My primarily research area is machine learning for biomedical signals which cur-rently focuses on functional brain imaging using electroencephalography (EEG) signals.
My research concerns functional brain imaging, where modeling in the context of EEG is my current focus. EEG is a measurement technique of the electrical activity in the brain. EEG is directly related to neuronal signaling and is measured by a set of sensors placed on the scalp in a structured way. Compared to the widely used fMRI and PET, EEG offers high temporal resolution, in fact below 100 ms, and is therefore highly interesting for neuropsychological applications.
Functional brain imaging using EEG is a relatively new research field, which aims at a better understanding of the human brain by localizing so-called current sources with the high temporal resolution. In modern EEG imaging methods, the sources are represented as a dipole field, where each dipole is assigned to a network of tens of thousands cortical neurons.
To accomplish this EEG source localization problem, which is highly underdetermined, my research deals with topics like:
Variational Bayesian (VB) inference methods
Markov Chain Monte Carlo (MCMC) methods
Sparsity (e.g. ARD priors for the current sources in the brain)
Spatio-temporal basis functions to incorporate spatial and temporal dependence of the sources