Our research is positioned at the interface between physics, applied mathematics, and neuroscience. One of our main targets is the development of innovative nonlinear time series analysis techniques. These techniques aim to characterize dynamical systems for which nonlinearities cause a complicated temporal evolution. A further main target of our work is the application of nonlinear time series techniques to electrophysiological recordings from the brain of epilepsy patients. We furthermore study coupled oscillator network models that show so-called chimera states.

The links in this list below direct you to videos in which we explain some of our research.

• Summaries of our 2022 publication in Physical Review E. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients.
  • Spanish version for non-expert audience
  • English version for non-expert audience
  • English version for expert audience
• Conference talk about our 2021 publication in Chaos: Chimeras confined in fractal boundaries in the complex plane. 
  • 2021: Chimeras and Fractals from a Network of Coupled Quadratic Maps
• Research seminars providing an overview about our work
  • 2022: Quantitative analysis of intracranial EEG recordings from epilepsy patients.
  • 2018: Concepts and tools from nonlinear dynamics: Helpful to understand and diagnose epilepsy?
  • 2015: Why is our nonlinear time series analysis group not linear?