Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials
Journal article, 2014

One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert independently identified the expected source location, further corroborating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations.

RECONSTRUCTION

Electroencephalogram (EEG) source localization

ELECTRICAL-STIMULATION

HEAD MODELS

DIPOLE LOCALIZATION

inverse problem

CORTEX

MEDIAN NERVE-STIMULATION

FUNCTIONAL MRI

finite element method (FEM)

FINITE-ELEMENT-METHOD

Author

Yazdan Shirvany

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Mahmood Qaiser

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Johan Carlson

Fraunhofer-Chalmers Centre

Stefan Jakobsson

Fraunhofer-Chalmers Centre

Anders Hedström

Sahlgrenska Academy

Mikael Persson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Neural Systems and Rehabilitation Engineering

1534-4320 (ISSN)

Vol. 22 1 11-20 13

Subject Categories

Medical Engineering

DOI

10.1109/TNSRE.2013.2281435

More information

Latest update

3/23/2018