An explainable multi-view representation fusion learning framework with hybrid MetaFormer for EEG-based epileptic seizure detection

Multi-view learning (MVL), a paradigm of deep learning, has greatly facilitated the detection of epileptic seizures from electroencephalograms (EEGs) owing to its remarkable capability to learn generalization features. However, existing MVL-based seizure detection methods rely on decision strategies to aggregate the discriminative outputs of separate learners, leading to insufficient extraction of inter-view complementarity and limiting the detection performance. To address this issue, this paper focuses on two aspects and proposes a multi-view representation fusion learning framework, which enables direct information fusion at the feature encoding level. Firstly, to enhance discriminability, we construct hierarchical multi-view representations based on the Gramian Angular Summation Field and an improved Stockwell transform by introducing the spatial characteristics of EEG montages and temporal dependency dynamics. Secondly, to process both local and global features comprehensively, we propose a hybrid MetaFormer network that incorporates inverted depth-wise separable convolutions and sparsity-enhanced shifted-window attention mechanisms. Specifically, the fusion unit with cross-attention mechanisms exploits the Key and Value matrices to achieve effective inter-view information exchange. The experimental results on the public CHB-MIT and Siena datasets demonstrate that the proposed method outperforms competing techniques in both sample-based and event-based evaluations for EEG seizure detection. In addition, an explanation module is devised based on feature importance scoring. In this way, our method enables post-hoc explanations for the multi-view fusion learning process and discriminative results utilizing topographic maps, indicating an explainable computational solution for EEG seizure detection.