  The event camera's low power consumption and ability to capture microsecond
brightness changes make it attractive for various computer vision tasks.
Existing event representation methods typically convert events into frames,
voxel grids, or spikes for deep neural networks (DNNs). However, these
approaches often sacrifice temporal granularity or require specialized devices
for processing. This work introduces a novel token-based event representation,
where each event is considered a fundamental processing unit termed an
event-token. This approach preserves the sequence's intricate spatiotemporal
attributes at the event level. Moreover, we propose a Three-way Attention
mechanism in the Event Transformer Block (ETB) to collaboratively construct
temporal and spatial correlations between events. We compare our proposed
token-based event representation extensively with other prevalent methods for
object classification and optical flow estimation. The experimental results
showcase its competitive performance while demanding minimal computational
resources on standard devices. Our code is publicly accessible at
\url{https://github.com/NJUVISION/EventTransformer}.
