A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection.

Introduction: Motor imagery (MI) classification and sleep apnea (SA) detection are two critical tasks in brain-computer interface (BCI) and biomedical signal analysis. Traditional deep learning models have shown promise in these domains, but often struggle with temporal sparsity and energy efficiency, especially in real-time or embedded applications. Methods: In this study, we propose SpiTranNet, a novel architecture that deeply integrates Spiking Neural Networks (SNNs) with Transformers through Spiking Multi-Head Attention (SMHA), where spiking neurons replace standard activation functions within the attention mechanism. This integration enables biologically plausible temporal processing and energy-efficient computations while maintaining global contextual modeling capabilities. The model is evaluated across three physiological datasets, including one electroencephalography (EEG) dataset for MI classification and two electrocardiography (ECG) datasets for SA detection. Results: Experimental results demonstrate that the hybrid SNN-Transformer model achieves competitive accuracy compared to conventional machine learning and deep learning models. Discussion: This work highlights the potential of neuromorphic-inspired architectures for robust and efficient biomedical signal processing across diverse physiological tasks.