Application of reliability-based automatic repeat request to multi-class classification for brain-computer interfaces

A brain-computer interface (BCI) is a system which 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 transmission speed and accuracy. In the field of data transmission, on the other hand, reliability-based hybrid automatic repeat request (RB-HARQ), one of the error control methods, has been developed to achieve both of the performances. The authors, therefore, have considered BCIs as communications between users and computers, and applied reliability-based ARQ, customized RB-HARQ, to BCIs. It has been shown that the proposed method is superior to other error control methods in two-class classification. In this paper, the proposed method is extended to deal with multi-class classification of EEG data, and is shown to be effective in multi-class problems.