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)|
|Number of pages||4|
|Journal||Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings|
|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
- Source localization