MEG/EEG error bounds for a dipole source with a realistic head model

Magnetoencephalography (MEG) and electroencephalography (EEG) are non-invasive methods for studying human brain activity, with a time resolution of milliseconds. Current dipoles are the most common model describing sources of brain activity. We show how to compute error bounds when estimating the dipole source parameters for a realistic head model, using measurements of electric potential (EEG), magnetic field (MEG) and their combination. The electric potentials and magnetic field components are obtained by discretizing the integral equations for the fields via the boundary element method (BEM) and a weighted residuals technique. This process requires an accurate representation of the surfaces separating the irregular head layers, as obtained from MR or CT imaging.

[1]  M. Hämäläinen,et al.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data , 1989, IEEE Transactions on Biomedical Engineering.

[2]  J. D. Munck.  A linear discretization of the volume conductor boundary integral equation using analytically integrated elements (electrophysiology application) , 1992 .

[3]  C. Brebbia,et al.  Boundary Elements IX , 1987 .

[4]  Carlos Alberto Brebbia,et al.  Boundary Elements: An Introductory Course , 1989 .

[5]  B.N. Cuffin,et al.  EEG localization accuracy improvements using realistically shaped head models , 1996, IEEE Transactions on Biomedical Engineering.

[6]  A. S. Ferguson,et al.  A complete linear discretization for calculating the magnetic field using the boundary element method , 1994, IEEE Transactions on Biomedical Engineering.

[7]  Richard M. Leahy,et al.  Error bounds for MEG and EEG source localization , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[8]  M. Melamed.  Detection , 2021, SETI: Astronomy as a Contact Sport.

[9]  B. Radich,et al.  EEG dipole localization bounds and MAP algorithms for head models with parameter uncertainties , 1995, IEEE Transactions on Biomedical Engineering.

[10]  A. Nehorai,et al.  Magnetoencephalography with diversely oriented and multicomponent sensors , 1997, IEEE Transactions on Biomedical Engineering.

[11]  Petre Stoica,et al.  MUSIC, maximum likelihood and Cramer-Rao bound: further results and comparisons , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[12]  J.C. de Munck,et al.  A random dipole model for spontaneous brain activity , 1992, IEEE Transactions on Biomedical Engineering.

[13]  Roger Ghanem,et al.  Numerical solution of spectral stochastic finite element systems , 1996 .

[14]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[15]  Ted Belytschko,et al.  Element-free Galerkin method for wave propagation and dynamic fracture , 1995 .