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2010
ACM

A POMDP approach to P300-based brain-computer interfaces

9 years 9 months ago
A POMDP approach to P300-based brain-computer interfaces
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 th...
Jaeyoung Park, Kee-Eung Kim, Sungho Jo
Added 17 Mar 2010
Updated 17 Mar 2010
Type Conference
Year 2010
Where IUI
Authors Jaeyoung Park, Kee-Eung Kim, Sungho Jo
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