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 psychologi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10496111/ |
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| author | Oscar Almanza-Conejo Juan Gabriel Avina-Cervantes Arturo Garcia-Perez Mario Alberto Ibarra-Manzano |
| author_facet | Oscar Almanza-Conejo Juan Gabriel Avina-Cervantes Arturo Garcia-Perez Mario Alberto Ibarra-Manzano |
| author_sort | Oscar Almanza-Conejo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-176a5185df4246d2bc18aeb72d5afce5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-176a5185df4246d2bc18aeb72d5afce52025-08-20T02:48:51ZengIEEEIEEE Access2169-35362024-01-0112526595266810.1109/ACCESS.2024.338735710496111Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised ClusteringOscar Almanza-Conejo0https://orcid.org/0000-0001-7621-4601Juan Gabriel Avina-Cervantes1https://orcid.org/0000-0003-1730-3748Arturo Garcia-Perez2https://orcid.org/0000-0001-7070-1855Mario Alberto Ibarra-Manzano3https://orcid.org/0000-0003-4317-0248Department of Electronics Engineering, Engineering Division of the Campus Irapuato-Salamanca, University of Guanajuato, Salamanca, MexicoDepartment of Electronics Engineering, Engineering Division of the Campus Irapuato-Salamanca, University of Guanajuato, Salamanca, MexicoDepartment of Electronics Engineering, Engineering Division of the Campus Irapuato-Salamanca, University of Guanajuato, Salamanca, MexicoDepartment of Electronics Engineering, Engineering Division of the Campus Irapuato-Salamanca, University of Guanajuato, Salamanca, MexicoPopular 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.https://ieeexplore.ieee.org/document/10496111/Emotion recognitiongamingconflict learningclusteringmachine learning |
| spellingShingle | Oscar Almanza-Conejo Juan Gabriel Avina-Cervantes Arturo Garcia-Perez Mario Alberto Ibarra-Manzano Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering IEEE Access Emotion recognition gaming conflict learning clustering machine learning |
| title | Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering |
| title_full | Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering |
| title_fullStr | Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering |
| title_full_unstemmed | Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering |
| title_short | Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering |
| title_sort | emotion recognition in gaming dataset to reduce artifacts in the self assessed labeling using semi supervised clustering |
| topic | Emotion recognition gaming conflict learning clustering machine learning |
| url | https://ieeexplore.ieee.org/document/10496111/ |
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