Region-Based Current-Source Reconstruction for the Inverse EEG Problem

This paper presents a new method for the reconstruction of current sources for the electroencephalography (EEG) inverse problem, which produces reconstructed sources, which are confined to a few anatomical regions. The method is based on a partition of the gray matter into a set of regions, and in the construction of a simple linear model for the potential produced by feasible source configurations inside each one of these regions. The proposed method computes the solution in two stages: in the first one, a subset of active regions is found so that the combined potentials produced by sources inside them approximate the measured potential data. In the second stage, a detailed reconstruction of the current sources inside each active region is performed. Experimental results with synthetic data are presented, which show that the proposed scheme is fast, computationally efficient and robust to noise, producing results that are competitive with other published methods, especially when the current sources are effectively distributed in few anatomical regions. The proposed method is also validated with real data from an experiment with visual evoked potentials.

[1]  Carl D. Meyer,et al.  Matrix Analysis and Applied Linear Algebra , 2000 .

[2]  Bin He,et al.  Spatio-temporal EEG source localization using a three-dimensional subspace FINE approach in a realistic geometry inhomogeneous head model , 2006, IEEE Transactions on Biomedical Engineering.

[3]  Karl J. Friston,et al.  Anatomically Informed Basis Functions , 2000, NeuroImage.

[4]  L. Kaufman,et al.  Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation , 1992, IEEE Transactions on Biomedical Engineering.

[5]  E. Harth,et al.  Electric Fields of the Brain: The Neurophysics of Eeg , 2005 .

[6]  Nelson J. Trujillo-Barreto,et al.  Bayesian model averaging in EEG/MEG imaging , 2004, NeuroImage.

[7]  Olaf Hauk,et al.  Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data , 2004, NeuroImage.

[8]  Dezhong Yao,et al.  A Self-Coherence Enhancement Algorithm and its Application to Enhancing Three-Dimensional Source Estimation from EEGs , 2001, Annals of Biomedical Engineering.

[9]  Andreas Ziehe,et al.  Combining sparsity and rotational invariance in EEG/MEG source reconstruction , 2008, NeuroImage.

[10]  Dezhong Yao,et al.  Gaussian source model based iterative algorithm for EEG source imaging , 2009, Comput. Biol. Medicine.

[11]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[12]  P Golland,et al.  Multimodal functional imaging using fMRI-Informed Regional EEG/MEG Source Estimation , 2009, NeuroImage.

[13]  K. Matsuura,et al.  Selective minimum-norm solution of the biomagnetic inverse problem , 1995, IEEE Transactions on Biomedical Engineering.

[14]  R. Pascual-Marqui.  Review of methods for solving the EEG inverse problem , 1999 .

[15]  J. Maltez,et al.  Evaluation of L1 and L2 minimum norm performances on EEG localizations , 2004, Clinical Neurophysiology.

[16]  Z. Zhang,et al.  A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres. , 1995, Physics in medicine and biology.

[17]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[18]  Kevin Whittingstall,et al.  Correspondence of visual evoked potentials with FMRI signals in human visual cortex. , 2008, Brain topography.

[19]  Saeid Sanei,et al.  EEG Source Localization , 2013 .

[20]  Dezhong Yao,et al.  Lp Norm Iterative Sparse Solution for EEG Source Localization , 2007, IEEE Transactions on Biomedical Engineering.

[21]  Fusheng Yang,et al.  Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction , 2005, IEEE Transactions on Biomedical Engineering.

[22]  Karl J. Friston,et al.  Anatomically Informed Basis Functions for EEG Source Localization: Combining Functional and Anatomical Constraints , 2002, NeuroImage.

[23]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[25]  Jérémie Mattout,et al.  Localization Estimation Algorithm (LEA): A Supervised Prior-Based Approach for Solving the EEG/MEG Inverse Problem , 2003, Information Processing in Medical Imaging.

[26]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[27]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[28]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[29]  L. Garey.  Brodmann's localisation in the cerebral cortex , 1999 .

[30]  Karl J. Friston,et al.  Multiple sparse priors for the M/EEG inverse problem , 2008, NeuroImage.

[31]  Bin He,et al.  An alternative subspace approach to EEG dipole source localization , 2004 .

[32]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[33]  M. Fuchs,et al.  Linear and nonlinear current density reconstructions. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[34]  Manfred Fuchs,et al.  Evaluation of sLORETA in the Presence of Noise and Multiple Sources , 2003, Brain Topography.

[35]  Hesheng Liu,et al.  Efficient electromagnetic source imaging with adaptive standardized LORETA/FOCUSS , 2005, IEEE Transactions on Biomedical Engineering.

[36]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[37]  Dezhong Yao,et al.  Neuroelectric source imaging using 3SCO: A space coding algorithm based on particle swarm optimization and l 0 norm constraint , 2010, NeuroImage.

[38]  Fusheng Yang,et al.  A recursive algorithm for the three-dimensional imaging of brain electric activity: shrinking LORETA-FOCUSS , 2004, IEEE Transactions on Biomedical Engineering.

[39]  Matti Stenroos,et al.  A Matlab library for solving quasi-static volume conduction problems using the boundary element method , 2007, Comput. Methods Programs Biomed..

[40]  I. Jolliffe.  Principal Component Analysis , 2002 .

[41]  E. George.  The Variable Selection Problem , 2000 .

[42]  K. Matsuura,et al.  A robust reconstruction of sparse biomagnetic sources , 1997, IEEE Transactions on Biomedical Engineering.