Detecting affective covert user states with passive brain-computer interfaces

T. Zander, Sabine Jatzev
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引用次数: 56

Abstract

Brain-Computer Interfaces (BCIs) provide insight into ongoing cognitive and affective processes and are commonly used for direct control of human-machine systems [16]. Recently, a different type of BCI has emerged [4, 17], which instead focuses solely on the non-intrusive recognition of mental state elicited by a given primary human-machine interaction. These so-called passive BCIs (pBCIs) do, by their nature, not disturb the primary interaction, and thus allow for enhancement of human-machine systems with relatively low usage cost [12,18], especially in conjunction with gel-free sensors. Here, we apply pBCIs to detect cognitive processes containing covert user states, which are difficult to access with conventional exogenous measures. We present two variants of a task inspired by an erroneously adapting human-machine system, a scenario important in automated adaptation. In this context, we derive two related, yet complementary, applications of pBCIs. First, we show that pBCIs are capable of detecting a covert user state related to the perception of loss of control over a system. The detection is realized by exploiting non-stationarities induced by the loss of control. Second, we show that pBCIs can be used to detect a covert user state directly correlated to the user's interpretation of erroneous actions of the machine. We then demonstrate the use of this information to enhance the interaction between the user and the machine, in an experiment outside the laboratory.
被动脑机接口检测用户情感隐蔽状态
脑机接口(bci)提供了对正在进行的认知和情感过程的洞察,通常用于直接控制人机系统[16]。最近,出现了一种不同类型的脑机接口[4,17],它只关注由给定的主要人机交互引起的对精神状态的非侵入性识别。就其性质而言,这些所谓的被动脑机接口(pbci)不会干扰主要的交互作用,因此可以以相对较低的使用成本增强人机系统[12,18],特别是与无凝胶传感器结合使用时。在这里,我们应用pbci来检测包含隐蔽用户状态的认知过程,这是传统的外生测量难以获得的。我们提出了由错误适应的人机系统激发的任务的两种变体,这是自动适应的重要场景。在这种情况下,我们得出两个相关的,但互补的,pbci的应用。首先,我们证明了pbci能够检测与对系统失去控制的感知相关的隐蔽用户状态。该检测是通过利用由失控引起的非平稳性来实现的。其次,我们证明了pbci可以用来检测与用户对机器错误动作的解释直接相关的隐蔽用户状态。然后,在实验室之外的实验中,我们演示了如何使用这些信息来增强用户与机器之间的交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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