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基于网络摄像头的眼动追踪技术在视觉配对比较任务中评估视觉记忆

Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task.

作者信息

Bott Nicholas T, Lange Alex, Rentz Dorene, Buffalo Elizabeth, Clopton Paul, Zola Stuart

机构信息

Department of Medicine, School of Medicine, Stanford UniversityStanford, CA, United States.

Neurotrack Technologies, Inc.Redwood City, CA, United States.

出版信息

Front Neurosci. 2017 Jun 28;11:370. doi: 10.3389/fnins.2017.00370. eCollection 2017.

Abstract

Web cameras are increasingly part of the standard hardware of most smart devices. Eye movements can often provide a noninvasive "window on the brain," and the recording of eye movements using web cameras is a burgeoning area of research. This study investigated a novel methodology for administering a visual paired comparison (VPC) decisional task using a web camera.To further assess this method, we examined the correlation between a standard eye-tracking camera automated scoring procedure [obtaining images at 60 frames per second (FPS)] and a manually scored procedure using a built-in laptop web camera (obtaining images at 3 FPS). This was an observational study of 54 clinically normal older adults.Subjects completed three in-clinic visits with simultaneous recording of eye movements on a VPC decision task by a standard eye tracker camera and a built-in laptop-based web camera. Inter-rater reliability was analyzed using Siegel and Castellan's kappa formula. Pearson correlations were used to investigate the correlation between VPC performance using a standard eye tracker camera and a built-in web camera. Strong associations were observed on VPC mean novelty preference score between the 60 FPS eye tracker and 3 FPS built-in web camera at each of the three visits ( = 0.88-0.92). Inter-rater agreement of web camera scoring at each time point was high (κ = 0.81-0.88). There were strong relationships on VPC mean novelty preference score between 10, 5, and 3 FPS training sets ( = 0.88-0.94). Significantly fewer data quality issues were encountered using the built-in web camera. Human scoring of a VPC decisional task using a built-in laptop web camera correlated strongly with automated scoring of the same task using a standard high frame rate eye tracker camera.While this method is not suitable for eye tracking paradigms requiring the collection and analysis of fine-grained metrics, such as fixation points, built-in web cameras are a standard feature of most smart devices (e.g., laptops, tablets, smart phones) and can be effectively employed to track eye movements on decisional tasks with high accuracy and minimal cost.

摘要

网络摄像头越来越成为大多数智能设备标准硬件的一部分。眼动通常能提供一个无创的“大脑之窗”,而利用网络摄像头记录眼动是一个新兴的研究领域。本研究调查了一种使用网络摄像头进行视觉配对比较(VPC)决策任务的新方法。为了进一步评估该方法,我们检验了标准眼动追踪摄像头自动评分程序(以每秒60帧的速度获取图像)与使用笔记本电脑内置网络摄像头手动评分程序(以每秒3帧的速度获取图像)之间的相关性。这是一项针对54名临床正常老年人的观察性研究。受试者完成了三次门诊就诊,同时由标准眼动追踪摄像头和基于笔记本电脑内置的网络摄像头记录他们在VPC决策任务中的眼动情况。使用西格尔和卡斯特兰的kappa公式分析评分者间信度。采用皮尔逊相关性分析来研究使用标准眼动追踪摄像头和内置网络摄像头时VPC表现之间的相关性。在三次就诊中的每一次,60帧每秒的眼动追踪器和3帧每秒的内置网络摄像头在VPC平均新奇偏好得分上都观察到了很强的相关性(=0.88 - 0.92)。每个时间点网络摄像头评分的评分者间一致性都很高(κ = 0.81 - 0.88)。在10帧每秒、5帧每秒和3帧每秒的训练集之间,VPC平均新奇偏好得分存在很强的相关性(=0.88 - 0.94)。使用内置网络摄像头遇到的数据质量问题明显更少。使用笔记本电脑内置网络摄像头对VPC决策任务进行人工评分与使用标准高帧率眼动追踪摄像头对同一任务进行自动评分密切相关。虽然这种方法不适用于需要收集和分析细粒度指标(如注视点)的眼动追踪范式,但内置网络摄像头是大多数智能设备(如笔记本电脑、平板电脑、智能手机)的标准功能,并且可以有效地用于以高精度和低成本追踪决策任务中的眼动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89fd/5487587/7aeb5c443e6b/fnins-11-00370-g0001.jpg

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