HR-SNN: An End-to-End Spiking Neural Network for Four-Class Classification Motor Imagery Brain–Computer Interface

Abstract

Spiking neural network (SNN) excels in processing temporal information and conserving energy, particularly when deployed on neuromorphic hardware. These strengths position SNN as an ideal choice for developing wearable brain–computer interface (BCI) devices. However, the application of SNN in complex BCI tasks, like four-class motor imagery classification, is limited. In light of this, this study introduces a powerful SNN architecture hybrid response SNN (HR-SNN). We employ parameterwise gradient descent methods to optimize spike encoding efficiency. The SNN's frequency perception is improved by integrating a hybrid response spiking module. In addition, a diff-potential spiking decoder is designed to optimize SNN output potential utilization. Validation experiments are performed on PhysioNet and BCI competition IV 2a datasets. On PhysioNet, our model achieves accuracies of 67.24% and 74.95% using global training and subject-specific transfer learning, respectively. On BCI competition IV 2a, our approach attains an average accuracy of 77.58%, surpassing all the compared SNN models and demonstrating competitiveness against state-of-the-art (SOTA) convolution neural network (CNN) approaches. We validate the robustness of HR-SNN under noise and channel loss scenarios. Additionally, energy analysis reveals HR-SNN's superior energy efficiency compared to existing CNN models. Notably, HR-SNN exhibits a 2–16 times energy consumption advantage over existing SNN methods.