About the lab

Our group works on the intersection of data science and neuroimaging. We develop algorithms to analyze EEG, MEG (electro- and magnetoencephalography) and other electrophysiological data. The main advantage of these recordings is their high temporal resolution, which enables us to study dynamical properties of neuronal populations inaccessible to fMRI. The downside of EEG and MEG, on the other hand, is that the mixing of different brain activities into sensors can lead to misinterpretations about the locations and the functional connectivity profile of the underlying brain sources. Our group develops davanced data analysis methods that allow reliable conclusions about communication patterns in the brain. We use these methods to identify neural diagnostic markers of brain diseases.

The group is tightly linked with the Department of Uncertainty, Inverse Modeling and Machine Learning at Technische Universität Berlin, and Research Group 8.44 Machine learning and Uncertainty at Physikalisch-Technische Bundesanstalt (PTB) Berlin, both also led by Dr. Stefan Haufe.