title: Faster P300 Classifier Training Using Spatiotemporal Beamforming.
abstract: The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.
authors.affiliation: 1 Laboratory for Neuro-and Psychophysiology, K. U. Leuven, Herestraat 49, Leuven, B-3000, Belgium.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Wittevrongel
authors.affiliation: 1 Laboratory for Neuro-and Psychophysiology, K. U. Leuven, Herestraat 49, Leuven, B-3000, Belgium.
authors.forename: Marc M
authors.initials: MM
authors.lastname: Van Hulle
mesh_terms: D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D009483:Neuropsychological Tests; D060388:Support Vector Machine; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology; D055815:Young Adult
keywords: BCI; EEG; LCMV; P300; beamforming; event-related potential; spatiotemporal filter