On the performance of metahuristic algorithms in the solution of the EEG inverse problem

The problem of electroencephalographic (EEG) source localization involves an optimization problem that can be solved through global optimization methods. In this paper, we evaluate the performance in localizing EEG sources of simulated annealing (SA) and genetic algorithm (GA) as a function of the optimization's initialization parameters and the signal-to-noise ratio (SNR). We use the concentrated likelihood function (CLF) as objective function and the Cramér-Rao bound (CRB) as a reference on the performance. The CRB sets the lower limit on the variance of our estimated values. Then, through simulations on realistic EEG data we show that both SA and GA are highly sensitive to noise, but adjustments on their parameters for a fixed SNR value do not improve performance significantly. However SA is more sensitive to noise and its performance may be affected by correlated sources. Our results also confirm that in both algorithms the mean square error (MSE) in the location EEG sources is minimum.