Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering

• Published in: IEEE access Vol. 12; pp. 52659 - 52668
• Main Authors: Almanza-Conejo, Oscar, Avina-Cervantes, Juan Gabriel, Garcia-Perez, Arturo, Ibarra-Manzano, Mario Alberto
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; Clustering; Clustering algorithms; Computer & video games; conflict learning; Datasets; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Games; gaming; Labeling; Labels; Machine learning; Measurement; Signal classification; User experience; Video games
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3387357

Popular comments suggest that continuous exposure of children and adolescents to video games yields a non-benefit behavior in the players' mental health. Contrarily, several studies have proven that commercial and serious games improve mental activity; some are used in treating psychological and physical disorders. This paper presents a method based on electroencephalogram signals analysis to classify multiple emotions recorded from subjects' gameplay seasons. In the core of this study, a self-assessed labeling method is evaluated using the Force, EEG, and Emotion Labelled Dataset (FEEL) for emotion recognition tasks. Besides, a 1-D Local Binary Pattern (LBP) method transforms the EEG temporal behavior to extract time-frequency features. Complementarily, the database artifacts were removed using a novel Conflict Learning approach for machine learning models, associating the extracted samples with the subjects' emotion labeling. A semi-supervised clustering method was employed to show the similarity between self-assessed subjects' labels. Finally, numerical results suggested a conflict between 23 original labels, improving the classification by over 92% in accuracy for 19 self-assessed classes.