# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments  
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individual tested in different locations or from different individuals altogether. Our study operates under this constraint, treating the data as independent observations without attempting to resolve patient identity. All of these scenarios--multiple patients across centers, with some cases involving a single patient tested across sessions--are considered possible within our analysis. This fragmentation, coupled with privacy and regulatory concerns, limits the feasibility of centralized processing [1]. Consequently, there is a growing need for intelligent decision-making frameworks that can operate effectively under data distribution constraints while maintaining high diagnostic accuracy. Given the complexity of EEG signals, manual interpretation is a time-consuming task that demands substantial expertise [2]. Automated classification systems can offer consistent and fast support to neurologists, aiding in long-term monitoring, early diagnosis, and personalized therapy strategies. According to the World Health Organization, epilepsy affects approximately 50 million people worldwide [3], and EEG remains the primary tool for its diagnosis. However, the increasing volume of EEG data collected from patients poses challenges for timely expert analysis. AI-based models, including deep learning and ensemble methods, have been explored to support clinical workflows by identifying abnormal patterns automatically [2; 4]. In particular, our ensemble-based approach aims to improve robustness and adaptability across diverse EEG data distributions. Previous studies have explored decision-making in scenarios involving dispersed or decentralized data, such as federated learning [5; 6], local modeling [7; 8], and ensemble-based techniques [9; 10]. Federated learning approaches enable collaborative model training without direct data sharing, but they often require synchronization and substantial communication overhead. Alternatively, local modeling strategies independently train models on separate data partitions and aggregate their predictions using ensemble methods. While effective, traditional ensemble strategies like majority voting or static averaging do not account for the varying relevance or reliability of models across different input instances, especially in heterogeneous and non-iid data environments. EEG signal classification has been extensively studied using a range of machine learning and deep learning techniques [11; 12; 13]. Common approaches include feature-based models utilizing statistical, spectral, and wavelet descriptors, as well as end-to-end deep learning architectures [14; 15]. Boosting algorithms, such as AdaBoost and Gradient Boosting, have demonstrated strong performance due to their ability to focus on hard-to-classify instances [16; 17]. However, many existing works assume access to a unified dataset and do not address the challenges associated with EEG data dispersion, such as feature inconsistency, sample variability, and localized context. Despite growing interest in distributed machine learning, there remains a significant gap in effectively integrating classifier outputs from dispersed EEG datasets in a way that adapts to each decision context. Existing ensemble methods often treat all models equally, regardless of their relevance or agreement. Moreover, most EEG classification studies either disregard the implications of data decentralization or lack mechanisms to dynamically select models best suited to individual test cases. While various dynamic ensemble selection techniques have been proposed in the literature [18; 19; 20; 21; 22], their application to EEG signal classification remains largely unexplored.