Advanced search
Add to list

A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data

Guillaume Crevecoeur (UGent) , Hans Hallez (UGent) , Peter Van Hese (UGent) , Yves D'Asseler (UGent) , Luc Dupré (UGent) and Rik Van de Walle (UGent)
Author
Organization
Abstract
Epilepsy is a neurological disorder caused by intense electrical activity in the brain. The electrical activity, which can be modelled through the superposition of several electrical dipoles, can be determined in a non-invasive way by analysing the electro-encephalogram. This source localization requires the solution of an inverse problem. Locally convergent optimization algorithms may be trapped in local solutions and when using global optimization techniques, the computational effort can become expensive. Fast recovery of the electrical sources becomes difficult that way. Therefore, there is a need to solve the inverse problem in an accurate and fast way. This paper performs the localization of multiple dipoles using a global-local hybrid algorithm. Global convergence is guaranteed by using space mapping techniques and independent component analysis in a computationally efficient way. The accuracy is locally obtained by using the Recursively Applied and Projected-MUltiple Signal Classification (RAP-MUSIC) algorithm. When using this hybrid algorithm, a four times faster solution is obtained.
Keywords
RECIPROCITY, CRITERIA, OPTIMIZATION, BRAIN, RAP-MUSIC, SOURCE LOCALIZATION, SPACE MAPPING TECHNIQUES, signal classification, inverse problems optimization, DIPOLES, NUMBER, EEG source analysis, space mapping

Citation

Please use this url to cite or link to this publication:

MLA
Crevecoeur, Guillaume, Hans Hallez, Peter Van Hese, et al. “A Hybrid Algorithm for Solving the EEG Inverse Problem from Spatio-temporal EEG Data.” MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 46.8 (2008): 767–777. Print.
APA
Crevecoeur, G., Hallez, H., Van Hese, P., D’Asseler, Y., Dupré, L., & Van de Walle, R. (2008). A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 46(8), 767–777.
Chicago author-date
Crevecoeur, Guillaume, Hans Hallez, Peter Van Hese, Yves D’Asseler, Luc Dupré, and Rik Van de Walle. 2008. “A Hybrid Algorithm for Solving the EEG Inverse Problem from Spatio-temporal EEG Data.” Medical & Biological Engineering & Computing 46 (8): 767–777.
Chicago author-date (all authors)
Crevecoeur, Guillaume, Hans Hallez, Peter Van Hese, Yves D’Asseler, Luc Dupré, and Rik Van de Walle. 2008. “A Hybrid Algorithm for Solving the EEG Inverse Problem from Spatio-temporal EEG Data.” Medical & Biological Engineering & Computing 46 (8): 767–777.
Vancouver
1.
Crevecoeur G, Hallez H, Van Hese P, D’Asseler Y, Dupré L, Van de Walle R. A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING. 2008;46(8):767–77.
IEEE
[1]
G. Crevecoeur, H. Hallez, P. Van Hese, Y. D’Asseler, L. Dupré, and R. Van de Walle, “A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data,” MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol. 46, no. 8, pp. 767–777, 2008.
@article{429045,
  abstract     = {Epilepsy is a neurological disorder caused by intense electrical activity in the brain. The electrical activity, which can be modelled through the superposition of several electrical dipoles, can be determined in a non-invasive way by analysing the electro-encephalogram. This source localization requires the solution of an inverse problem. Locally convergent optimization algorithms may be trapped in local solutions and when using global optimization techniques, the computational effort can become expensive. Fast recovery of the electrical sources becomes difficult that way. Therefore, there is a need to solve the inverse problem in an accurate and fast way. This paper performs the localization of multiple dipoles using a global-local hybrid algorithm. Global convergence is guaranteed by using space mapping techniques and independent component analysis in a computationally efficient way. The accuracy is locally obtained by using the Recursively Applied and Projected-MUltiple Signal Classification (RAP-MUSIC) algorithm. When using this hybrid algorithm, a four times faster solution is obtained.},
  author       = {Crevecoeur, Guillaume and Hallez, Hans and Van Hese, Peter and D'Asseler, Yves and Dupré, Luc and Van de Walle, Rik},
  issn         = {0140-0118},
  journal      = {MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING},
  keywords     = {RECIPROCITY,CRITERIA,OPTIMIZATION,BRAIN,RAP-MUSIC,SOURCE LOCALIZATION,SPACE MAPPING TECHNIQUES,signal classification,inverse problems optimization,DIPOLES,NUMBER,EEG source analysis,space mapping},
  language     = {eng},
  number       = {8},
  pages        = {767--777},
  title        = {A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data},
  url          = {http://dx.doi.org/10.1007/s11517-008-0341-z},
  volume       = {46},
  year         = {2008},
}

Altmetric
View in Altmetric
Web of Science
Times cited: