A feasibility study of EEG dipole source localization using particle swarm optimization

Interpretation of the clinical electroencephalographs (EEGs) almost always involves speculation as to the possible locations of the sources inside the brain that are responsible for the observed activity on the scalp. Dipoles are widely used to approximate the sources of electrical activity inside the brain. In this paper, we introduce a novel particle swarm optimization (PSO) algorithm to the EEG dipole source localization problem. A three-concentric-shell model is chosen as our head model, and the dipole number is restricted to 2. The 2 dipoles, each of which has 3 position elements, are combined and represented as a 6-element particle. Initialized by randomly setting the positions and velocities, the particle swarm evolves iteratively. Reported here are simulated cases to demonstrate the feasibility of the proposed PSO-based algorithm. Four groups of dipoles with different physiological meanings are chosen as the tested source models. Simulated cases with 10% noise level are also tested. The results show that PSO is feasible and efficient for the source localization in EEG. Furthermore, compared with the generally accepted genetic algorithm (GA), the PSO algorithm appears to be more accurate and needs less computation time