Electroencephalography (EEG) signal has been playing a crucial role in clinical diagnosis and treatment of neurological diseases. However, it is very challenging to efficiently reconstruct the brain image given sources from very few scalp measurements due to high ill-posedness. Recently some efforts have been devoted to developing EEG source reconstruction methods using various forms of regularization, including the $ell_1$-norm, the total variation (TV), as well as the fractional-order TV. However, since high-dimensional data are very large, these methods are difficult to implement. In this paper, we propose accelerated methods for EEG source imaging based on the TV regularization and its variants. Since the gradient/fractional-order gradient operators have coordinate friendly structures, we apply the Chambolle-Pock and ARock algorithms, along with diagonal preconditioning. In our algorithms, the coordinates of primal and dual variables are updated in an asynchronously parallel fashion. A variety of experiments show that the proposed algorithms have more rapid convergence than the state-of-the-art methods and have the potential to achieve the real-time temporal resolution.