be used in an interactive application such as robotic vehicle operation.  Experimental results with many subjects performing online spatial auditory and tactile BCI sessions provide a validation of the novel paradigms, while the feasibility of the concept is illuminated through information-transfer rates. I will also review our data-driven signal processing and classification improvement procedures which greatly improve online BCI experiments.