Microsaccades are small, involuntary eye movements vital for visual perception and neural processing. Traditional microsaccade studies typically use eye trackers or frame-based analysis, which, while precise, are costly and limited in scalability and temporal resolution. Event-based sensing offers a high-speed, low-latency alternative by capturing fine-grained spatiotemporal changes efficiently. This work introduces a pioneering event-based microsaccade dataset to support research on small eye movement dynamics in cognitive computing. Using Blender, we render high-fidelity eye movement scenarios and simulate microsaccades with angular displacements from 0.5 to 2.0 degrees, divided into seven distinct classes. These are converted to event streams using v2e, preserving the natural temporal dynamics of microsaccades, with durations ranging from 0.25 ms to 2.25 ms. We evaluate the dataset using Spiking-VGG11, Spiking-VGG13, and Spiking-VGG16, and propose Spiking-VGG16Flow, an optical-flow-enhanced variant implemented in SpikingJelly. The models achieve around 90 percent average accuracy, successfully classifying microsaccades by angular displacement, independent of event count or duration. These results demonstrate the potential of spiking neural networks for fine motion recognition and establish a benchmark for event-based vision research. The dataset, code, and trained models will be publicly available at https://waseemshariff126.github.io/microsaccades/ .

翻译：微眼动是微小且非自主的眼球运动，对视觉感知与神经处理至关重要。传统微眼动研究通常采用眼动仪或基于帧的分析方法，虽精度高但成本昂贵，且在可扩展性与时间分辨率方面受限。事件感知通过高效捕获细粒度时空变化，提供了一种高速、低延迟的替代方案。本研究引入了一个开创性的事件驱动微眼动数据集，以支持认知计算中微小眼动动态的研究。利用Blender，我们渲染高保真眼动场景，模拟角位移范围为0.5至2.0度的微眼动，并将其划分为七个不同类别。通过v2e工具将这些数据转换为事件流，保留了微眼动自然的时间动态特性，持续时间介于0.25毫秒至2.25毫秒之间。我们使用Spiking-VGG11、Spiking-VGG13和Spiking-VGG16评估该数据集，并提出了Spiking-VGG16Flow——一种在SpikingJelly中实现的光流增强变体。这些模型实现了约90%的平均准确率，成功按角位移对微眼动进行分类，且不受事件数量或持续时间的影响。这些结果证明了脉冲神经网络在精细运动识别方面的潜力，并为事件驱动视觉研究建立了基准。数据集、代码及训练模型将公开发布于https://waseemshariff126.github.io/microsaccades/。