Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Computer Vision and Image Understanding
P300 wave detection based on rough sets
Transactions on Rough Sets V
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Most of the previous work on non-invasive brain-computer interfaces (BCIs) has been focused on feature extraction and classification algorithms to achieve high performance for the communication between the brain and the computer. While significant progress has been made in the lower layer of the BCI system, the issues in the higher layer have not been sufficiently addressed. Existing P300-based BCI systems, for example the P300 speller, use a random order of stimulus sequence for eliciting P300 signal for identifying users' intentions. This paper is about computing an optimal sequence of stimulus in order to minimize the number of stimuli, hence improving the performance. To accomplish this, we model the problem as a partially observable Markov decision process (POMDP), which is a model for planning in partially observable stochastic environments. Through simulation and human subject experiments, we show that our approach achieves a significant performance improvement in terms of the success rate and the bit rate.