title: An application of Gaussian processes on ocular artifact removal from EEG.
abstract: Consequences of eye movements are one of the main interferences 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 recorded 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.
authors.forename: Saman
authors.initials: S
authors.lastname: Noorzadeh
authors.forename: Bertrand
authors.initials: B
authors.lastname: Rivet
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Gumery
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography; D005133:Eye Movements; D006801:Humans; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted