So far my research followed three main directions:
This line of research dealt with the application of time series analysis to electrophysiological data, in particular to the electroencephalogram (EEG) of epilepsy patients. One important aim was the extraction of information that might be useful for diagnostic purposes. Examples include the localization of the epileptic focus or the prediction of epileptic seizures.
This includes the development, analysis and comparison of different approaches to quantify the synchronization between two time series. In more recent works particular attention was paid to measures that estimate the synchronization between discrete events within the time series (such as spikes). Both coupled model systems as well as electrophysiological data were analysed.
The topic of these studies were simulations of neuronal models (such as the FitzHugh-Nagumo or the Hodgkin-Huxley model) under the influence of noise. Typically the regularity of the neuronal response was analyzed depending on the amount of noise and on the correlation strength. Coherence resonance refers to a maximum of regularity for intermediate parameter values.