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Evaluation of Classifiers for Emotion Detection while Performing Physical and Visual Tasks: Tower of Hanoi and IAPS
Prince of Songkla University, Thailand.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-8591-1035
BrightWare, Saudi Arabia.
Jinnah International Hospital, Pakistan.
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2018 (English)In: Intelligent Systems and Applications. IntelliSys 2018: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 / [ed] Kohei Arai, Supriya Kapoor, Rahul Bhatia, Springer, 2018, p. 347-363Conference paper, Published paper (Refereed)
Abstract [en]

With the advancement in robot technology, smart human-robot interaction is of increasing importance for allowing the more excellent use of robots integrated into human environments and activities. If a robot can identify emotions and intentions of a human interacting with it, interactions with humans can potentially become more natural and effective. However, mechanisms of perception and empathy used by humans to achieve this understanding may not be suitable or adequate for use within robots. Electroencephalography (EEG) can be used for recording signals revealing emotions and motivations from a human brain. This study aimed to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. For experimental purposes, we used visual (IAPS) and physical (Tower of Hanoi) tasks to record human emotional states in the form of EEG data. The obtained EEG data processed, formatted and evaluated using various machine learning techniques to find out which method can most accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a method for improving the accuracy of results. According to the results, Support Vector Machine was the first, and Regression Tree was the second best method for classifying EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00%, respectively. In both tasks, SVM was better in performance than RT. 

Place, publisher, year, edition, pages
Springer, 2018. p. 347-363
Series
Advances in Intelligent Systems and Computing,, ISSN 2194-5357, E-ISSN 2194-5365 ; 868
Keywords [en]
K-Nearest Neighbor (KNN), Regression Tree (RT), Bayesian Network (BNT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Tower of Hanoi (ToH), Cognitive Psychology, Human Computer Interaction (HCI), Electroencephalography (EEG)
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-78544DOI: 10.1007/978-3-030-01054-6_25Scopus ID: 2-s2.0-85057084220ISBN: 978-3-030-01053-9 (print)ISBN: 978-3-030-01054-6 (electronic)OAI: oai:DiVA.org:lnu-78544DiVA, id: diva2:1259499
Conference
Intelligent Systems Conference (IntelliSys), 6-7 September, 2018, London
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-08-29Bibliographically approved

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Hagelbäck, Johan

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