A lightweight spiking neural network for EEG-based motor imagery classification

• Published in: Neural networks Vol. 191; p. 107741
• Main Authors: Zhang, Herui, Wang, Haoran, An, Jiayu, Zheng, Shitao, Wu, Dongrui
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2025
• Subjects: Action Potentials - physiology; Brain - physiology; Brain-Computer Interfaces; Brain–computer interface; Deep learning; Electroencephalography - methods; Humans; Imagination - physiology; Motor imagery; Neural Networks, Computer; Spiking neural network; Deep learning; Motor imagery; Spiking neural network; Brain–computer interface
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2025.107741

Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram (EEG) based motor imagery (MI) classification, a classical brain–computer interface paradigm. The model has three desirable characteristics: (1) it has a brain-inspired architecture; (2) it is energy efficient; and, (3) it is dataset agnostic. Within-subject and cross-subject experiments on three public datasets demonstrated the superiority of our SNN model over four classical convolutional neural network based models in EEG based MI classification.