Analysis of intracranial EEG recordings from epilepsy patients Analysis of intracranial EEG recordings from epilepsy patients

The research of the nonlinear time series analysis group is positioned at the interface between physics, applied mathematics, neuroscience, and engineering. 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. In patients with a certain type of epilepsy an optimal diagnostics requires to carry out electroencephalographic recordings directly from inside the patients’ skull. These intracranial EEG recordings allow medical doctors to localize the areas in the patients’ brain in which epileptic seizures start (epileptic focus). Provided that a number of medical criteria are fulfilled, these brain areas can then be removed by neurosurgery rendering the patient completely seizure-free. For medical doctors the most important information remains to observe several acute seizure onsets. Recent work has shown that the application of signal analysis techniques to intracranial EEG recordings allows one to localize the epileptic focus also from recordings from time periods where no seizure took place. The project aim is to apply additional nonlinear time series analysis techniques to intracranial EEG recordings to test whether this allows for such a localization of the epileptic focus.

For more details see our recent publications at Please note that we publish on different topics. You will be able to conclude from titles which projects are related to the study of EEG recordings.

In a first phase the student will familiarize himself/herself with the signal analysis techniques studying signals from mathematical model systems. In the second step, the student will learn about intracranial EEG recordings. The final phase will consist of the application of the signal analysis techniques to the EEG recordings.

As a starting point, the student should have a good knowledge in basics of dynamical systems theory as well as in linear signal techniques such as spectral and correlation analysis. Knowledge on nonlinear signals techniques is a plus. However, the essentials of this nonlinear analysis framework will also be taught in one of the elective courses. The student should certainly know how to program in Matlab or some other higher programming language. On the other hand, the student will be instructed how to use the High Performance Computing Cluster for distributed computing. Basic knowledge in electrophysiology and epileptology is a plus but not a prerequisite.

Both the study of mathematical model systems as well as the analysis of the EEG is purely numerical. The programming language Matlab will be used for this purpose. This thesis involves no laboratory, experimental or clinical work. The student will not be in contact with the patients. Previous experience in the study of dynamical systems is a plus. The candidate will work in our nonlinear time series analysis group, an international team that puts a strong emphasis on well-organized supervision of its junior members and regular research activities such as journal clubs, project meetings, etc.