Neural correlates of states of user experience in gaming using EEG and predictive analytics

• Main Authors: MALLAPRAGADA, Chandana, NAH, Fiona Fui-Hoon, SIAU, Keng, CHEN, Langtao, YELAMANCHILI, Tejaswini
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: neural correlates; electroencephalogram; predictive analytics; linear regression; support vector machine; neural networks.; Graphics and Human Computer Interfaces; Systems Architecture
• Online Access: https://ink.library.smu.edu.sg/sis_research/9527; https://ink.library.smu.edu.sg/context/sis_research/article/10527/viewcontent/Neural_correlates_of_states_of_user_experience_in_gaming_using_EEG_and_predictive_analytics.pdf
• Institution: Singapore Management University

In this research, we will analyze EEG signals to obtain neural correlate classifications of user experience by applying predictive analytics. Boredom, flow, and anxiety are three states experienced by users interacting with a computer-based system. A within-subjects experiment was used to collect EEG data for these three states and a baseline. We will apply predictive analytics including linear regression, support vector machine, and neural networks to analyze and classify the EEG data for these three states of user experience.