Literature DB >> 7685264

Error bounds for EEG and MEG dipole source localization.

J C Mosher1, M E Spencer, R M Leahy, P S Lewis.   

Abstract

General formulas are presented for computing a lower bound on localization and moment error for electroencephalographic (EEG) or magnetoencephalographic (MEG) current source dipole models with arbitrary sensor array geometry. Specific EEG and MEG formulas are presented for multiple dipoles in a head model with 4 spherical shells. Localization error bounds are presented for both EEG and MEG for several different sensor configurations. Graphical error contours are presented for 127 sensors covering the upper hemisphere, for both 37 sensors and 127 sensors covering a smaller region, and for the standard 10-20 EEG sensor arrangement. Both 1- and 2-dipole cases were examined for all possible dipole orientations and locations within a head quadrant. The results show a strong dependence on absolute dipole location and orientation. The results also show that fusion of the EEG and MEG measurements into a combined model reduces the lower bound. A Monte Carlo simulation was performed to check the tightness of the bounds for a selected case. The simple head model, the low power noise and the few strong dipoles were all selected in this study as optimistic conditions to establish possibly fundamental resolution limits for any localization effort. Results, under these favorable assumptions, show comparable resolutions between the EEG and the MEG models, but accuracy for a single dipole, in either case, appears limited to several millimeters for a single time slice. The lower bounds increase markedly with just 2 dipoles. Observations are given to support the need for full spatiotemporal modeling to improve these lower bounds. All of the simulation results presented can easily be scaled to other instances of noise power and dipole intensity.

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Year:  1993        PMID: 7685264     DOI: 10.1016/0013-4694(93)90043-u

Source DB:  PubMed          Journal:  Electroencephalogr Clin Neurophysiol        ISSN: 0013-4694


  32 in total

1.  Monte Carlo simulation studies of EEG and MEG localization accuracy.

Authors:  Arthur K Liu; Anders M Dale; John W Belliveau
Journal:  Hum Brain Mapp       Date:  2002-05       Impact factor: 5.038

Review 2.  Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks.

Authors:  P L Nunez; B M Wingeier; R B Silberstein
Journal:  Hum Brain Mapp       Date:  2001-07       Impact factor: 5.038

3.  Some limitations of spatio temporal source models.

Authors:  D Cabrera Fernández; R Grave de Peralta Menéndez; S L González Andino
Journal:  Brain Topogr       Date:  1995       Impact factor: 3.020

4.  Comparison between electrocardiographic and magnetocardiographic inverse solutions using the boundary element method.

Authors:  R Hren; X Zhang; G Stroink
Journal:  Med Biol Eng Comput       Date:  1996-03       Impact factor: 2.602

5.  Exactness of source analysis of biomagnetic signals of epileptiform spikes by the method of spatial filtering: a computer simulation.

Authors:  H Wagner; M Eiselt; U Zwiener
Journal:  Med Biol Eng Comput       Date:  1997-11       Impact factor: 2.602

6.  ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.

Authors:  Taha Erenler; Yesim Serinagaoglu Dogrusoz
Journal:  Med Biol Eng Comput       Date:  2019-07-30       Impact factor: 2.602

7.  Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging.

Authors:  Thinh Nguyen; Thomas Potter; Christof Karmonik; Robert Grossman; Yingchun Zhang
Journal:  J Vis Exp       Date:  2018-06-30       Impact factor: 1.355

8.  Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study.

Authors:  F Quandt; C Reichert; H Hinrichs; H J Heinze; R T Knight; J W Rieger
Journal:  Neuroimage       Date:  2011-11-30       Impact factor: 6.556

9.  Direction of magnetoencephalography sources associated with feedback and feedforward contributions in a visual object recognition task.

Authors:  Seppo P Ahlfors; Stephanie R Jones; Jyrki Ahveninen; Matti S Hämäläinen; John W Belliveau; Moshe Bar
Journal:  Neurosci Lett       Date:  2014-11-20       Impact factor: 3.046

10.  The folding fingerprint of visual cortex reveals the timing of human V1 and V2.

Authors:  Justin Ales; Thom Carney; Stanley A Klein
Journal:  Neuroimage       Date:  2009-09-22       Impact factor: 6.556

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