Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
A Brain-Computer Interface (BCI) is a system which could enable patients like those with Amyotrophic Lateral Sclerosis to control some equipment or 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 Reliability-Based Automatic Repeat reQuest (RB-ARQ), an error control method. 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, is applied to RB-ARQ. Also, a quantitative comparison among the error control methods has been done for the first time. The performance of BCIs have been further improved by RB-ARQ compared to the top ranked methods in the BCI competition with no significant difference between LDA and SVM.
