Evaluating The Impact Of White Matter Conductivity Anisotropy On Reconstructing Eeg Sources By Linearly Constrained Minimum Variance Beamformer

EEG source imaging aims to reconstruct the neural activities of the brain accountable for the recorded scalp potentials. This procedure requires solving two problems, namely, forward and inverse problems. For the forward problem, the head is modeled as a volume conductor and the Poisson's equation that describes the relation between neural activities and the observed EEG signals is solved. In this study, we enhanced the forward model by considering the white matter anisotropic conductivity tensor estimated from diffusion-weighted images. The second step is to solve the inverse problem in which the activity of the brain sources is estimated from measured data using the forward solution obtained in the previous step. Spatial filtering, also called beamforming, is an inverse method that reconstructs the time course of the source at a particular location by a linear combination of the sensor space data. We evaluated quantitatively the impact of the enhanced anisotropic forward model on linearly constrained minimum variance beamformer for both superficial and deep sources in a simulation environment, in terms of normalized mean squared error. Results showed that the anisotropic head forward model moderately enhanced the reconstruction of the sources, especially deep thalamic and olfactory sources.