Keywords and Phrases
Neural Correlates; Flow; Electroencephalogram; Machine Learning; Support Vector Machine; Random Forests; Multinomial Logistic Regression; K-Nearest Neighbor; Minimum Redundancy and Maximum Relevance
"In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and k-nearest neighbor classify the three user states -- Boredom, Flow, and Anxiety? (ii) Can we distinguish the flow state from other user states using machine learning models? (iii) What are the essential components of EEG signals for classifying the three user states? To extract the critical components of EEG signals, a feature selection method known as minimum redundancy and maximum relevance method was implemented. An average accuracy of 85 % is achieved for classifying the three user states by using the support vector machine classifier"--Abstract, page iii.
Nah, Fiona Fui-Hoon, 1966-
Siau, Keng, 1964-
Hall, Richard H.
Business and Information Technology
M.S. in Information Science and Technology
Missouri University of Science and Technology
viii, 40 pages
© 2018 Chandana Mallapragada, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Mallapragada, Chandana, "Classification of EEG signals of user states in gaming using machine learning" (2018). Masters Theses. 7831.