Abstract
Users’ performance is known to be impacted by their emotional states. To better understand this relationship, different situations could be simulated during which the users’ emotional reactions are analyzed through sensors like eye tracking and EEG. In addition, virtual reality environments provide an immersive simulation context that induces high intensity emotions such as excitement. Extracting excitement from EEG provides more precise measures then other methods, however it is not always possible to use EEG headset in virtual reality environment. In this paper we present an alternative approach to the use of EEG for excitement detection using only eye movements. Results showed that there is a correlation between eye movements and excitement index extracted from EEG. Five machine learning algorithms were used in order to predict excitement trend exclusively from eye tracking. Results revealed that we can detect the offline excitements trend directly from eye movements with a precision of 92% using deep neural network.
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We acknowledge NSERC-CRD and Beam Me Up for funding this work.
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Abdessalem, H.B., Chaouachi, M., Boukadida, M., Frasson, C. (2019). Toward Real-Time System Adaptation Using Excitement Detection from Eye Tracking. In: Coy, A., Hayashi, Y., Chang, M. (eds) Intelligent Tutoring Systems. ITS 2019. Lecture Notes in Computer Science(), vol 11528. Springer, Cham. https://doi.org/10.1007/978-3-030-22244-4_26
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DOI: https://doi.org/10.1007/978-3-030-22244-4_26
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