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We study the performance of various beamformers for estimating a current dipole source at a known location using electroencephalography (EEG) and magnetoencephalography(MEG). We present our beamformers in the form of the generalized sidelobe canceler (GSC). Under this structure, the beamformer can be solved by finding a filter that achieves the minimum mean-squared error (MMSE) between the mainbeam...
We present analytical forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG) assuming a single-shell ellipsoidal geometry that approximates the anatomy of the head and a dipole current models the source. The structure of our solution facilitates the analysis of the inverse problem by factoring the lead field into a product...
We estimate a source current dipole at a known location in the presence of low-rank interference using magnetoencephalography (MEG). We present a space-time processor for MEG data based on the generalized sidelobe canceler (GSC). We extend the classical vector beamformer to a matrix structure without making any assumptions on the rank of the covariance matrix of noise and interference, or constraint...
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