Masters Theses

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

Abstract

"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.

Advisor(s)

Nah, Fiona Fui-Hoon, 1966-

Committee Member(s)

Siau, Keng, 1964-
Hall, Richard H.
Chen, Langtao

Department(s)

Business and Information Technology

Degree Name

M.S. in Information Science and Technology

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2018

Pagination

viii, 40 pages

Note about bibliography

Includes bibliographic references (pages 36-39).

Rights

© 2018 Chandana Mallapragada, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11434

Electronic OCLC #

1084479987

Share

 
COinS