Abstract
In this study we built a deep-learning model based on EEG data to recognize the confusion of the player. The model was constructed from the EEG data of 20 participants and their confusion measured using a camera-based emotion recognition system (Facereader 7.1) while playing adventure 3D game. We asked the participants to identify their emotions while playing the game using a menu always displayed in the interface. This paper presents a confusion recognition model based on EEG features that can be used in levels of confusion detection. Results show that we can detect the level of confusion with high accuracy (94.8% accuracy for four confusion levels). We discussed about our results and the potential applications of such model (for entertainment or education purposes…).
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Notes
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Self-report’s emotion categories: Confusion, Surprise, Frustration, Fear, Boredom, Sadness, Anger, Engagement, Flow, Excitement, Joy and Calm.
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We acknowledge NSERC-CRD, BMU and Prompt for funding this research.
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Benlamine, M.S., Frasson, C. (2021). Confusion Detection Within a 3D Adventure Game. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_43
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