The magnetic fields generated by eye movements are major artifacts in MEG measurements. We propose a hybrid hierarchical variational Bayesian method to remove eye movement artifacts from MEG data. Our method is an extension of the hierarchical variational Bayesian method for MEG source localization proposed by Sato et al. [Sato, M., Yoshioka, T., Kajihara, S., Toyama, K., Goda, N., Doya, K., and Kawato, M., (2004). Hierarchical Bayesian estimation for MEG inverse problem. NeuroImage 23(3), 806-826]. First, we assumed a single dipole at each left and right eyeball as a source of eye artifacts. Second, we constructed an EOG forward model describing the relationship between eye dipoles and electric potentials, i.e., EOG. Based on the Bayesian framework, the proposed method concurrently estimates eye and brain current sources from both MEG and EOG data. Thereby the brain current sources can be isolated from eye artifacts. The new method was tested in two ways. In the simulation experiments, the performance of eye artifact removal was evaluated from various aspects; locations of brain current sources, temporal correlation between eye and brain current sources, the level of MEG observation noise and so on. In real MEG experiments, we measured MEG and EOG data during smooth pursuit eye movements for a horizontally or circularly moving target. Our method successfully removed eye artifacts from the simulated and real MEG data with the estimation of brain current sources that were located in eye movement related areas. Our method should be widely applicable to MEG data obtained in tasks with non-negligible eye movements.