2 Stages-region-based P300 Speller in Brain–Computer Interface

Abstract

Brain–computer interface (BCI) applications present communication model without using peripheral nerves and neuromuscular systems. The P300 waves are used in BCI applications. Signal classification accuracy is a significant parameter for P300 BCI application. In this study, our goal is to investigate P300 speller structure for higher classification accuracy. There are a lot studies about P300 speller variations of stimulus model. This models mostly includes about row–column-based visual or audio stimuli model P300 spellers. But there is not enough study about region-based P300 spellers. This study contributes to region-based P300 spellers researches. Our new paradigm 2-stages region-based P300 speller has different amount of regions and stimulus position. During the experiment also, row–column-based P300 speller was used for comparing accuracy rates with our unique design 2-stages region-based P300 speller. The subject focused on the desired character stimulus. We used the stepwise linear discriminant analysis (SWLDA) method for classification that either included the desired P300 signal or not. According to SWLDA, the maximum mean classification accuracy value of the experiment was 83.33% with 2-stages region-based P300 speller. With this new paradigm, the classification accuracy was improved by 23.89% according to the most commonly used row–column-based P300 speller.