ArticleBehavior research methods2026
Systematic classification differences across eye movement detection algorithms.
Article in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Eye movement (EM) detection is a critical step in most eye-tracking (ET) research, typically relying on detectors-specialized algorithms designed to segment raw ET data into discrete oculomotor events. However, variability in detection algorithms and the lack of standardized evaluation frameworks hinder transparency and reproducibility across studies. In this work, we introduce pEYES, an open-source toolkit designed to streamline EM detection and enable robust, quantitative comparisons between detectors. The toolkit provides implementations for several widely used threshold-based detectors, along with multiple standardized evaluation procedures for assessing detection performance. Using pEYES, we evaluated seven detection algorithms on two publicly available human-annotated datasets containing recordings of subjects freely viewing color images. Performance was assessed using metrics such as Cohen's kappa, relative timing offset and deviation, and a sensitivity index (
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