A Method for the Detection of Poorly-Formed or Misclassified Saccades: A case study using the GazeCom Dataset




Friedman, Lee
Djanian, Shagen
Komogortsev, Oleg

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There are many automatic methods for the detection of eye movement types like fixation and saccades. Evaluating the accuracy of these methods can be a difficult and time-consuming process. We present a method to detect misclassified or poorly formed saccades\footnote{Throughout the manuscript, when we use the word ``misclassified'', we will be referring to both misclassified or poorly formed. saccades.}, regardless of how they were classified. We developed and tested our method on saccades from the very large and publicly available GazeCom dataset. We started out by creating a total of 9 metrics (velocity shape, velocity shape amplitude, position shape, position shape amplitude, flatness, entropy, kurtosis, skewness, and the Dip Test statistic of multimodality) which will be explained below. We applied these metrics to horizontal saccades of 20, 40 and 60 ms duration. For each duration, we performed a data reduction step with factor analysis to see how these 9 metrics were naturally grouped. For every duration, there were 2 factors, one which was dominated by our velocity shape metric and one which was dominated by our entropy metric. We determined that the entropy metric was the single most valuable metric for detecting misclassified saccades. We illustrate the types of saccades that our entropy metric indicates are misclassified. Link to Python Code https://github.com/sdjanian/sacanalysis



saccades, GazeCom, Computer Science



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