Explainable Orthogonal Attention Networks for EEG-based Analysis: Leveraging Disentangled Representations to Enhance Diagnosis

Abstract

The complexity of EEG data presents significant challenges for accurate diagnosis in neurological conditions such as Alzheimer’s disease. In this paper, we introduce Explainable Orthogonal Attention Networks, a novel approach for EEG-based analysis that decouples spatial and temporal features to more effectively capture disease-related neural patterns. By leveraging orthogonal attention mechanisms, our model independently processes spatial relationships across EEG channels and temporal dynamics, enhancing both explainability and predictive performance. Our approach outperforms baselines, achieving superior performance in objective metrics, with a 14% relative improvement, while offering insights into the neural mechanisms underlying Alzheimer’s disease. Using attention maps and spectral analysis, we identified critical parietal and frontal contributions, along with EEG markers like elevated theta and reduced alpha power, commonly associated with Alzheimer’s disease. This method represents a significant step forward in developing explainable and high-performing EEG-based diagnostic tools. We will make the code and model’s weights publicly available upon publication at anonymized.