BCI Competition 2003--Data set IIb: support vector machines for the P300 speller paradigm.
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.
Kaper
Matthias
M
Faculty of Technology, Neuroinformatics Group, Bielefeld University, Bielefeld 33501, Germany. mkaper@techfak.uni-bielefeld.de
Meinicke
Peter
P
Grossekathoefer
Ulf
U
Lingner
Thomas
T
Ritter
Helge
H
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