Evoked potentials estimation in brain-computer interface using support vector machine

The single-trial Visual Evoked Potentials estimation of brain-computer interface was investigated. Communication carriers between brain and computer were induced by "imitating-human-natural-reading" paradigm. With carefully signal preprocess and feature selection procedure, we explored the single-trial estimation of EEG using v-support vector machines in six subjects, and by comparison the results using P300 features from channel Fz and Pz, gained a satisfied classification accuracy of 91.3%, 88.9%, 91.5%, 92.1%, 90.2% and 90.1% respectively. The result suggests that the experimental paradigm is feasible and the speed of our mental speller can be boosted.