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使用神经形态视觉传感器进行眼动分类

Khadija Iddrisu¹, Waseem Shariff², Maciej Stec²

¹Faculty of Engineering and Computing, Dublin City University, D09DXA0 Dublin, Ireland.

Journal of eye movement research

|February 20, 2026

概括

这项研究表明,尖端神经网络 (SNN) 使用事件摄像头有效地分类眼动. 这种方法为神经认知诊断提供了一种计算效率高且强大的方法.

科学领域:

神经科学是一个神经科学.
计算机视觉计算机视觉
生物灵感计算生物灵感计算

背景情况:

眼动分类 (固定,跳动) 对于理解神经和认知过程至关重要.
传统的RGB相机在运动模糊,延迟和噪声方面扎,限制了眼球跟踪的准确性.
神经形态事件摄像头 (ECs) 捕获异步的,高时间分辨率的数据,非常适合快速的眼动,但它们稀疏的数据挑战了传统的算法.

研究的目的:

用事件摄像头数据验证尖端神经网络 (SNN) 在眼动分类方面的有效性.
引入一种新的卷积式SNN架构,旨在处理稀疏,基于事件的视觉流.
在基于事件的眼球追踪任务中建立SNN性能的基准.

主要方法:

手动注释了EV-Eye数据集,这是最大的基于事件的公共眼睛跟踪基准,分为saccade和固定序列.
开发并实施了一个卷积式尖端神经网络 (SNN) 架构,该架构直接处理来自事件摄像头的尖端流.
将拟议的SNN模型与已建立的尖端网络 (SpikingVGG,SpikingDenseNet) 进行基准测试,并将计算复杂性与人工神经网络 (ANN) 进行比较.

主要成果:

在从10个用户的数据中分类和固定时,获得了94%的准确性和0.92精度.
与人工神经网络 (ANN) 相比,计算效率的提高超过了十倍.
突出了SNN在处理稀疏,基于事件的视觉数据方面的稳定性和效率.

结论:

尖端神经网络 (SNN) 提供了一种高效和强大的解决方案,用于使用事件摄像头数据进行眼动分类.
这种方法在开发快速,低功耗的神经认知诊断系统方面具有重大潜力.
该研究开创了SNNs在基于事件的眼睛跟踪中的应用,为性能和效率设定了新的标准.

关键词:

活动摄像头,活动摄像头.眼睛的运动眼睛的运动刺激神经网络的神经网络.

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相关概念视频

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.