Today 4-5 pm in YNiC open plan. Jing Kan from the Department of Computer Science will talk about "Basis functions source model applied to MEG Spatio-Temporal Source Reconstruction". Everyone is welcome to attend. Best wishes Rebecca Abstract: The aim of this paper is to explore a new method of MEG source spatio-temporal reconstruction based on modeling the neural source with extended spherical basis functions. The high resolution 3D cortical mesh is extracted along with the corresponding MRI scan. Inspired by the theory that Laplacian eigenvectors of spherical mesh are equivalent to its basis functions which represent the whole mesh, we build a new model that describes the source distributed on each mesh vertex. This model consists of analogous basis functions and unknown weighted coefficients. Along with the leadfield, the weighted coefficients can be calculated in the light of forward formula of MEG.. The distributed neural source on the mesh is then reconstructed according to the above Basis functions expanded model. Expanding this process from single time point to continuous time series, it is possible to obtain the spatio-temporal reconstructed neural source distributed on cortical mesh vertices. Finally, the method is implemented using real data for signal reconstruction experiments. The robustness of this MEG reconstruction solution is discussed by two aspects. One is to compare with classical methods, i.e. minimum-norm method. The other is to apply the algorithm into meshes with different resolutions. It is clear that these approaches provide a new angle and inspiration of computer graphics to MEG signal reconstruction. Key word: MEG,, inverse problem, eigen-decomposition, basis function, Laplacian eigenvector, spheroidal model, weighted coefficient, spatio-temporal source reconstruction