A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification over raw EEG data. In particular, the FG-HANet extracts features not only from original inputs but also from their mirrored versions, and applies Finite Impulse Response (FIR) filters at a granularity as fine as 2-Hz to acquire fine-grained spectral information. Furthermore, to guarantee sufficient attention to hemispheric asymmetry features, we tailor a three-stage training pipeline for the FG-HANet to further boost its performance. We conduct extensive evaluations on two public datasets, SEED and SEED-IV, and experimental results well demonstrate the superior performance of the proposed FG-HANet, i.e. 97.11% and 85.70% accuracy, respectively, building a new state-of-the-art. Our results also reveal the hemispheric dominance under different emotional states and the hemisphere asymmetry within 2-Hz frequency bands in individuals. These not only align with previous findings in neuroscience but also provide new insights into underlying emotion generation mechanisms.