Biometric systems leveraging ElectroEncephaloGram (EEG) data for user authentication present significant potential in diverse contexts, especially in Smart City ecosystems where secure access to sensitive data is crucial (e.g., healthcare systems, intelligent transportation, smart grids, public safety, and citizen services). However, the complexity and variability of EEG data raise challenges in developing effective solutions. In this context, after a preliminary series of experiments used to find the best feature extraction method for the input, and performed by exploiting the Biometric EEG Dataset (BED), this paper proposes a novel EEG-based user verification framework. It utilizes Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction, followed by feature selection via the Boruta strategy and automated data quantization. An important aspect of this approach is the integration of Generative Adversarial Networks (GANs) to generate synthetic EEG data, which, along with real data, is employed to train an ensemble of Artificial Neural Networks (ANNs). The ensemble decision is made using soft voting mechanisms, promising a robust and competitive solution compared to current state-of-the-art techniques. Initial experiments suggest that this framework has significant potential for further development and optimization.