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This manuscript demonstrates an improved model-based approach for syntheticdegradation of previously captured eye movement signals. Signals recorded on ahigh-quality eye tracking sensor are transformed such that their resulting eyetracking signal quality is similar to recordings captured on a low-qualitytarget device. The proposed model improves the realism of the degraded signalsversus prior approaches by introducing a mechanism for degrading spatialaccuracy and temporal precision. Moreover, a percentile-matching technique isdemonstrated for mimicking the relative distributional structure of the signalquality characteristics of the target data set. The model is demonstrated toimprove realism on a per-feature and per-recording basis using data from anEyeLink 1000 eye tracker and an SMI eye tracker embedded within a virtualreality platform. The model improves the median classification accuracyperformance metric by 35.7% versus the benchmark model towards the ideal metricof 50%. This paper also expands the literature by providing anapplication-agnostic realism assessment workflow for synthetically generatedeye movement signals.

A Mechanistic Transform Model for Synthesizing Eye Movement Data with Improved Realism