Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments

This study presents a novel approach to EEG signal classification in distributed environments using dynamic ensemble selection. In scenarios where data dispersion arises due to privacy constraints or decentralized data collection, traditional global modelling is impractical. We propose a framework where classifiers are trained locally on independent subsets of EEG data without requiring centralized access. A dynamic coalition-based ensemble strategy is employed to integrate the outputs of these local models, enabling adaptive and instance-specific decision-making. Coalitions are formed based on conflict analysis between model predictions, allowing either consensus (unified) or diversity (diverse) to guide the ensemble structure. Experiments were conducted on two benchmark datasets: an epilepsy EEG dataset comprising 150 segmented EEG time series from ten patients, and the BCI Competition IV Dataset 1, with continuous recordings from seven subjects performing motor imagery tasks, for which a total of 1400 segments were extracted. In the study, we also evaluated the non-distributed (centralized) approach to provide a comprehensive performance baseline. Additionally, we tested a convolutional neural network specifically designed for EEG data, ensuring our results are compared against advanced deep learning methods. Gradient Boosting combined with measurement-level fusion and unified coalitions consistently achieved the highest performance, with an F1-score, accuracy, and balanced accuracy of 0.987 (for nine local tables). The results demonstrate the effectiveness and scalability of dynamic coalition-based ensembles for EEG diagnosis in distributed settings, highlighting their potential in privacy-sensitive clinical and telemedicine applications.