Brain-Computer Interfaces (BCIs) are systems that translate one's thoughts into commands to restore control and communication to severely paralyzed people, and they are also appealing to healthy people. One of the challenges is to improve the performance of BCIs, often measured by the accuracy and the trial duration, or the information transfer rate (ITR), i.e., the mutual information per unit time. Since BCIs are communications between a user and a system, error control schemes such as forward error correction and automatic repeat request (ARQ) can be applied to BCIs to improve the accuracy. This paper presents reliability-based ARQ (RB-ARQ), a variation of ARQ designed for BCIs, which employs the maximum posterior probability for the repeat decision. The current results show that RB-ARQ is more effective than the conventional methods, i.e., better accuracy when trial duration was the same, and shorter trial duration when the accuracy was the same. This resulted in a greater information transfer rate and a greater utility, which is a more practical performance measure in the P300 speller task. The results also show that such users who achieve a poor accuracy for some reason can benefit the most from RB-ARQ, which could make BCIs more universal.