Neuromagnetic source parameter estimation of MEG inverse problem by a synthetic nonlinear optimization method

The ill-posed MEG (magnetoencephalography) inverse problem draws on a wide range of signal processing techniques. In this paper, we focus on the source localization, i.e., estimating neuromagnetic source parameters by modeling the total source activity in the brain as a single equivalent current dipole. This kind of estimation problem can be solved by nonlinear optimization methods. According to the determination criteria of residual-error-size, the synthetic algorithm proposed here means that we can choose different optimization methods during the source scanning procedure. Computer simulations have been conducted to test the feasibility of the presented approach.