title: Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.
abstract: The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.
authors.forename: L
authors.initials: L
authors.lastname: Korczowski
authors.forename: M
authors.initials: M
authors.lastname: Congedo
authors.forename: C
authors.initials: C
authors.lastname: Jutten
mesh_terms: D000293:Adolescent; D000328:Adult; D062207:Brain-Computer Interfaces; D016002:Discriminant Analysis; D004569:Electroencephalography; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D010865:Pilot Projects; D055815:Young Adult