A POMDP Approach to P 300 Brain-Computer Interfaces *
A POMDP Approach to P 300 Brain-Computer Interfaces *
复制标题
P 300 脑机接口的 POMDP 方法 *
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Sungho Jo
中科院分区：
文献类型：
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作者：
Jaeyoung Park;Kee;Sungho Jo
以往基于P300的脑机接口(BCI)的研究主要集中在特征提取和分类算法上，以实现人脑和计算机之间的高性能通信。虽然在BCI系统的这一较低层取得了重大进展，但较高层的问题还没有得到充分解决。现有的基于P300的脑机接口系统使用随机顺序的刺激序列来产生P300信号来识别用户的意图。本文研究的是如何计算一个最优的激励序列，从而使激励的个数最少，从而提高性能。为了实现这一目标，我们将问题建模为具有观测延迟的部分可观测马尔可夫决策过程(POMDP)。通过仿真和人体实验，我们的方法在成功率和比特率方面都取得了显著的性能提高。
Most of the previous work on brain-computer interfaces (BCIs) using P300 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 such lower layer of the BCI system, the issues in the higher layer have not been addressed sufficiently. Existing P300-based BCI systems use a random order of stimulus sequence for eliciting P300 signal for identifying users‟ intentions. This paper is about computing an optimal sequence of stimuli in order to minimize the number of stimuli, hence improving the performance. To accomplish this objective, we model the problem as a partially observable Markov decision process (POMDP) with observation delays. 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.