利用瞳孔和角膜反射信号进行微扫视检测

D. Niehorster, M. Nyström
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引用次数: 1

摘要

在当代研究中,通常使用基于视频的眼动仪获得的校准的注视速度信号来进行微动检测。为了产生该信号,瞳孔和角膜反射(CR)信号相互相减,并应用微分滤波器,两者都可以防止由于信号失真和噪声放大而检测到小的微跳。我们提出了一种新的算法,该算法直接从未校准的瞳孔和CR信号中检测微眼跳。它基于瞳孔和CR信号之间的去趋势,然后是窗相关。该算法优于该领域最常用的算法(Engbert & Kliegl, 2003),特别是对于即使用肉眼也难以在速度信号中看到的小幅度微跳。我们认为,在检测微小的微跳时,考虑眼动仪最基本的输出,即瞳孔和CR信号是有利的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Microsaccade detection using pupil and corneal reflection signals
In contemporary research, microsaccade detection is typically performed using the calibrated gaze-velocity signal acquired from a video-based eye tracker. To generate this signal, the pupil and corneal reflection (CR) signals are subtracted from each other and a differentiation filter is applied, both of which may prevent small microsaccades from being detected due to signal distortion and noise amplification. We propose a new algorithm where microsaccades are detected directly from uncalibrated pupil-, and CR signals. It is based on detrending followed by windowed correlation between pupil and CR signals. The proposed algorithm outperforms the most commonly used algorithm in the field (Engbert & Kliegl, 2003), in particular for small amplitude microsaccades that are difficult to see in the velocity signal even with the naked eye. We argue that it is advantageous to consider the most basic output of the eye tracker, i.e. pupil-, and CR signals, when detecting small microsaccades.
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