Spatio-temporal EEG dipole estimation by means of a hybrid genetic algorithm

Ling Zou; Shanan Zhu; Bin He

Spatio-temporal EEG dipole estimation by means of a hybrid genetic algorithm

Ling Zou, Shanan Zhu, Bin He

Biomedical Engineering

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

EEG source localization can be considered as a nonlinear optimization process. In the present study, a hybrid genetic algorithm (HGA) is introduced, which combines genetic and local search strategies to overcome the disadvantages of conventional genetic algorithm and local optimization methods. This HGA algorithm was used to localize two dipoles from scalp EEG, and yielded localization accuracy range of 0.95cm-1.55cm when the noise level is within 15%, which is better than the Simplex and GA algorithms in localizing multiple dipoles.

Original language	English (US)
Pages (from-to)	4436-4439
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	26 VI
State	Published - 2004
Event	Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States Duration: Sep 1 2004 → Sep 5 2004

Keywords

EEG
GA
HGA
Source localization

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title = "Spatio-temporal EEG dipole estimation by means of a hybrid genetic algorithm",

abstract = "EEG source localization can be considered as a nonlinear optimization process. In the present study, a hybrid genetic algorithm (HGA) is introduced, which combines genetic and local search strategies to overcome the disadvantages of conventional genetic algorithm and local optimization methods. This HGA algorithm was used to localize two dipoles from scalp EEG, and yielded localization accuracy range of 0.95cm-1.55cm when the noise level is within 15%, which is better than the Simplex and GA algorithms in localizing multiple dipoles.",

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note = "Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 ; Conference date: 01-09-2004 Through 05-09-2004",

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AU - Zhu, Shanan

AU - He, Bin

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AB - EEG source localization can be considered as a nonlinear optimization process. In the present study, a hybrid genetic algorithm (HGA) is introduced, which combines genetic and local search strategies to overcome the disadvantages of conventional genetic algorithm and local optimization methods. This HGA algorithm was used to localize two dipoles from scalp EEG, and yielded localization accuracy range of 0.95cm-1.55cm when the noise level is within 15%, which is better than the Simplex and GA algorithms in localizing multiple dipoles.

KW - EEG

KW - GA

KW - HGA

KW - Source localization

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M3 - Conference article

AN - SCOPUS:11144312368

SN - 0589-1019

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JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004

Y2 - 1 September 2004 through 5 September 2004

ER -

Spatio-temporal EEG dipole estimation by means of a hybrid genetic algorithm

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Keywords

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