## 3 Methods
### Facial Expression Recognition
The study tested three hypotheses: (H1) adaptive multimodal interventions enhance engagement over static systems, (H2) emotion and cognitive-aware adaptations improve task performance, and (H3) neurophysiological signals add predictive value for engagement. The experiment spanned four weeks, involving 40 children (6-10 years) diagnosed with ADHD or ASD, recruited from five primary schools in Brazil in partnership with the Cambe City Council (state of Parana). Each child participated in a total of eight training sessions (i.e., twice per week over four weeks). Among them, 62.5% (25) had ADHD, while 37.5% (15) had ASD. Each participant completed a ten-minute trial with three tasks: a memory/concentration task (matching and recall exercises), a reasoning task (logical problem-solving challenges), and a social interaction task (collaborative teamwork). During gameplay, EEG signals classified real-time brainwave activity, while facial expressions analyzed emotional responses. The game dynamically adjusted visual, text, and auditory stimuli to optimize engagement based on cognitive and affective states. \[\hat{\theta}_{i}=\frac{\alpha_{i}^{\prime}}{\alpha_{i}^{\prime}+\beta_{i}^{ \prime}}\rightarrow\mu_{i}, \tag{2}\]3 Experimental setup: Children interacted with games on a laptop while wearing an EEG headband to monitor brain activity. Facial expressions were recorded using the laptop’s built-in camera for real-time affective analysis. Ethics and Data Privacy

The study was conducted in direct collaboration with the Municipal Department of Education of Cambe in the state of Parana, Brazil, and it was conducted in accordance with the ethical guidelines for AI-based interventions involving children. Informed consent was obtained from all parents and data privacy protocols were strictly followed. To ensure emotional well-being, real-time monitoring allowed facilitator intervention if distress was observed, and school representatives, such as teachers and pedagogical staff, remained present throughout the sessions. Only children aged 6 to 10 participated, in line with previous child-machine interaction studies, to ensure cognitive and social appropriateness. ## 5 Results

### Brainwave Processing and Classification

For the EEG-based concentration training, we utilized the Mental States EEG dataset [21], which consists of recordings representing three distinct mental states--concentration, relaxation, and neutral. The dataset includes EEG recordings from four adult participants (two male, two female), with each state recorded for 60 s per individual. A Muse EEG headband was used to capture signals from TP9, AF7, AF8, and TP10 electrode positions. EEG classification was performed using our 5-CNN architecture, trained on a specialized feature engineering pipeline, as previously detailed. The classification results (with an 80-20% train-test split) are shown in \[\hat{\theta}_{i}=\frac{\alpha_{i}^{\prime}}{\alpha_{i}^{\prime}+\beta_{i}^{ \prime}}\rightarrow\mu_{i}, \tag{2}\]4

The classification performance using the 5-CNN model along with improvements following the integration of synthetic EEG samples via the WGAN-GP demonstrate the benefits of synthetic data generation. The WGAN-GP model was trained on adult EEG data to augment limited real samples, improving the classifier accuracy from 92.0% to 98.48% when 50% synthetic data were used. Figure 7 demonstrates the similarities between real and synthetic EEG data generated by the WGAN-GP. However, excessive augmentation beyond 75% synthetic data did not yield higher improved returns, suggesting that 50% synthetic data augmentation provides the optimal balance between performance enhancement and dataset realism. These findings validate that a WGAN-GP generates EEG signals with statistical properties that closely match real data, improving classification accuracy. Figure 8 presents the effectiveness of Transfer Learning (TL) in EEG classification.