Title: A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data
Authors: Crevecoeur, Guillaume ×
Hallez, Hans
Van Hese, Peter
D'Asseler, Yves
Dupre, Luc
Van De Walle, Rik #
Issue Date: 2008
Publisher: P. Peregrinus Ltd.
Series Title: Medical and Biological Engineering and Computing vol:46 issue:8 pages:767-777
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 noninvasive
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.
ISSN: 0140-0118
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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