3-Dimensional Brain Source Imaging by Means of Laplacian Weighted Minimum Norm Estimate in a Realistic Geometry Head Model

Abstract

We investigated the source localization performance of the Laplacian weighted minimum norm (LWMN) estimate technique in a realistic geometry (RG) head model in the present study. We simulated current sources at different brain regions with various noise levels. The present results show there is no obvious depth dependency on the three-dimensional (3D) source estimation. The average source localization error over all simulated cases is about 10 mm. The tangential sources exhibit larger localization errors than the radial sources when they are close to the epicortical surface. The localization error will increase when the noise level increases. The LWMN technique was applied to source imaging of motor potentials induced by finger movement in a human subject. Both activities in the motor and premotor cortex, which are related to the execution and coordinating of the finger movement, were reconstructed by the LWMN technique. The present study suggests that LWMN has great ability in 3D sources imaging.