Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces

A brain-computer interface (BCI) is a system that could enable patients like those with amyotrophic lateral sclerosis to control some equipment and to communicate with other people, and has been anticipated to be achieved. One of the problems in BCI research is a trade-off between speed and accuracy, and it is practically important to adjust those two performance measures effectively. So far the authors have considered BCIs as communications between users and computers, and have proposed an error control method, reliability-based automatic repeat request (RB-ARQ). It has been shown that, with linear discriminant analysis (LDA) as a classifier, RB-ARQ is more effective than other error control methods. In this paper, support vector machines (SVMs), one of the most popular classifiers, are applied to RB-ARQ. A quantitative comparison showed that there was no significant difference between LDA and SVM. Also, it was demonstrated that RB-ARQ improved the accuracy from the one acquired by the top ranked methods in the BCI competition to 100 percents, with less loss of the speed.