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

Volume

51

Issue

6

fYear

2004

fDate

6/1/2004 12:00:00 AM

Firstpage

1073

Lastpage

1076

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;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2004.826698

Filename

1300805