A POMDP Approach to Optimizing P300 Speller BCI Paradigm

To achieve high performance in brain-computer interfaces (BCIs) using P300, most of the work has been focused on feature extraction and classification algorithms. Although significant progress has been made in such signal processing methods in the lower layer, the issues in the higher layer, specifically determining the stimulus schedule in order to identify the target reliably and efficiently, remain relatively unexplored. In this paper, we propose a systematic approach to compute an optimal stimulus schedule in P300 BCIs. Our approach adopts the partially observable Markov decision process, which is a model for planning in partially observable stochastic environments. We show that the thus obtained stimulus schedule achieves a significant performance improvement in terms of the success rate, bit rate, and practical bit rate through human subject experiments.