aspg-3-3924-1791419317 20261008002837 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2026 21 1 Abdelhameed Ibrahim School of ICT, Information Technology & Design Faculty, Bahrain Polytechnic, Isa Town, Bahrain Christos Gatzoulis School of ICT, Information Technology & Design Faculty, Bahrain Polytechnic, Isa Town, Bahrain El-Sayed M. El-kenawy School of ICT, Information Technology & Design Faculty, Bahrain Polytechnic, Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, Jordan Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt; Jadara University Research Center, Jadara University, Jordan Normal false false false EN-US X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> UnhideWhenUsed="true" Name="toc 3"/> 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6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} Emotion recognition using electroencephalogram (EEG) signals has become a pivotal area in affective computing, particularly within the context of human–computer interaction and game-based environments. This study aims to enhance the accuracy and robustness of EEG-based emotion classification by introducing a hybrid framework that combines the k-Nearest Neighbors (KNN) classifier with advanced metaheuristic feature selection techniques. Using the publicly available GAMEEMO dataset, which includes EEG recordings from 28 subjects engaged in four emotionally distinct computer games (boring, calm, horror, and funny), EEG data were acquired through a 14-channel Emotiv Epoc+ device and labeled using the Self-Assessment Manikin (SAM) scale. Baseline machine learning models including Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), and KNN were evaluated, with KNN achieving the highest base line performance. The KNN classifier was further optimized using several metaheuristic algorithms—namely WAO, BBO, GWO, GA, FA, PSO—and the proposed Improved Human Optimization Algorithm (iHOW). Experimental results show that the iHOW+KNN model achieved the best overall performance with an accuracy of 96.85%, sensitivity of 95.50%, specificity of 95.82%, and F1-score of 95.54%. Visual assessments using heatmaps, radar plots, and confidence intervals further validated the model’s reliability. These findings demonstrate the effectiveness of the iHOW+KNN framework in addressing the challenges of high-dimensional EEG data and highlight the potential of wearable EEG devices for real-time emotion recognition in affective computing applications into user experiences within the gaming environment. 2026 110 130 3924 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.210108 https://www.americaspg.com/journal/3/article/3924 https://www.americaspg.com/storage/41756508227.pdf