Klaus-Robert Müller

Klaus-Robert Muller studied physics until 1989 and received the Ph.D. degree in computer science in 1992 at KIT. He has been a Professor of computer science at TU Berlin since 2006, and at the same time director of the Bernstein Focus on Neurotechnology Berlin. He was a research fellow at the University of Tokyo in 1994/95. In 1995, he founded the Intelligent Data Analysis group at GMD-FIRST and directed it until 2008. From 1999 to 2006, he was a Professor at the University of Potsdam. Dr. Muller was amongst others the SEL Alcatel Communication Award in 2006 and the Berliner Wissenschaftspreis des regierenden Burgermeisters in 2014. In 2012, he was elected to be a member of the German National Academy of Sciences-Leopoldina. His research interests are intelligent data analysis, machine learning, signal processing, and brain-computer interfaces.

Associated articles

TBME, Featured Articles
M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring
For the further development of the fields of telemedicine, neurotechnology and Brain-Computer Interfaces (BCI), advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bio-electrical and bio-optical neurophysiological measurements –... Read more
TNSRE, Featured Articles
Open Access Dataset for EEG+NIRS Single-Trial Classification
       We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated... Read more
TBME, Featured Articles
Mammography Image Quality Assurance Using Deep Learning
Image quality assurance is crucial in mammography to ensure reliable breast cancer diagnostics. Analyzing images of a technical phantom allows to routinely and reliably assess image quality. Current state-of-the-art analysis determines local image quality features by applying pre-processing and regression procedures for a set of repeatedly recorded images. This proof of concept paper demonstrates that mammography image quality assessment can benefit from deep learning. A neural network is trained on a large database of phantom images, and it is shown that the trained net retrieves the local image quality features already from single images without cumbersome pre-processing. This allows to maintain quality standards at significantly less labor... Read more