Study of P300 Detection Performance by Different P300 Speller Approaches Using Electroencephalography

Abstract

Brain-Computer Interfaces (BCIs) are bidirectional devices that have allowed people to control computers or external devices through their brain activity. The P300 Speller is one of the most widely used BCI applications, where subjects can transmit textual information mentally with satisfactory performance. However, the P300 Speller still has room for improvement in practical use, such as selecting the best balance between accuracy and speed. Based on a lack of literature in this direction, this study evaluates two distinct approaches to the P300 Speller. The first is based on rows and columns following the traditional implementation, while the second is based on regions, employing subsets of characters during spelling. In both approaches, the effects of two different stimulus presentation parameters (the number of repetitions per stimulus and the interval between them) on the accuracy and performance efficiency of the P300 Speller are studied. The results show that both approaches obtain similar values in terms of detection performance, obtaining around 75% F1-score for predicting a character with four series of 12 blinks per character. In addition, the region-based approach presents a more robust scheme for false predictions, maintaining a similar spelling duration. The theoretical study performed indicates that spelling a character requires around one minute.