Dual-Transformer Cross-Attention Framework for Alzheimer’s disease detection via dPTE-Guided EEG channel selection and multi-modal integration

Accurate and efficient detection of Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) poses significant challenges in neuroscience and healthcare. Our proposed AI framework, Dual-Transformer Cross-Attention Network (DTCA-Net), combines deep learning and directed Phase Transfer Entropy (dPTE) to optimize channel selection crucial for the detection of AD and FTD. Our method identified six critical EEG channels (F7, F8, T3, T4, O1, and O2) through dPTE analysis. We propose the DTCA-Net architecture, which fuses dPTE and differential entropy (DE) features via a multi-head cross-attention layer, projecting dPTE into queries and DE into keys/values over the temporal dimension, so that connectivity (dPTE) shifts are explicitly aligned with spectral complexity (DE), yielding richer spatiotemporal representations. By leveraging a reduced set of EEG channels identified via dPTE, DTCA-Net performance is comparable to previous state-of-the-art models. Additionally, we introduce an adaptive post-processing voting mechanism to enhance subject-level predictions. This approach achieves an F1 score of 84.9% for AD vs. control (CN) detection and 66.5% for FTD vs. CN detection. Overall, compared to traditional full-channel utilization, our model demonstrates the practicality of a reduced-channel solution for clinical applications in AD and FTD detection, enhancing the accessibility and cost-effectiveness of EEG-based diagnostics. The code has been released on GitHub . • Identified key EEG channels for Alzheimer’s detection (AD) using phase transfer entropy. • Introduced a Dual-Transformer Cross-Attention Network using only six EEG channels. • Highlighted lateral frontal, temporal, and occipital electrodes as crucial for AD.