Classification of Eye Tracking Data in Visual Information Processing Tasks Using Convolutional Neural Networks and Feature Engineering

Eye tracking technology has been adopted in numerous studies in the field of human–computer interaction (HCI) to understand visual and display-based information processing as well as the underlying cognitive processes employed by users when navigating a computer interface. Analyzing eye tracking data can also help identify interaction patterns with regard to salient regions of an information display. Deep learning technology is increasingly being used in the analysis of eye tracking data by allowing for the classification of large amounts of eye tracking results. In this paper, eye tracking data and convolutional neural networks (CNNs) were used to perform a classification task to predict three types of information presentation methods. As a first step, a number of data preprocessing and feature engineering approaches were applied to eye tracking data collected through a controlled visual information processing experiment. The resulting data were used as input for the comparison of four CNN models with different architectures. In this experiment, two CNN models were effective in classifying the information presentations with overall accuracy greater than 80%.