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Zemblys et al. \cite{gazeNet} reported on a method for the classification ofeye-movements ("gazeNet"). I have found 3 errors and two problems with thatpaper that are explained herein. \underline{\textit{\textbf{Error 1}}} ThegazeNet classification method was built assuming that a hand-scored datasetfrom Lund University was all collected at 500 Hz, but in fact, six of the 34recording files were actually collected at 200Hz. Of the six datasets that wereused as the training set for the gazeNet algorithm, 2 were actually collectedat 200Hz. \underline{\textit{\textbf{Problem 1}}} has to do with the fact thateven among the 500Hz data, the inter-timestamp intervals varied widely.\underline{\textit{\textbf{Problem 2}}} is that there are many unusualdiscontinuities in the saccade trajectories from the Lund University datasetthat make it a very poor choice for the construction of an automaticclassification method. \underline{\textit{\textbf{Error 2}}} The gazeNetalgorithm was trained on the Lund dataset, and then compared to other methods,not trained on this dataset, in terms of performance on this dataset. This isan inherently unfair comparison, and yet no where in the gazeNet paper is thisunfairness mentioned. \underline{\textit{\textbf{Error 3}}} arises out of thenovel event-related agreement analysis employed by the gazeNet authors.Although the authors intended to classify unmatched events as either falsepositives or false negatives, many are actually being classified as truenegatives. True negatives are not errors, and any unmatched event misclassifiedas a true negative is actually driving kappa higher, whereas unmatched eventsshould be driving kappa lower.

Three errors and two problems in a recent paper: gazenet: End-to-end  eye-movement event detection with deep neural networks (Zemblys, Niehorster,  and Holmqvist, 2019)