The study of eye movements, particularly saccades and fixations, are fundamental to understanding the mechanisms of human cognition and perception. Accurate classification of these movements requires sensing technologies capable of capturing rapid dynamics without distortion. Event cameras, also known as Dynamic Vision Sensors (DVS), provide asynchronous recordings of changes in light intensity, thereby eliminating motion blur inherent in conventional frame-based cameras and offering superior temporal resolution and data efficiency. In this study, we introduce a synthetic dataset generated with Blender to simulate saccades and fixations under controlled conditions. Leveraging Spiking Neural Networks (SNNs), we evaluate its robustness by training two architectures and finetuning on real event data. The proposed models achieve up to 0.83 accuracy and maintain consistent performance across varying temporal resolutions, demonstrating stability in eye movement classification. Moreover, the use of SNNs with synthetic event streams yields substantial computational efficiency gains over artificial neural network (ANN) counterparts, underscoring the utility of synthetic data augmentation in advancing event-based vision. All code and datasets associated with this work is available at https: //github.com/Ikhadija-5/SynSacc-Dataset.

翻译：眼动研究，特别是扫视与注视，是理解人类认知与感知机制的基础。对这些运动的精确分类需要能够无失真捕捉快速动态的传感技术。事件相机，亦称动态视觉传感器（DVS），以异步方式记录光强变化，从而消除了传统帧式相机固有的运动模糊，并提供了卓越的时间分辨率与数据效率。本研究引入了一个利用Blender生成的合成数据集，用于模拟受控条件下的扫视与注视。我们利用脉冲神经网络（SNNs），通过训练两种架构并在真实事件数据上进行微调，评估了其鲁棒性。所提出的模型实现了高达0.83的准确率，并在不同时间分辨率下保持了一致的性能，证明了其在眼动分类中的稳定性。此外，与人工神经网络（ANN）相比，使用SNNs处理合成事件流带来了显著的计算效率提升，突显了合成数据增强在推进基于事件的视觉研究中的效用。本工作相关的所有代码与数据集可在 https://github.com/Ikhadija-5/SynSacc-Dataset 获取。