Adaptive spiking convolutional network for decoding motor imagery patterns

Motor imagery (MI) decoding is crucial for the advancement of brain-computer interface (BCI) technologies. However, existing models often suffer from susceptibility to noise and lack biological interpretability. In this study, we introduce the Adaptive Firing Threshold Spiking Convolutional Network (AFTSC-Net), which enhances the biological relevance of spiking neural networks (SNNs) by integrating an adaptive firing rate mechanism with the spatial feature extraction capabilities of convolutional neural networks (CNNs). Additionally, we refined surrogate gradient functions through enhanced spiking neuron mechanisms, significantly reducing computational power consumption while improving the accuracy of MI pattern recognition from electroencephalography (EEG) signals. To validate the efficacy of AFTSC-Net, we conducted experiments with 36 elite athletes from soccer, basketball, and table tennis, performing a comprehensive analysis of neural activity across various motor imagery tasks. The model not only demonstrated superior performance on the athlete dataset but also achieved the state-of-the-art results on public benchmark datasets, surpassing existing methods in terms of accuracy and computational efficiency. These findings highlight the potential of biologically inspired neural networks to enhance MI decoding accuracy and robustness, setting a new standard for real-time BCI applications.