BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm
M Kaper, P Meinicke, U Grossekathoefer…�- IEEE Transactions�…, 2004 - ieeexplore.ieee.org
IEEE Transactions on biomedical Engineering, 2004•ieeexplore.ieee.org
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.
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.
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.
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