BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller

By Alain Rakotomamonjy, Vincent Guigue. IEEE Transactions on Biomedical Engineering, 2008

535 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cd7d1f2568b9064800547f664d02c6e20f80d483

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Citations in the corpus

Brain Computer Interfaces, a Review (2012, methodology): “ensure that the number of repetitions remain unaffected [130,131].”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, methodology): “Its accuracy is equivalent to the best current method on the Data set II of the third BCI competition [16].”
$\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning (2011, methodology): “Note that using a small part of the examples for training is motivated by the use of ensemble of SVM (which we do not consider here) [ 42 ] at a later stage of the EEG classification procedure.”
Single-trial analysis and classification of ERP components - A tutorial (2011, unknown): “Among the non-linear methods the support vector machine is the most powerful method applied to ERP classification (Müller et al., 2001; Meinicke et al., 2003; Rakotomamonjy and Guigue, 2008).”
xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface (2009, methodology): “For instance, Rakotomamonjy and Guigue [11] used as a classifier an ensemble of several linear support”
(C)overt attention and visual speller design in an ERP-based brain-computer interface (2010, background): “In the past decades, classification techniques improved [8-11] and practical communication rates including feedback of 5.82 symbols/min have been reported [12], but this information throughput is still not competitive when compared to conventional communication means such as speech, typing, or…”