## 3 Methods
### Facial Expression Recognition
### Limitations and Future Directions

This study presents a promising neuro-adaptive framework using BIFL to enhance engagement and cognitive performance in children with neurodevelopmental disorders. However, some limitations must be acknowledged. Firstly, the participant group was limited to children aged 6-10 years. Emotional needs and attention regulation can vary significantly across developmental stages, and future studies should assess generalizability to younger and older populations. Secondly, EEG signals were acquired using the Muse headband, which, while portable and child-friendly, offers only four dry electrodes and is prone to motion artifacts--particularly relevant for children with ADHD. Although we applied preprocessing and artifact-removal techniques, future work should explore more robust denoising pipelines for mobile EEG. Thirdly, the facial expression recognition model was initially trained on adult facial datasets (e.g., KDEF, Emotional dataset), which may not fully reflect the emotional expression patterns of children. Since children often display more exaggerated or subtler expressions, this mismatch may introduce bias. While we mitigated this through geometric feature extraction and EEG-assisted disambiguation, future work should incorporate child-specific facial datasets for improved accuracy. Fourthly, the EEG model was pre-trained on adult data and adapted using a short (20 s) per-child calibration phase. Although this enabled real-time personalization, it may limit the model's ability to capture finer individual variability. Future research could benefit from integrating larger child-specific EEG datasets and employing domain adaptation, data augmentation (e.g., noise injection, waveform synthesis), or adversarial learning to improve generalization. Moreover, the intervention lasted four weeks, so long-term outcomes such as sustained attention gains, user fatigue, and habituation were not explored. Longitudinal studies are needed to assess the durability of cognitive and emotional improvements over time. All participants were from Brazilian schools, which may limit cultural generalizability. Future cross-cultural validation can help ensure broader applicability of the BIFL system. Finally, although BIFL was effective in real-time adaptation, it would be valuable to benchmark its performance against Reinforcement Learning (RL) models and Large Language Model (LLM)-based personalization strategies. While real-time clinical contexts pose challenges for RL, due to the need for large-scale offline training and consistent reward structures, simulated environments with synthetic behavioral trajectories could enable safe and meaningful comparison in future studies. Methodology, D.R.F. ; Software, D.R.F. ; Validation, D.R.F. ; Investigation, D.R.F. and P.P.d.S.A. ; Writing--original draft, D.R.F. ; Writing--review and editing, D.R.F. and P.P.d.S.A. ; Supervision, D.R.F. and P.P.d.S.A. All authors have read and agreed to the published version of the manuscript. This research did not receive direct external funding. However, the first author was supported by mobility grants for conducting pilot studies in Brazil, funded by the Newton Fund (UK), CONFAP (Brazil), and IEEE RAS-SIGHT. **Institutional Review Board Statement:** The study was conducted in direct collaboration with the Municipal Department of Education of Cambe, in the state of Parana. The process did not involve a local university; therefore, there was no internal university ethics committee involved. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** Due to the sensitive nature of the data involving children, and in compliance with GDPR and institutional ethical guidelines, the dataset is not publicly available. Access to data may be considered upon reasonable request and subject to additional ethical approval.