This manuscript demonstrates an improved model-based approach for synthetic degradation of previously captured eye movement signals. Signals recorded on a high-quality eye tracking sensor are transformed such that their resulting eye tracking signal quality is similar to recordings captured on a low-quality target device. The proposed model improves the realism of the degraded signals versus prior approaches by introducing a mechanism for degrading spatial accuracy and temporal precision. Moreover, a percentile-matching technique is demonstrated for mimicking the relative distributional structure of the signal quality characteristics of the target data set. The model is demonstrated to improve realism on a per-feature and per-recording basis using data from an EyeLink 1000 eye tracker and an SMI eye tracker embedded within a virtual reality platform. The model improves the median classification accuracy performance metric by 35.7% versus the benchmark model towards the ideal metric of 50%. This paper also expands the literature by providing an application-agnostic realism assessment workflow for synthetically generated eye movement signals.

翻译：本文展示了一种改进的基于模型的方法，用于对先前捕获的眼动信号进行合成退化处理。通过变换高质量眼动传感器记录的信号，使其产生的眼动信号质量与低质量目标设备记录的信号相似。与先前方法相比，所提出的模型通过引入空间精度和时间精度的退化机制，提高了退化信号的逼真度。此外，本文展示了一种百分位匹配技术，用于模拟目标数据集信号质量特征的相对分布结构。该模型利用 EyeLink 1000 眼动仪和嵌入虚拟现实平台中的 SMI 眼动仪收集的数据，在每特征和每记录的基础上证明了逼真度的提升。与基准模型相比，该模型将中位分类准确率性能指标提高了35.7%，接近理想指标50%。本文还通过提供一种与应用无关的合成眼动信号逼真度评估工作流程，拓展了现有文献。