TY - JOUR
T1 - Optimising experimental design for MEG beamformer imaging
AU - Brookes, Matthew J
AU - Vrba, Jiri
AU - Robinson, Stephen E
AU - Stevenson, Claire M
AU - Peters, Andrew M
AU - Barnes, Gareth R
AU - Hillebrand, Arjan
AU - Morris, Peter G
PY - 2008/2/15
Y1 - 2008/2/15
N2 - In recent years, the use of beamformers for source localisation has significantly improved the spatial accuracy of magnetoencephalography. In this paper, we examine techniques by which to optimise experimental design, and ensure that the application of beamformers yields accurate results. We show that variation in the experimental duration, or variation in the bandwidth of a signal of interest, can significantly affect the accuracy of a beamformer reconstruction of source power. Specifically, power will usually be underestimated if covariance windows are made too short, or bandwidths too narrow. The accuracy of spatial localisation may also be reduced. We conclude that for optimum accuracy, experimenters should aim to collect as much data as possible, and use a bandwidth spanning the entire frequency distribution of the signal of interest. This minimises distortion to reconstructed source images, time courses and power estimation. In the case where experimental duration is short, and small covariance windows are therefore used, we show that accurate power estimation can be achieved by matrix regularisation. However, large amounts of regularisation cause a loss in the spatial resolution of the MEG beamformer, hence regularisation should be used carefully, particularly if multiple sources in close proximity are expected.
AB - In recent years, the use of beamformers for source localisation has significantly improved the spatial accuracy of magnetoencephalography. In this paper, we examine techniques by which to optimise experimental design, and ensure that the application of beamformers yields accurate results. We show that variation in the experimental duration, or variation in the bandwidth of a signal of interest, can significantly affect the accuracy of a beamformer reconstruction of source power. Specifically, power will usually be underestimated if covariance windows are made too short, or bandwidths too narrow. The accuracy of spatial localisation may also be reduced. We conclude that for optimum accuracy, experimenters should aim to collect as much data as possible, and use a bandwidth spanning the entire frequency distribution of the signal of interest. This minimises distortion to reconstructed source images, time courses and power estimation. In the case where experimental duration is short, and small covariance windows are therefore used, we show that accurate power estimation can be achieved by matrix regularisation. However, large amounts of regularisation cause a loss in the spatial resolution of the MEG beamformer, hence regularisation should be used carefully, particularly if multiple sources in close proximity are expected.
KW - Algorithms
KW - Brain/anatomy & histology
KW - Brain Mapping/methods
KW - Computer Simulation
KW - Electroencephalography
KW - Humans
KW - Image Processing, Computer-Assisted/methods
KW - Magnetoencephalography/methods
KW - Reproducibility of Results
U2 - 10.1016/j.neuroimage.2007.09.050
DO - 10.1016/j.neuroimage.2007.09.050
M3 - Article
C2 - 18155612
VL - 39
SP - 1788
EP - 1802
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - 4
ER -