Saccades typically last between 20 and 100 ms, depending on the amplitude of the saccade ( 1, 4, 5). One prominent component of gaze behaviour is saccades, ballistic eye movements that rapidly re-orient the eye and thus the image impinging on the retina.

The analysis of gaze behaviour yields critical insights into processes underlying visual attention, perception, and executive control, as well as their mechanistic underpinnings ( 1, 2, 3). We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation. Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. We propose instead to use the median absolute deviation (MAD), a robust estimator of dispersion that is not influenced by outliers. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. Saccade detection is a critical step in the analysis of gaze data.

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