DAFF-SNN: Dual Attention-driven and Feature Fusion-based Spiking Neural Network for Epilepsy Detection based on Electroencephalogram

Electroencephalogram (EEG) signals provide rich spatiotemporal brain features crucial for epilepsy diagnosis. Though traditional deep neural networks combining attention mechanisms excel in feature extraction, these models do not fully emulate the neural processing efficiency of the brain and they fall short in fully harnessing the spatiotemporal dynamics of EEG data. This study introduces the dual attention-driven and feature fusion-based spiking neural network (DAFF-SNN), which synergizes the spatiotemporal attention mechanism with SNNs' inherent temporal processing prowess to enhance the precision and efficiency for epilepsy detection. The DAFF-SNN utilizes an adaptive spiking fusion module (ASFM) for feature integration, optimizing the feature fusion process by exploiting the spatiotemporal complementarity of EEG data through a spike-driven strategy. Evaluations on the Bonn, Beriut, CHB-MIT, and Siena datasets demonstrate DAFF-SNN's high accuracies (100%, 97.6%, 98.2%, 99.6%), achieving comparable or superior performance to state-of-the-art ANN methods, highlighting its efficient epilepsy detection capability.