## 3 Methods
Adult-trained models applied to child data achieved an initial accuracy of approximately 85% in the first epoch, demonstrating strong generalization. The results also

\[\hat{\theta}_{i}=\frac{\alpha_{i}^{\prime}}{\alpha_{i}^{\prime}+\beta_{i}^{ \prime}}\rightarrow\mu_{i}, \tag{2}\]5 Training and test results on the Mental States EEG dataset, without data augmentation. The classifier achieved 92% accuracy on the test set. child training, and bidirectional TL (adult-to-children and children-to-adult) to evaluate adaptability. Even with limited calibration data--acquiring 20 s of data from the children before the experiments to fine-tune the models--classification remained effective, indicating that TL enhances adaptation. Although the WGAN-GP was trained on adult EEG data, Transfer Learning and a brief per-child calibration phase allowed effective adaptation to child data. \[\hat{\theta}_{i}=\frac{\alpha_{i}^{\prime}}{\alpha_{i}^{\prime}+\beta_{i}^{ \prime}}\rightarrow\mu_{i}, \tag{2}\]6 EEG Transfer Learning: performance of models from adult on child data and vice versa. Classification using only 20 s of calibrations shows reliable classification. \[\hat{\theta}_{i}=\frac{\alpha_{i}^{\prime}}{\alpha_{i}^{\prime}+\beta_{i}^{ \prime}}\rightarrow\mu_{i}, \tag{2}\]7 Comparison of real and synthetic EEG signals generated by the WGAN-GP, showing waveform and spectral similarities. Top left image: synthetic signal of concentration. Top right: real concentration. Bottom left and right: synthetic and real wave form of concentration, respectively. Real-Time Testing and Model Generalization

During live experiments with children, our system achieved an average classifier confidence of 89% in detecting mental states in real time. This performance is largely attributable to (i) data augmentation, given the training data, and (ii) an initial calibration phase. Before gameplay, each child provided 20 s of concentration and relaxation EEG samples, allowing for fine-tuning the pre-trained classification model. This personalized calibration significantly enhanced classification accuracy, ensuring robust recognition of neurocognitive states. This step is essential for an accurate real-time classification. ### Facial Expression Recognition

Facial expressions were analyzed using the KDEF dataset [20] in conjunction with our approach. Given our features engineering, the DBMM ensemble combined predictions from SVM and LR to enhance classification accuracy. Incorporation of temporal features significantly improved performance, increasing the accuracy from 88% to 95%, demonstrating the importance of capturing temporal dependencies in facial expressions. While the KDEF dataset consists of deliberately posed expressions, it remains effective for real-time emotion classification, albeit with slightly reduced accuracy when applied to spontaneous emotions. To further enhance generalization, the classification models were also trained using the Real Emotion dataset, which consists of six adult participants who express genuine emotions while watching emotional video clips [22]. The dataset includes recordings of anger, fear, disgust, happiness, neutrality, sadness, and surprise, spanning 17 min per participant. When trained solely on this dataset, DBMM (SVM + RF) achieved 85% accuracy without temporal features, which increased to 90% when incorporating them. Cross-dataset evaluation demonstrated that training on KDEF and testing on Real Emotion yielded 75% accuracy, while the inverse setup resulted in 78% accuracy. By merging both datasets, the overall performance improved to 97%, making this the final model selected for real-time facial expression recognition within CGI. Real-Time Performance and Generalization

During the on-the-fly tests with children, the facial expression classification system achieved an average classifier confidence of 87% in recognizing emotional states in real-time.