A Study on Combination of Reliability-Based Automatic Repeat Request with Error Potential-Based Error Correction for Improving P300 Speller Performance

摘要

Brain-computer interfaces (BCIs) are systems that translate one's thoughts into commands to restore control and communication to severely paralyzed people, and are also appealing to healthy people. The P300 speller, one of the best-known BCIs for communication, allows users to select letters just by thoughts. However, due to the low signal-to-noise ratio of the P300, signal averaging is often performed, which improves the spelling accuracy but degrades the spelling speed. The authors have proposed reliability-based automatic repeat request (RB-ARQ) to ease this problem. RB-ARQ could be enhanced when it is combined with error correction based on the error-related potentials (ErrPs) that occur on erroneous feedbacks. Thus, this study aims to reveal the characteristics of the ErrPs in the P300 speller paradigm, and to combine RB-ARQ with ErrP-based error correction to further improve performance. The results show that the ErrPs observed in the current study resemble the previously reported ErrPs observed in a cursor control task using a BCI, and that the performance of the P300 speller could be improved by 35% on average.