  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.
