My primary research interest is developing new methods for non-invasive brain-computer interfaces (BCIs). BCIs are devices that allow subjects to communicate by intentionally modulating the electromagnetic field of the brain. While nowadays most healthy subjects are capable of operating a BCI that allows basic communication, up to date subjects in late stages of amyotrophic lateral sclerosis (ALS) have failed to communicate by means of a BCI. It is my conviction that in order to advance the current state-of-the-art in BCIs, and enable BCI-communication for subjects in late stages of ALS, we need a better understanding how different brain regions interact in order to solve specific (BCI-related) tasks.

Accordingly, my research focuses on developing and applying methods for connectivity/causal inference in neuroimaging data. Approaches I currently pursue include

• Network information theory
• Causal Bayesian networks
• Granger causality.

It is a particular concern of mine to foster exchange between neuroimaging and machine learning, e.g. by organizing the NIPS 2009 Workshop on Connectivity Inference in Neuroimaging.

I am also interested in spatial filtering for BCIs, particularly in

• Beamforming
• Multiclass Common Spatial Patterns
• Independent Component Analysis.