title: BCI Competition 2003--Data set IIb: support vector machines for the P300 speller paradigm.
abstract: We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.
authors.affiliation: Faculty of Technology, Neuroinformatics Group, Bielefeld University, Bielefeld 33501, Germany. mkaper@techfak.uni-bielefeld.de
authors.forename: Matthias
authors.initials: M
authors.lastname: Kaper
authors.forename: Peter
authors.initials: P
authors.lastname: Meinicke
authors.forename: Ulf
authors.initials: U
authors.lastname: Grossekathoefer
authors.forename: Thomas
authors.initials: T
authors.lastname: Lingner
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D016208:Databases, Factual; D004569:Electroencephalography / Q000145:classification / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D015443:Word Processing