Learning Emotions EEG-based Recognition and Brain Activity: A Survey Study on BCI for Intelligent Tutoring System

Tao Xu · Yun Zhou · Zi Wang · Yixin Peng

66 citations0/0 files storedProcedia Computer Science

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Intelligent Tutoring Systems and Adaptive Learning

Abstract

Learners experience emotions in a variety of valence and arousal in learning, which impacts the cognitive process and the success of learning. Learning emotions research has a wide range of benefits from improving learning outcomes and experience in Intelligent Tutoring System (ITS), as well as increasing operation and work productivity. This survey reviews techniques that have been used to measure emotions and theories for modeling emotions. It investigates EEG-based Brain-Computer Interaction (BCI) of general and learning emotion recognition. The induction methods of learning emotions and related issues are also included and discussed. The survey concludes with challenges for further learning emotion research.

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References

1994Measuring emotion: The self-assessment manikin and the semantic differential 2010Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications 1996Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament 2012Academic Emotions and Student Engagement 2013Emotional state classification from EEG data using machine learning approach 2014The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions 2009Emotion classification based on gamma-band EEG 2011EEG-based emotion recognition during watching movies

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