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Evaluating ANN Efficiency in Recognizing EEG and Eye-Tracking Evoked Potentials in Visual-Game-Events

  • Andreas Wulff-JensenEmail author
  • Luis Emilio Bruni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 586)

Abstract

EEG and Eye-tracking signals have customarily been analyzed and inspected visually in order to be correlated to the controlled stimuli. This process has proven to yield valid results as long as the stimuli of the experiment are under complete control (e.g.: the order of presentation). In this study, we have recorded the subject’s electroencephalogram and eye-tracking data while they were exposed to a 2D platform game. In the game we had control over the design of each level by choosing the diversity of actions (i.e. events) afforded to the player. However we had no control over the order in which these actions were undertaken. The psychophysiological signals were synchronized to these game events and used to train and test an artificial neural network in order to evaluate how efficiently such a tool can help us in establishing the correlation, and therefore differentiating among the different categories of events. The highest average accuracies were between 60.25%–72.07%, hinting that it is feasible to recognize reactions to complex uncontrolled stimuli, like game events, using artificial neural networks.

Keywords

Artificial neural network Machine learning Electroencephalogram Eye-tracking Games Pupillometry Game events Psychophysiology 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Architecture, Design and Media TechnologyAalborg University CopenhagenCopenhagenDenmark

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