# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 4 Discussion  
This study investigated the use of dynamic ensemble selection for EEG signal classification in scenarios where data are distributed due to privacy or logistical constraints. The experimental results across both the EEG Epilepsy dataset and the BCI Competition IV Dataset 1 indicate that the proposed approach--particularly when using Gradient Boosting and unified measurement-level coalitions--achieved consistently strong performance metrics. These outcomes suggest that the coalition-based strategy may offer advantages in adapting to the heterogeneity of EEG signals across distributed sources. While the unified coalition strategy generally outperformed both diverse coalition and traditional ensemble methods, this should be interpreted within the context of the datasets and settings examined. The dynamic nature of the model selection process appears to support more adjusted decisions, especially when high agreement among local models can be obtained. Moreover, the benefit of measurement-level fusion over abstract-level fusion highlights the value of using detailed probability distributions rather than relying solely on class labels. This study has several limitations that should be considered. First, the distributed environment was simulated by partitioning centralized EEG datasets, rather than using data collected from genuinely independent sources. This means that important real-world challenges were not fully addressed. Second, the experimental design assumed synchronous model training and evaluation, as well as balanced class distributions and uniform feature representation across all local sites. In real-world scenarios, data are often highly imbalanced or non-iid, and devices may operate asynchronously, which can introduce additional complexity and impact model robustness. Third, the study relied on a relatively small number of subjects and focused on two publicly available EEG datasets related to epilepsy and motor imagery. While stratified sampling was used to maintain class balance, the limited sample size and scope may restrict the generalizability of the findings to other patient populations or clinical applications. Finally, the feature extraction process was based on conventional statistical, spectral, and wavelet-based features. Incorporating more advanced feature representations, such as deep-learned embeddings, could potentially improve classification performance and should be explored in future research. Despite these limitations, the proposed framework appears promising for applications where data decentralization is essential due to privacy, security, or logistical constraints--such as mobile health (mHealth) platforms, tele-neurology, and collaborative clinical research. Its modular design allows for integration with various base classifiers and coalition strategies and has shown the ability to scale with increasing numbers of distributed data sources in our experiments. To contextualize our findings, we compared our best results with those reported in recent EEG-based epilepsy classification studies. For instance, [PERSON] et al. [14] reported an F1-score of 0.9516 using a dual-attention deep learning framework on a similarly structured epilepsy dataset, while [PERSON] et al. [12] achieved an accuracy of approximately 0.95 using a CNN-based pipeline on intracranial EEG data. In our study, the unified coalition-based ensemble approach with Gradient Boosting achieved an F1-score and balanced accuracy of 0.987. While direct comparisons are limited by differences in datasets and experimental conditions, these results suggest that our method may offer competitive performance, even in a distributed data setting. The reliability of EEG features across sessions is a well-documented challenge, particularly in clinical and cognitive applications that rely on single-session recordings for machine learning-based decision-making.