Title :
BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm
Author :
Kaper, Matthias ; Meinicke, Peter ; Grossekathoefer, Ulf ; Lingner, Thomas ; Ritter, Helge
Author_Institution :
Fac. of Technol., Bielefeld Univ., Germany
fDate :
6/1/2004 12:00:00 AM
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.
Keywords :
biomedical electrodes; electroencephalography; handicapped aids; medical signal processing; signal classification; support vector machines; BCI Competition 2003; P300 speller paradigm; electrode positions; machine-learning technique; signal classification; support vector machines; Brain computer interfaces; Councils; Data analysis; Electrodes; Electroencephalography; Event detection; Pattern recognition; Support vector machine classification; Support vector machines; Testing; Algorithms; Artificial Intelligence; Brain; Cognition; Databases, Factual; Electroencephalography; Event-Related Potentials, P300; Humans; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; User-Computer Interface; Word Processing;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2004.826698
