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Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort

机译:在立体可视化期间分类EEG信号以估计视觉舒适度

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摘要

With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users’ states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials’ (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users’ state by modifying the overlap of left and right images according to the classifier output.
机译:具有立体声显示的深度感觉太强可能阻碍视觉舒适性并且可能导致疲劳或疼痛。我们使用脑电图(EEG)来开发一个新颖的脑电电脑界面,监测用户状态以减少视觉应变。我们介绍了第一个系统,在使用EEG期间,在立体视觉中,在立体视觉中判断舒适条件。特别地,我们表明事件相关电位的变化(ERP)幅度或立体物体呈现后的EEG振荡功率的变化可用于估计视觉舒适度。我们的系统在1秒内反应到深度变化,平均达到63%的精度(高达76%),平均每次测量7个连续变化时(高达93%)。当使用简化的信号处理来模拟在线分析或减少EEG通道的数量时,性能是稳定的(≈62.5%)。本研究可能导致自适应系统,自动适用于用户的立体显示器和观看条件。例如,可以通过根据分类器输出修改左和右图像的重叠来匹配与用户状态的立体效果。

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