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

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.