Our research is positioned at the interface between computational biomedical engineering, statistical physics, and applied mathematics.

Our main aim is to advance the understanding of functions and dysfunctions of the brain from the perspective of dynamical systems theory. We use three complementary approaches to pursue this goal:

(1) The development of innovative nonlinear signal analysis techniques to study data modalities which are specific for recordings from the brain. This includes for example the detection of directional interactions from point process data or stimulus triggered recordings.

(2) The analysis of large bodies of biomedical data. An example is the analysis of long-term electroencephalographic (EEG) recordings from epilepsy patients with regard to the predictability of epileptic seizures or the localization of the seizure generating brain area.

(3) The data-driven analysis of mathematical models of networks of coupled oscillators. A recent result in this context is our discovery of an intriguing analogy between the collapse of so-called chimera states in networks of phase oscillators on the hand and synchronization-desynchronization phenomena in epileptic seizures on the other hand.

While this research is often of basic nature, we strive to bridge the gap towards the practical applicability of our findings. For this purpose, we collaborate with international clinical partners aiming to exploit our work for an improved diagnostics and treatment of epilepsy patients. Furthermore, we created several successful public domain repositories of biomedical data and analysis tools which allow the broader scientific community to build upon our work.




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