Consequences of eye movements are one of the main inferences that distort the brain EEG recordings. In this paper, a multi-modal approach is used to estimate the ocular artifacts in the EEG: both vertical and horizontal eye movement signals recoded by an eye tracker are used as a reference to denoise the EEG. A Gaussian process, i.e. a second order statistics method, is assumed to model the link between the eye tracker signals and the EEG signals. The proposed method is thus a non-linear extension of the well-known adaptive filtering and can be applied with a single EEG signal contrary to independent component analysis (ICA) which is extensively used. The results show the applicability and the efficiency of this model on the ocular artifact removal.