A Bayesian model for solving the EEG source localization problem is proposed. The model promotes sparsity using a multivariate Bernoulli Laplacian prior. A Gibbs sampler is used to generate samples according to the posterior distribution. Metropolis–Hastings moves are used to improve the convergence rate of the sampler. The method is compared with synthetic and real data to state of the art algorithms.