The human brain is a highly convoluted surface with many folds and fissures that vary in size, shape and extent from person to person. Mathematical and computational models and tools are required that will assist in quantifying these individual differences and help researchers to understand whether these differences play a role in the way the brain functions. Recently there has been great interest in trying to unfold and flatten the cortical surface to create flat maps of the brain. It is anticipated that these maps will allow locations of functional activation from different subjects to be compared more easily. I will present a novel computer realization of the Riemann Mapping Theorem that uses circle packings to produce an initial approximation of the conformal map of the surface of the human brain. I will present results from this mapping technique and discuss some of the topological issues that arise. If time permits, I will also discuss a model that represents the head as three concentric spherical shells and a neural source of activity as a dipole. This model can then be used to localize a neural source of activity in the brain from EEG data.