Predicting game-induced emotions using EEG, data mining and machine learning

Min, Xuan Lim and Jason Teo (2024) Predicting game-induced emotions using EEG, data mining and machine learning. Bulletin of the National Research Centre. pp. 48-57.

Text FULL TEXT.pdf Restricted to Registered users only Download (757kB) | Request a copy

Abstract

Background Emotion is a complex phenomenon that greatly afects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specifc properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufcient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarifed the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing diferent kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The crosssubject and subject-based experiments were conducted to evaluate the classifers’ performance. The top 10 association rules generated by the RCAR classifer will be examined to determine the possible relationship between the EEG signal’s frequency changes and game-induced emotions. Results The RCAR classifer constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifer could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance. Conclusion The fndings in this study fll the existing gap of game-induced emotion recognition feld by providing an in-depth evaluation on the ruleset algorithm’s performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result.

Item Type:	Article
Keyword:	EEG-based emotion recognition, Data mining, Machine learning, Game-induced EEG
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > T Technology (General) > T1-995 Technology (General) > T10.5-11.9 Communication of technical information
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	28 Apr 2025 14:54
Last Modified:	28 Apr 2025 14:54
URI:	https://eprints.ums.edu.my/id/eprint/43599

Actions (login required)

View Item