In the twinkling of an eye: Synchronization of EEG and eye tracking based on blink signatures

Per Baekgaard, Michael Kai Petersen, J. E. Larsen
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引用次数: 13

Abstract

Achieving robust adaptive synchronization of multimodal biometric inputs: The recent arrival of wireless EEG headsets that enable mobile real-time 3D brain imaging on smartphones, and low cost eye trackers that provide gaze control of tablets, will radically change how biometric sensors might be integrated into next generation user interfaces. In experimental lab settings EEG neuroimaging and eye tracking data are traditionally combined using external triggers to synchronize the signals. However, with biometric sensors increasingly being applied in everyday usage scenarios, there will be a need for solutions providing a continuous alignment of signals. In the present paper we propose using spontaneous eye blinks, as a means to achieve near real-time synchronization of EEG and eye tracking. Analyzing key parameters that define eye blink signatures across the two domains, we outline a probability function based algorithm to correlate the signals. Comparing the accuracy of the method against a state of the art EYE-EEG plug-in for offline analysis of EEG and eye tracking data, we propose our approach could be applied for robust synchronization of biometric sensor data collected in a mobile context.
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眨眼间:基于眨眼特征的脑电图与眼动追踪同步
实现多模态生物识别输入的鲁棒自适应同步:最近出现的无线脑电图耳机可以在智能手机上实现移动实时3D脑成像,而低成本的眼动仪可以提供平板电脑的凝视控制,这将从根本上改变生物识别传感器如何集成到下一代用户界面中。在实验实验室设置中,EEG神经成像和眼动追踪数据传统上使用外部触发来同步信号。然而,随着生物识别传感器越来越多地应用于日常使用场景,将需要提供连续校准信号的解决方案。在本文中,我们提出利用自发眨眼作为一种手段来实现脑电图和眼动追踪的近实时同步。通过分析定义这两个领域眨眼特征的关键参数,我们提出了一种基于概率函数的眨眼信号关联算法。将该方法的准确性与用于离线分析EEG和眼动追踪数据的最先进eye -EEG插件进行比较,我们提出我们的方法可以应用于移动环境中收集的生物特征传感器数据的鲁棒同步。
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