<scp>EEG</scp> Signal Identification of Epilepsy Based on Step‐Forward Encoding and Bidirectional Spiking Recurrent Neural Network

ABSTRACT Electroencephalography (EEG) is a crucial tool for diagnosing neurological disorders like epilepsy. While Artificial Neural Networks (ANNs) have shown strong performance, their large parameter counts and high power consumption limit their practical application. Spiking Neural Networks (SNNs), with their inherent sparsity and parallelism, offer a promising solution; yet most existing SNN models for epilepsy detection are confined to binary classification and fail to fully exploit the rich spatiotemporal dependencies within EEG data. To address these limitations, this study proposes a lightweight Bidirectional Spiking Recurrent Neural Network (Bi‐SRNN) for advanced seizure stage classification. We employ Step‐Forward (SF) encoding to mitigate information loss from high‐frequency EEG oscillations and introduce the Bi‐SRNN architecture, based on the Adaptive Leaky Integrate‐and‐Fire (ALIF) model, to specifically enhance multi‐class classification performance and capture long‐term temporal features. Our model achieved accuracies of 100% and 99.00% in binary and ternary classification tasks on the public Bonn dataset through five‐fold cross‐validation, also achieving strong results on the New Delhi dataset. Furthermore, in transfer learning experiments where the model pre‐trained on the Bonn dataset was applied to new datasets, it demonstrated good generalization performance, also achieving strong results on the New Delhi dataset. With superior performance in both accuracy and model efficiency, the proposed method is well‐suited for deployment on edge devices, offering a more effective tool to assist in clinical diagnosis and treatment.