Pinegger Andreas, Faller Josef, Halder Sebastian, Wriessnegger Selina C, Müller-Putz Gernot R
Institute for Knowledge Discovery, Graz University of Technology, BioMedTech-Graz, Graz, Austria.
J Neural Eng. 2015 Feb;12(1):014001. doi: 10.1088/1741-2560/12/1/014001. Epub 2015 Jan 14.
Brain-computer interfaces (BCI) based on event-related potentials (ERP) were proven to be a reliable synchronous communication method. For everyday life situations, however, this synchronous mode is impractical because the system will deliver a selection even if the user is not paying attention to the stimulation. So far, research into attention-aware visual ERP-BCIs (i.e., asynchronous ERP-BCIs) has led to variable success. In this study, we investigate new approaches for detection of user engagement.
Classifier output and frequency-domain features of electroencephalogram signals as well as the hybridization of them were used to detect the user's state. We tested their capabilities for state detection in different control scenarios on offline data from 21 healthy volunteers.
The hybridization of classifier output and frequency-domain features outperformed the results of the single methods, and allowed building an asynchronous P300-based BCI with an average correct state detection accuracy of more than 95%.
Our results show that all introduced approaches for state detection in an asynchronous P300-based BCI can effectively avoid involuntary selections, and that the hybrid method is the most effective approach.
基于事件相关电位（ERP）的脑机接口（BCI）已被证明是一种可靠的同步通信方法。然而，在日常生活场景中，这种同步模式并不实用，因为即使用户没有注意到刺激，系统也会做出选择。到目前为止，对注意力感知视觉ERP-BCI（即异步ERP-BCI）的研究取得了不同程度的成功。在本研究中，我们探究检测用户参与度的新方法。
利用脑电图信号的分类器输出和频域特征以及它们的混合来检测用户状态。我们在来自21名健康志愿者的离线数据上，测试了它们在不同控制场景下的状态检测能力。
分类器输出和频域特征的混合优于单一方法的结果，并使得构建基于异步P300的BCI成为可能，其平均正确状态检测准确率超过95%。
我们的结果表明，在基于异步P300的BCI中，所有引入的状态检测方法都能有效避免非自愿选择，且混合方法是最有效的方法。
