Enhanced Alzheimer's detection with EEG source imaging and multi-branch joint attention

Objective. Alzheimer's disease (AD) is a neurodegenerative disorder detectable via electroencephalogram (EEG). Traditional EEG-based AD detection methods do not fully leverage spatial information about brain activity and the correlation between different frequency bands, leading to suboptimal cross-patient performance.Approach. We propose a new multi-branch joint attention network (MJANet) based on electrophysiological source imaging (ESI) to create comprehensive spatial power maps, improving the spatial resolution of EEG. The MJANet incorporates a multi-branch joint attention (MBJA) mechanism to capture interactions across different frequency bands. The MJANet employs a new MBJA mechanism to capture interactions across frequency bands and a moving shifted window to capture global image features. It analyzes correlations between activities in various bands and brain regions to boost cross-patient detection capabilities.Main results. The proposed approach is validated on a public dataset with a leave-one-subject-out cross-validation strategy, achieving an 85.23% accuracy rate in differentiating AD from normal controls (NC), representing an 8.03% improvement over the state-of-the-art. Moreover, it achieves 75.57% accuracy in distinguishing frontotemporal dementia (FTD) from NC, and 63.97% for the classification of AD, NC, and FTD. We utilize GradCAM to visualize the joint attention mechanism, providing insights into its decision-making process.Significance. This work explores a novel biomarker that has the potential to enhance clinical diagnostic methods and improve diagnostic accuracy.