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Learning Emotions EEG-based Recognition and Brain Activity: A Survey Study on BCI for Intelligent Tutoring System
Author(s) -
Tao Xu,
Yun Zhou,
Zi Wang,
Yixin Peng
Publication year - 2018
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2018.04.056
Subject(s) - computer science , brain–computer interface , valence (chemistry) , arousal , electroencephalography , emotion recognition , cognition , human–computer interaction , artificial intelligence , psychology , physics , quantum mechanics , neuroscience , psychiatry
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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