MEG Source Reconstruction with Basis functions source model

The aim of this paper is to introduce a classical method of pattern recognition as the solution for the medical imaging, and to provide a new angle of using the pattern recognition theory for MEG source reconstruction. We explore a new method of MEG source spatio-temporal reconstruction based on modeling the neural source with extended basis functions. Inspired by the graph theory that Laplacian eigenvectors of spherical mesh are equivalent to its basis functions representing the cortex mesh, we build a new model to describe the current source distributed on each mesh vertex. This model consists of analogous basis functions and unknown weighted coefficients. Along with leadfield, the weighted coefficients can be calculated in the light of the forward formulae of MEG. Expanding this process from a single time point to continuous time series, it is able to obtain the spatio-temporal reconstructed source distributed on cortical mesh vertices. Under the condition of zero-mean Gaussian noise with small value of variance, the results show robustness to noise and better performance than minimum-norm, but intensive to the deep sources.