  High-quality and challenging event stream datasets play an important role in
the design of an efficient event-driven mechanism that mimics the brain.
Although event cameras can provide high dynamic range and low-energy event
stream data, the scale is smaller and more difficult to obtain than traditional
frame-based data, which restricts the development of neuromorphic computing.
Data augmentation can improve the quantity and quality of the original data by
processing more representations from the original data. This paper proposes an
efficient data augmentation strategy for event stream data: EventMix. We
carefully design the mixing of different event streams by Gaussian Mixture
Model to generate random 3D masks and achieve arbitrary shape mixing of event
streams in the spatio-temporal dimension. By computing the relative distances
of event streams, we propose a more reasonable way to assign labels to the
mixed samples. The experimental results on multiple neuromorphic datasets have
shown that our strategy can improve its performance on neuromorphic datasets
both for ANNs and SNNs, and we have achieved state-of-the-art performance on
DVS-CIFAR10, N-Caltech101, N-CARS, and DVS-Gesture datasets.
