Paper Abstract and Keywords |
Presentation |
2011-03-08 15:25
Sequential Learning of EEG:SSVEP Four-class Classification:Sequential Monte Carlo Implemention Shu Shigezumi, Hideyuki Hara (Waseda Univ.), Atsushi Takemoto (Kyoto Univ.), Yumi Dobashi (Waseda Univ.), Katsuki Nakamura (Kyoto Univ.), Takashi Matsumoto (Waseda Univ.) MBE2010-125 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
An attempt was made to perform a sequential learning of SSVEP four class classification problem. As opposed to the batch learning algorithm, the sequential learning algorithm does not divide the data into training and test datasets; rather, it starts learning with the first single trial data and proceeds with the learning sequentially using the rest of the data. The algorithm was implemented by the Sequential Monte Carlo method. SSVEP data was acqired from 5 subjects. The algorithm appeared functional. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Brain-computer-Interface / Baysian Sequential Learning / Sequential Monte Carlo / electroencephalography / steady-state visual evoked potential(SSVEP) / multi-class classification / / |
Reference Info. |
IEICE Tech. Rep., vol. 110, no. 460, MBE2010-125, pp. 125-130, March 2011. |
Paper # |
MBE2010-125 |
Date of Issue |
2011-02-28 (MBE) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MBE2010-125 |