Delay-Embedded Representations for Robust Saccade Classification in Noisy Oculographic Signals

We introduce ACMA (Delay-Augmented Clustering and Model Approximation), a noise-robust pipeline for saccade detection in oculographic signals that couples delay-embedded clustering with physiologically grounded parametric refinement. Existing detectors face a recurring trade-off: velocity-thresholding methods are simple and interpretable but degrade catastrophically as recording noise grows, adaptive event-based detectors recover part of this gap at moderate noise but lose reliability at high noise, and recent deep models are sensitive to noise distributions unseen during training. ACMA addresses this trade-off in two stages. First, a sliding-window two-cluster decomposition with time-delay embedding identifies candidate saccades while remaining stable across noise regimes. Second, a parametric saccade waveform constrained by priors on amplitude, duration, and the saccadic main sequence validates and characterizes each event. We evaluate ACMA on a saccade-detection benchmark spanning a wide range of noise levels and nine representative baselines, including a leading deep-learning detector (U'n'Eye), adaptive methods (NH, REMoDNaV, Engbert), clustering-based I2MC, and classical velocity-thresholding methods (IVT, IVVT, IDT, IDVT). ACMA delivers consistent F1 gains in moderate-to-high noise regimes, where competing detectors degrade sharply, while additionally returning physically meaningful per-event metrics (amplitude, duration, peak velocity) for downstream neurological assessment, fatigue monitoring, and brain-computer interface applications.