Work

BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm

Year: 2004

Type: article

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.

Source: IEEE Transactions on Biomedical Engineering

DOI: 10.1109/tbme.2004.826698

Authors: M. Kaper, P. Meinicke, U. Grossekathoefer, T. Lingner, H. Ritter

Institutions: Bielefeld University, University of Göttingen

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W2100470503

FWCI: 8.409

Cites: 14

Cited by: 499

Related to: 10