EEGDecoder-x: an explainable deep learning framework for cross-subject EEG-based detection of Alzheimer's and Creutzfeldt–Jakob disease

Abstract

Early detection of neurodegenerative diseases is critical. Distinguishing early-stage Creutzfeldt–Jakob disease (CJD) from “mimics” like Alzheimer's disease (AD) remains a major challenge; while EEG is valuable in advanced CJD, early-stage abnormalities are often non-specific and overlap with other rapidly progressive dementias. Deep learning offers promising EEG-based diagnostic solutions, but clinical adoption requires transparent decision-making, the interpretability of the features learned by deep learning models is equally important. In this context, careful model design and explainability are essential. In this paper, we propose a novel interpretable framework, EEGDecoder-x, for decoding EEG signals from subjects with Alzheimer's disease, Creutzfeldt–Jakob disease, and healthy controls, while providing insight into the model's learned characteristics. The EEGDecoder-x framework comprises two main components: a hybrid attention network for disease decoding (EEGDecoder-Net) and an explainability module (EEGDecoder-XAI). EEGDecoder-Net combines a convolutional neural network with a dual attention mechanism, followed by a classification layer, enabling efficient spatio-temporal feature extraction. EEGDecoder-XAI provides a comprehensive local and global explanations of the network's learning process for spatio-temporal dimensions. We validate the proposed framework using a Leave-One-Subject-Out evaluation paradigm, achieving 97.22% classification accuracy on a dataset of 36 subjects (12 with AD, 12 with CJD, and 12 healthy controls), and outperforming the baseline models, demonstrating both the effectiveness and interpretability of EEGDecoder-x.