König Seth D, Buffalo Elizabeth A
Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA; Yerkes National Primate Research Center, 954 Gatewood Road, Atlanta, GA 30329, USA; Graduate Program in Neurobiology and Behavior, University of Washington, Seattle, WA 98195, USA.
Yerkes National Primate Research Center, 954 Gatewood Road, Atlanta, GA 30329, USA; Department of Neurology, Emory University School of Medicine, 1440 Clifton Road, Atlanta, GA 30322, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA.
J Neurosci Methods. 2014 Apr 30;227:121-31. doi: 10.1016/j.jneumeth.2014.01.032. Epub 2014 Feb 6.
Eye tracking is an important component of many human and non-human primate behavioral experiments. As behavioral paradigms have become more complex, including unconstrained viewing of natural images, eye movements measured in these paradigms have become more variable and complex as well. Accordingly, the common practice of using acceleration, dispersion, or velocity thresholds to segment viewing behavior into periods of fixations and saccades may be insufficient.
Here we propose a novel algorithm, called Cluster Fix, which uses k-means cluster analysis to take advantage of the qualitative differences between fixations and saccades. The algorithm finds natural divisions in 4 state space parameters-distance, velocity, acceleration, and angular velocity-to separate scan paths into periods of fixations and saccades. The number and size of clusters adjusts to the variability of individual scan paths.
Cluster Fix can detect small saccades that were often indistinguishable from noisy fixations. Local analysis of fixations helped determine the transition times between fixations and saccades.
Because Cluster Fix detects natural divisions in the data, predefined thresholds are not needed.
A major advantage of Cluster Fix is the ability to precisely identify the beginning and end of saccades, which is essential for studying neural activity that is modulated by or time-locked to saccades. Our data suggest that Cluster Fix is more sensitive than threshold-based algorithms but comes at the cost of an increase in computational time.
眼动追踪是许多人类和非人类灵长类动物行为实验的重要组成部分。随着行为范式变得越来越复杂，包括对自然图像的无约束观看，在这些范式中测量的眼动也变得更加多变和复杂。因此，使用加速度、离散度或速度阈值将观看行为划分为注视期和扫视期的常见做法可能并不充分。
在此，我们提出了一种名为“聚类注视”的新算法，该算法使用k均值聚类分析来利用注视和扫视之间的定性差异。该算法在距离、速度、加速度和角速度这4个状态空间参数中找到自然划分，以将扫描路径分离为注视期和扫视期。聚类的数量和大小会根据个体扫描路径的变异性进行调整。
“聚类注视”可以检测到通常与有噪声的注视难以区分的小扫视。对注视的局部分析有助于确定注视和扫视之间的转换时间。
由于“聚类注视”检测数据中的自然划分，因此不需要预定义阈值。
“聚类注视”的一个主要优点是能够精确识别扫视的开始和结束，这对于研究由扫视调制或与扫视时间锁定的神经活动至关重要。我们的数据表明，“聚类注视”比基于阈值的算法更敏感，但代价是计算时间增加。
