Probabilistic reconstruction of multiple sources in the bioelectromagnetic inverse problem
Abstract
A probabilistic multiple source solution for the bioelectromagnetic inverse problem is described. The model-dependent solution assumes a finite number of discrete primary sources at fixed locations within a bounded conductor. Covariance statistics derived from a set of detectors outside the conducting region are used to determine a metric on the space of possible sources. This metric function is used to construct a weighted pseudo-inverse matrix, which, in turn, may be used to estimate the spatio-temporal distribution of source activity. The results are embodied in the form of the PROMS (probabilistic reconstruction of (multiple sources) algorithm. Computer simulations using the algorithm are described. These methods are compared with other algorithms, including minimum norm estimation, and the MUSIC and spatial filtering algorithms.
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Weighted minimum norm mitigates the superficial bias by weighting current sources by the norm of the EEG/MEG signals they can generate with a unitary magnitude.
“To mitigate this bias, one strategy is to weight current sources by the norm of the EEG/MEG signals that can be generated by each of them with a unitary magnitude. Introducing this weighting to MN regularization leads to the so-called weighted minimum norm (WMN) solution (24–27).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [26]
Cites
Multiple emitter location and signal parameter estimation
Schmidt, R. (1986) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.1986.1143830 · Cited by 14,718 · see citing works
Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem.
Sarvas, J. (1987) · Physics in Medicine and Biology · doi:10.1088/0031-9155/32/1/004 · Cited by 1,945 · see citing works
Multiple dipole modeling and localization from spatio-temporal MEG data
Mosher, J. C., Lewis, P. S., & Leahy, R. M. (1992) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.141192 · Cited by 1,048 · see citing works
Evoked dipole source potentials of the human auditory cortex
Scherg, M., & CRAMON, D. V. (1986) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(86)90014-6 · Cited by 703 · see citing works
Regression and the Moore-Penrose Pseudoinverse.
Troskie, C. G., & Albert, A. (1975) · Journal of the American Statistical Association · doi:10.2307/2285416 · Cited by 629 · see citing works
Magnetic field tomography of coherent thalamocortical 40-Hz oscillations in humans.
Ribary, U., Ioannides, A. A., Singh, K. D., Hasson, R., Bolton, J. P., Lado, F. A., Mogilner, A. Y., & Llinas, R. H. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.24.11037 · Cited by 541 · see citing works
Parker, R. L. (1977) · Annual Review of Earth and Planetary Sciences · doi:10.1146/annurev.ea.05.050177.000343 · Cited by 531 · see citing works
Continuous probabilistic solutions to the biomagnetic inverse problem
Ioannides, A. A., BOLTON, J. P. R., & Clarke, C. J. (1990) · Inverse Problems · doi:10.1088/0266-5611/6/4/005 · Cited by 296 · see citing works
Fourier space solution to the magnetostatic imaging problem
Dallas, W. J. (1985) · Applied Optics · doi:10.1364/ao.24.004543 · Cited by 51 · see citing works
Reconstruction of Images from Neuromagnetic Fields
Singh, M., Doria, D., Henderson, V. W., Huth, G. C., & Beatty, J. (1984) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1984.4333324 · Cited by 49 · see citing works
Probabilistic methods in a biomagnetic inverse problem
Clarke, C. J. (1989) · Inverse Problems · doi:10.1088/0266-5611/5/6/008 · Cited by 36 · see citing works
Magnetic fields produced by models of biological current sources
Cuffin, B. N., & Cohen, D. (1977) · Journal of Applied Physics · doi:10.1063/1.324230 · Cited by 30 · see citing works
Minimum Norm Estimation of Current Distributions in Realistic Geometries
Crowley, C. W., Greenblatt, R. E., & Khalil, I. (1989) · Springer eBooks · doi:10.1007/978-1-4613-0581-1_133 · Cited by 18 · see citing works
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Electrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297 · see citing works
(2019) · Handbook of clinical neurology · doi:10.1016/b978-0-444-64032-1.00006-0 · Cited by 134 · see citing works
- Authors
- Greenblatt, R. E.
- Affiliations
- Source Signal Imaging (US)
- Corresponding author
- Greenblatt, R E
- Venue
- Inverse Problems
- Publisher
- IOP Publishing
- Published
- 1993-04-01
- Volume
- 9
- Issue
- 2
- Pages
- 271-284
- ISSN
- 0266-5611 (ISSN-L)
- 1361-6420
- OpenAlex
- W2054322658
- Cited by
- 48
- References
- 13
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 1 · 2019: 2 · 2018: 1 · 2017: 1 · 2012: 1
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Inverse Problems (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Physical Sciences › Engineering › Biomedical Engineering › Microwave Imaging and Scattering Analysis
- Keywords
- minimum norm estimation, MUSIC algorithm, spatial filtering
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