Abstract: Event camera, a novel bio-inspired vision sensor, has drawn a lot of attention for its low latency, low power consumption, and high dynamic range. Currently, overfitting remains a critical problem in event-based classification tasks for SNN due to its relatively weak spatial representation capability. Data augmentation is a simple but efficient method to alleviate overfitting and improve the generalization ability of neural networks, and saliency-based augmentation methods are proven to be effective in the image processing field. However, there is no approach available for extracting saliency maps from SNNs. Therefore, for the first time, we present Spiking Layer-Time-wise Relevance Propagation rule (\texttt{SLTRP}) and Spiking Layer-wise Relevance Propagation rule (\texttt{SLRP}) in order for SNN to generate stable and accurate CAM and saliency maps. Based on this, we propose \texttt{EventRPG}, which leverages relevance propagation on the spiking neural network for more efficient augmentation. Our proposed method has been evaluated on several SNN structures, achieving state-of-the-art performance in object recognition tasks including N-Caltech101, CIFAR10-DVS, with accuracies of $85.62\%$ and $85.55\%$, as well as action recognition task SL-Animals with an accuracy of $91.59\%$. Codes will be available soon.