Wenzel Markus A, Golenia Jan-Eike, Blankertz Benjamin
Neurotechnology Group, Technische Universität Berlin Berlin, Germany.
Front Neurosci. 2016 Feb 15;10:23. doi: 10.3389/fnins.2016.00023. eCollection 2016.
Electroencephalography (EEG) and eye tracking can possibly provide information about which items displayed on the screen are relevant for a person. Exploiting this implicit information promises to enhance various software applications. The specific problem addressed by the present study is that items shown in real applications are typically diverse. Accordingly, the saliency of information, which allows to discriminate between relevant and irrelevant items, varies. As a consequence, recognition can happen in foveal or in peripheral vision, i.e., either before or after the saccade to the item. Accordingly, neural processes related to recognition are expected to occur with a variable latency with respect to the eye movements. The aim was to investigate if relevance estimation based on EEG and eye tracking data is possible despite of the aforementioned variability.
Sixteen subjects performed a search task where the target saliency was varied while the EEG was recorded and the unrestrained eye movements were tracked. Based on the acquired data, it was estimated which of the items displayed were targets and which were distractors in the search task.
Target prediction was possible also when the stimulus saliencies were mixed. Information contained in EEG and eye tracking data was found to be complementary and neural signals were captured despite of the unrestricted eye movements. The classification algorithm was able to cope with the experimentally induced variable timing of neural activity related to target recognition.
It was demonstrated how EEG and eye tracking data can provide implicit information about the relevance of items on the screen for potential use in online applications.
脑电图（EEG）和眼动追踪可能会提供有关屏幕上显示的哪些项目与某人相关的信息。利用这些隐含信息有望增强各种软件应用程序。本研究解决的具体问题是，实际应用中显示的项目通常多种多样。因此，能够区分相关和不相关项目的信息显著性各不相同。结果，识别可能发生在中央凹视觉或周边视觉中，即在扫视到该项目之前或之后。因此，预计与识别相关的神经过程相对于眼动会有可变的潜伏期。目的是研究尽管存在上述变异性，但基于EEG和眼动追踪数据进行相关性估计是否可行。
16名受试者执行了一项搜索任务，在记录EEG并追踪无约束眼动的同时，改变目标显著性。根据获取的数据，估计搜索任务中显示的哪些项目是目标，哪些是干扰项。
当刺激显著性混合时，目标预测也是可能的。发现EEG和眼动追踪数据中包含的信息是互补的，并且尽管眼动不受限制，但仍能捕获神经信号。分类算法能够应对与目标识别相关的神经活动在实验中诱导的可变时间。
证明了EEG和眼动追踪数据如何能够提供有关屏幕上项目相关性的隐含信息，以供在线应用中潜在使用。
