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Performance analysis of reduced-rank beamformers for estimating dipole source signals using EEG/MEG

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3 Author(s)
D. Gutierrez ; Dept. of Comput. Syst. Eng. & Autom., Nat. Autonomous Univ. of Mexico, Mexico City, Mexico ; A. Nehorai ; A. Dogandzic

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 response and filtered observed signal. We express the MMSE as a function of the filter's rank and use it as a criterion to evaluate the performance of the beamformers. We do not make any assumptions on the rank of the interference-plus-noise covariance matrix. Instead, we treat it as low-rank and derive a general expression for the MMSE. We present numerical examples to compare the MSE performance of beamformers commonly studied in the literature: principal components (PCs),cross-spectral metrics (CSMs), and eigencanceler (EIG) beamformers. Our results show that good estimates of the dipole source signals can be achieved using reduced-rank beamformers even for low signal-to-noise ratio (SNR) values

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:53 ,  Issue: 5 )