In a recent article, Zemblys et al. 1 reported on a new method for the classification of eye-movements using deep neural networks. I will refer to this paper as the "gazeNet paper "  . I have found 2 errors and two problems with that paper that are explained herein. Error 1: The gazeNet machine-learning based \textit{\textbf{  classification method was built assuming that a hand-scored dataset from Lund University was all collected at 500 Hz, but in fact, six of the 34 recording files were actually collected at 200 Hz . Of the six datasets that were used as the training set for the gazeNet algorithm, 2 were actually collected at 200 Hz. Problem 1\underline{\textit{\textbf{ has to do with the fact that even among the 500 Hz data, the inter-timestamp intervals varied widely. Problem 2 \underline{\textit{\textbf{ is that there are many unusual discontinuities in the saccade trajectories from the Lund University dataset that make it a very poor choice for the construction of an automatic classification method. Error 2 \underline{\textit{\textbf{ arises out of the novel event-related agreement analysis employed by the gazeNet authors. Although the authors intended to classify unmatched events as either false positives or false negatives, many are actually being classified as true negatives. True negatives are not errors, and any unmatched event misclassified as a true negative is actually driving the kappa statistic higher, whereas unmatched events should be driving the kappa statistic lower.
 Zemblys et al. \mbox{%DIFAUXCMD gazeNet reported on a method for the classification of eye-movements ( "gazeNet " ) . I have found 2 errors and two problems with that paper that are explained herein. \textit{\textbf{Error 1: The gazeNet classification method was built assuming that a hand-scored dataset from Lund University was all collected at 500 Hz, but in fact, six of the 34 recording files were actually collected at 200Hz . Of the six datasets that were used as the training set for the gazeNet algorithm, 2 were actually collected at 200Hz.\underline{\textit{\textbf{Problem 1 has to do with the fact that even among the 500Hz data, the inter-timestamp intervals varied widely. \underline{\textit{\textbf{Problem 2 is that there are many unusual discontinuities in the saccade trajectories from the Lund University dataset that make it a very poor choice for the construction of an automatic classification method. \underline{\textit{\textbf{Error 2 arises out of the novel event-related agreement analysis employed by the gazeNet authors. Although the authors intended to classify unmatched events as either false positives or false negatives, many are actually being classified as true negatives. True negatives are not errors, and any unmatched event misclassified as a true negative is actually driving kappa higher, whereas unmatched events should be driving kappa lower.
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In a recent article,
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\mbox{%DIFAUXCMD gazeNet
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new
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using deep neural networks. I will refer to this paper as the
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Error 1: The gazeNet machine-learning based
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Error 1:
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The gazeNet
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200 Hz
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200 Hz. Problem 1
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Problem 1
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500 Hz
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Problem 2
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Error 2
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the kappa statistic
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the kappa statistic
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