Data-driven forward model inference for EEG brain imaging

Sofie Therese Hansen, Søren Hauberg, Lars Kai Hansen

Abstract: Highlights • We propose a technique for simultaneous inference of the EEG sources and the EEG forward model. • We show how forward models can be personalized when person-specific knowledge of head geometry and conductivity is missing. • Forward model inference is based on the EEG data and a prior over forward models. • The prior is built by decomposing a corpus of forward models using principal component analysis. • The free energy evaluates the combination of a forward model and the inverse solution under specific source model priors. Abstract Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.