Dipole Source Reconstruction of Brain Signals by Using Particle Swarm Optimization

Resolving the sources of neural activity is of prime importance in the analysis of Event Related Potentials (ERP). These sources can be modeled as effective dipoles. Identifying the dipole parameters from the measured multichannel data is called the EEG inverse problem. In this work, we propose a new method for the solution of EEG inverse problem. Our method uses Particle Swarm Optimization (PSO) technique for optimally choosing the dipole parameters. Simulations on synthetic data sets show, that our method well localizes the dipoles into their actual locations. In the real data sets, since the actual dipole parameters aren't known, the fit error between the measured data and the reconstructed data is minimized. It has been observed that our method reduces this error to the noise level by localizing only a few dipoles in the brain.