Data-driven forward model inference for EEG brain imaging

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.

Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity

“…The simplicity and transferability of BAE can be highlighted when compared to empirical Bayesian frameworks [58,59,60,61,62]. In an empirical Bayesian framework, the unknown forward model parameters are also treated as stochastic variables.…”

“…In an empirical Bayesian framework, the unknown forward model parameters are also treated as stochastic variables. However, unlike in BAE, in this option these forward model parameters are estimated from the measured EEG data alongside the source activity and parameters of the prior model by maximizing the Bayesian model-evidence (or a free-energy approximation thereof) [60], or marginalized by using Bayesian model averaging [63,64]. In other words, these are a-posteriori procedures since they require and depend on the EEG measurement data which is then used to estimate both the source activity and the parameters of the forward model that is exploited for the inference.…”

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

“…The forward model is constructed from electrophysiological first principles based on anatomical data and assumed values 15 of conductivities of the various tissues; scull, scalp, etc. Attempts have been made at estimating the forward model from the EEG data, see e.g., (Stahlhut et al, 2011;Akalin Acar et al, 2016;Hansen et al, 2016). However, in the following we will assume the forward model known and focus on solving the inverse problem.…”

Three‐dimensional FDTD modeling of neurons to solve EEG and MEG forward problem

“…In addition, the tiny magnetic fields produced by the currents of neurons activity are measured by removing the effects of electromagnetic characteristics. In this method, the magnetic field is read using superconducting quantum interference devices.This method has high spatial temporal accuracy and is called magnetoencephalography (MEG) …”

“…In this method, the magnetic field is read using superconducting quantum interference devices.This method has high spatial temporal accuracy and is called magnetoencephalography (MEG). 10,11 Since the electromagnetic biophysics of the cells are known, this measure would help analyze neural networks. 12,13 Analyzing the measured data is the demand and different modeling approaches have been proposed to assist the analyses.…”