Machine Learning in Neural Engineering

Department Empirical Inference
Max Planck Institute for Intelligent Systems

Our group develops machine learning algorithms to study the neural basis of (disorders of) cognition. We are particularly interested in the development of causal inference methods that enable us to relate the structure of cortical networks to behavioural deficits in patient populations. We translate these insights into clinical applications by means of brain-computer interfacing (BCI). We focus on two clinical applications. First, we design BCIs for communicating with severely paralysed patients in late stages of amyotrophic lateral sclerosis (ALS). Second, we develop novel rehabilitation systems for stroke patients, combining robot-assisted physical therapy with closed-loop neural feedback. In both projects, we closely work with clinical partners.

We have received several awards for our work, including the International BCI Research Award 2011 and the IEEE Brain Initiative Best Paper Award 2016. Media coverage of our work (in German) can be found on Deutsche Welle TV, ZDFinfo, and at the media center of the Max Planck Society.

Last updated: 07.05.17