title: [Bayesian classifier for brain-computer interface based on mental representation of movements].
abstract: This paper proposes Bayesian approach to classification of EEG patterns on the basis of imaginary movements of extremities based on analysis ofcovariance matrices of native EEG recordings. An efficacy of a Brain-Computer Interface (BCI) based on the proposed classifier is evaluated. Bayesian classifier is shown to be competitive with the MCSP (Multiclass Common Spatial Patterns) classifier known from the literature as one of the efficient variant for BCI implementation. The influence of eye movement and blinking artifacts on the BCI performance is investigated. It is shown that the presence of such artifacts does not affect the classification accuracy.
authors.forename: P D
authors.initials: PD
authors.lastname: Bobrov
authors.forename: A V
authors.initials: AV
authors.lastname: Korshakov
authors.forename: V Iu
authors.initials: VIu
authors.lastname: Roshchin
authors.forename: A A
authors.initials: AA
authors.lastname: Frolov
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000145:classification*; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D014584:User-Computer Interface; D014785:Vision, Ocular / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*