# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
To address these challenges, this study introduces a dynamic ensemble selection framework for EEG signal classification in dispersed data environments. Local models are trained independently on non-overlapping subsets of EEG data, simulating a decentralized scenario. At inference time, we dynamically form coalitions of models based on their predictive behavior for each test instance. These coalitions, formed through conflict analysis, represent either high consensus or high diversity. By using both measurement-level and abstract-level fusion strategies, we adaptively aggregate the most informative predictions. The novelty lies in the context-aware coalition formation and its application to EEG diagnosis under data fragmentation constraints. The main contributions of this work are as follows:  
* A comprehensive ensemble framework that supports EEG classification with dispersed data without centralized access. * A novel coalition-based dynamic model selection mechanism using conflict metrics. * An in-depth evaluation across multiple dispersion scenarios with various base classifiers. * Empirical evidence showing that unified coalitions with measurement-level fusion achieve good performance, particularly with Gradient Boosting models. EEG plays a pivotal role in clinical neurology by providing real-time insight into the brain's electrical activity. In epilepsy diagnosis, EEG allows for the identification of seizure patterns across different stages of the condition, which is crucial for timely intervention and treatment. However, interpreting EEG signals remains a challenging task due to their complexity and variability across patients. In this context, improving the accuracy and robustness of EEG classification models directly benefits medical professionals by offering decision support tools that reduce diagnostic uncertainty. Moreover, such improvements are also vital for the development of brain-computer interfaces (BCIs), which depend on reliable EEG interpretation to enable communication and control in individuals with severe motor impairments. By proposing a framework suitable for distributed data environments, this study addresses real-world constraints, such as data privacy and institutional fragmentation, and aims to support scalable, accurate, and privacy-preserving EEG analysis for both diagnostic and assistive technologies. The approach proposed in this paper introduces a dynamic ensemble classification framework that adapts to the varying quality and distribution of EEG signals without requiring centralized access to the full dataset. In contrast to existing methods that either assume access to homogeneous data or apply static ensemble techniques, our strategy dynamically selects and integrates classifier outputs based on the consistency or disagreement between models. The originality of the method lies in its coalition-based selection mechanism, which considers the local predictive behavior of models and builds consensus or diversity groups tailored to each test instance. This mechanism is applied to distributed EEG data representing seizure stages across different patients--addressing an important gap in the literature, where classifier integration is often simplistic and lacks adaptability to decentralized clinical data. The results suggest that our unified, context-aware ensemble strategy not only improves performance metrics but also supports realistic deployment scenarios, such as telemedicine and hospital networks, where data cannot be freely shared. The remainder of this paper is structured as follows. Section 2 describes the materials and methods used, including the classifiers, and coalition formation strategies. Section 3 presents the experimental setup and results. Also, the dataset, feature extraction, and data preparation are discussed in this section. Section 4 discusses the implications of the findings and limitations. Section 5 concludes the paper with key findings and possible future directions.