More than two-thirds of dementia cases are attributed to Alzheimer’s disease (AD), while the remaining cases include frontotemporal dementia (FTD), vascular dementia, and other related disorders. Electroencephalography (EEG) is among the most cost-effective methods for supporting the diagnosis of these conditions and can serve as a valuable source of information for AI-assisted diagnostic systems. This paper focuses on the classification of EEG data from patients with FTD, Alzheimer’s disease, and healthy controls. Our study focused on two key issues in this setting. First, the reliable differentiation between FTD and AD. Secondly, EEG data are noisy, difficult to Pre-process, and often limited in their ability to capture long-range relationships. Modern Hopfield networks offer a promising direction because they are effective in pattern storage and retrieval and are closely related to attention mechanisms. In this work, four neural network architectures integrated with modern Hopfield networks are investigated on a publicly available dataset. A standardized workflow was adopted so that all models were trained and evaluated under identical conditions. The models were assessed using 5-fold stratified cross-validation together with hold-out evaluation. The best-performing model achieved 96% accuracy in the present experimental setting. Overall, the results show that the more expressive Hopfield-based architectures improve performance within the proposed model family and suggest that modern Hopfield networks are a promising component for EEG-based dementia classification.