Dynamic Stopping Improves the Speed and Accuracy of a P300 Speller

摘要

Brain Computer Interface spellers based on the P300 paradigm traditionally use a fixed number of epochs (stimulus presentations) to predict a letter. In this contribution, we introduce a dynamical adjustment of the number of epochs based on a threshold on the confidence of a probabilistic classifier. This allows the average required number of epochs to be lowered drastically. As such, using a conceptually simple modification with no impact on computational requirements, we obtain a P300 speller which is not only faster but also more accurate, which in turn increases the usability of the system substantially.