 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.
<coherence>  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.
 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 200Hz.\underline{\textit{\textbf{ has to do with the fact that even among the 500 Hz data, the inter-timestamp intervals varied widely.