Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eyemovement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9): [2484][2485][2486][2487][2488][2489][2490][2491][2492][2493]2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

A novel algorithm for detection of saccades and postsaccadic oscillations in the presence of smooth pursuit movements is proposed. The method combines saccade detection in the acceleration domain with specialized on- and offset criteria for saccades and postsaccadic oscillations. The performance of the algorithm is evaluated by comparing the detection results to those of an existing velocity-based adaptive algorithm and a manually annotated database. The results show that there is a good agreement between the events detected by the proposed algorithm and those in the annotated database with Cohen's kappa around 0.8 for both a development and a test database. In conclusion, the proposed algorithm accurately detects saccades and postsaccadic oscillations as well as intervals of disturbances.

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