摘要
A matching pursuit (MP) based algorithm, called source deflated matching pursuit (SDMP), is proposed for locating sources of brain activity. By iteratively deflating the contribution of identified sources to multiple measurement vectors (MMVs), the SDMP algorithm transforms the original multi-basis-vector/matrix selection problem into a single-basis-vector/matrix selection problem, which not only mitigates the residual-source interference but also remedies the intrinsic bias when locating deep sources. The robustness of the proposed algorithm to two bias factors is verified through simulations.