TY - JOUR
T1 - Performance analysis of reduced-rank beamformers for estimating dipole source signals using EEG/MEG
AU - Gutiérrez, David
AU - Nehorai, Arye
AU - Dogandžić, Aleksandar
N1 - Funding Information:
Manuscript received January 30, 2005; revised October 1, 2005. The work of D. Gutiérrez was supported in part by the National Autonomous University of Mexico (UNAM) Postdoctoral Fellowship Program. This work was supported by the National Science Foundation (NSF) under Grant CCR-0105334 and Grant CCR-0330342 Asterisk indicates corresponding author.
PY - 2006/5
Y1 - 2006/5
N2 - 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.
AB - 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.
KW - Beamforming
KW - Dipole source signal
KW - Electroencephalography
KW - Low-rank covariance matrix
KW - Magnetoencephalography
KW - Sensor array processing
UR - https://www.scopus.com/pages/publications/33646363812
U2 - 10.1109/TBME.2005.863942
DO - 10.1109/TBME.2005.863942
M3 - Article
C2 - 16686406
AN - SCOPUS:33646363812
SN - 0018-9294
VL - 53
SP - 840
EP - 844
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 5
M1 - 1621135
ER -