Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller
Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller
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DOI：
10.1088/1741-2560/7/5/056006
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发表时间：
2010-10-01
影响因子：
4
通讯作者：
Cinel, Caterina
中科院分区：
文献类型：
--
作者：
Citi, Luca;Poli, Riccardo;Cinel, Caterina
P300是一种内源性事件相关电位（ERP），由罕见且显著的外部刺激自然引发。p300在脑机接口（bci）中的应用越来越频繁，因为基于erp的bci用户不需要特殊的培训。然而，P300波很难被检测到，因此，在接口做出可靠的决定之前，需要多个目标刺激的呈现。虽然P300波的检测已经有了很大的改进，但没有特别注意脑机接口中P300波的形状和时间的可变性。在本文中，我们通过记录、建模和利用p300振幅的调制来填补这一空白，该调制与东汉语拼写器中目标前面的非目标数量有关。我们方法的基本思想是使用分类器在多个刺激呈现过程中产生的反应的适当加权平均，而不是传统的平均。这使得对可能提供更多信息的事件进行更重的加权成为可能，从而提高了分类的准确性。最优的权重是通过一个数学模型来确定的，该模型精确地估计了我们的拼写器的准确性以及相对于传统方法的预期性能改进。用两个独立数据集进行的测试表明，我们的方法比迄今为止文献中表现最好的算法在准确性方面提供了显著的统计显著改进。我们提出的方法和理论模型是通用的，可以很容易地在其他基于p300的bci中使用，并且变化很小。
The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain-computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller. The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top-performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.