Independent components of magnetoencephalography: localization
Neural Computation
MEG Source Imaging Using Multipolar Expansions
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Independent components of magnetoencephalography: localization
Exploratory analysis and data modeling in functional neuroimaging
Journal of VLSI Signal Processing Systems
Sequential high-resolution direction finding from higher order statistics
IEEE Transactions on Signal Processing
Localization of abnormal EEG sources incorporating constrained BSS
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Journal of Signal Processing Systems
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A new method for source localization is described that is based on a modification of the well-known MUSIC algorithm. In classical MUSIC, the array manifold vector is projected onto an estimate of the signal subspace. Errors in the estimate of the signal subspace can make localization of multiple sources difficult. Recursively applied and projected (RAP) MUSIC uses each successively located source to form an intermediate array gain matrix and projects both the array manifold and the signal subspace estimate into its orthogonal complement. The MUSIC projection to find the next source is then performed in this reduced subspace. Special assumptions about the array manifold structure, such as Vandermonde or shift invariance, are not required. Using the metric of principal angles, we describe a general form of the RAP-MUSIC algorithm for the case of diversely polarized sources. Through a uniform linear array simulation with two highly correlated sources, we demonstrate the improved Monte Carlo error performance of RAP-MUSIC relative to MUSIC and two other sequential subspace methods: S and IES-MUSIC. We then demonstrate the more general utility of this algorithm for multidimensional array manifolds in a magnetoencephalography (MEG) source localization simulation