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使用压缩指数模型的眼跳轨迹预测

Trajectory prediction of saccadic eye movements using a compressed exponential model.

作者信息

Han Peng, Saunders Daniel R, Woods Russell L, Luo Gang

机构信息

School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, China.

出版信息

J Vis. 2013 Jul 31;13(8):27. doi: 10.1167/13.8.27.

Abstract

Gaze-contingent display paradigms play an important role in vision research. The time delay due to data transmission from eye tracker to monitor may lead to a misalignment between the gaze direction and image manipulation during eye movements, and therefore compromise the contingency. We present a method to reduce this misalignment by using a compressed exponential function to model the trajectories of saccadic eye movements. Our algorithm was evaluated using experimental data from 1,212 saccades ranging from 3° to 30°, which were collected with an EyeLink 1000 and a Dual-Purkinje Image (DPI) eye tracker. The model fits eye displacement with a high agreement (R² > 0.96). When assuming a 10-millisecond time delay, prediction of 2D saccade trajectories using our model could reduce the misalignment by 30% to 60% with the EyeLink tracker and 20% to 40% with the DPI tracker for saccades larger than 8°. Because a certain number of samples are required for model fitting, the prediction did not offer improvement for most small saccades and the early stages of large saccades. Evaluation was also performed for a simulated 100-Hz gaze-contingent display using the prerecorded saccade data. With prediction, the percentage of misalignment larger than 2° dropped from 45% to 20% for EyeLink and 42% to 26% for DPI data. These results suggest that the saccade-prediction algorithm may help create more accurate gaze-contingent displays.

摘要

注视相关显示范式在视觉研究中发挥着重要作用。从眼动仪到显示器的数据传输所导致的时间延迟,可能会在眼球运动期间导致注视方向与图像操作之间出现错位,从而影响这种相关性。我们提出了一种方法,通过使用压缩指数函数对快速眼动的轨迹进行建模来减少这种错位。我们的算法使用从1212次幅度在3°至30°之间的扫视实验数据进行了评估,这些数据是使用EyeLink 1000和双浦肯野图像(DPI)眼动仪收集的。该模型对眼球位移的拟合度很高(R²>0.96)。当假设存在10毫秒的时间延迟时,对于大于8°的扫视,使用我们的模型预测二维扫视轨迹,使用EyeLink眼动仪时可将错位减少30%至60%,使用DPI眼动仪时可减少20%至40%。由于模型拟合需要一定数量的样本,对于大多数小扫视和大扫视的早期阶段,预测并没有带来改善。还使用预先记录的扫视数据对模拟的100赫兹注视相关显示进行了评估。通过预测,对于EyeLink数据,大于2°的错位百分比从45%降至20%,对于DPI数据从42%降至26%。这些结果表明,扫视预测算法可能有助于创建更精确的注视相关显示。

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