Kaczorowska Monika, Plechawska-Wójcik Małgorzata, Tokovarov Mikhail, Krukow Paweł
Department of Computer Science, Lublin University of Technology, 20-618 Lublin, Poland.
Department of Clinical Neuropsychiatry, Medical University of Lublin, 20-439 Lublin, Poland.
Brain Sci. 2022 Apr 23;12(5):542. doi: 10.3390/brainsci12050542.
The study is focused on applying ex-Gaussian parameters of eye-tracking and cognitive measures in the classification process of cognitive workload level. A computerised version of the digit symbol substitution test has been developed in order to perform the case study. The dataset applied in the study is a collection of variables related to eye-tracking: saccades, fixations and blinks, as well as test-related variables including response time and correct response number. The application of ex-Gaussian modelling to all collected data was beneficial in the context of detection of dissimilarity in groups. An independent classification approach has been applied in the study. Several classical classification methods have been invoked in the process. The overall classification accuracy reached almost 96%. Furthermore, the interpretable machine learning model based on logistic regression was adapted in order to calculate the ranking of the most valuable features, which allowed us to examine their importance.
该研究专注于将眼动追踪的高斯分布参数和认知测量应用于认知工作量水平的分类过程。为了进行案例研究，已开发出数字化符号替换测试的计算机版本。该研究中应用的数据集是与眼动追踪相关的变量集合：扫视、注视和眨眼，以及与测试相关的变量，包括反应时间和正确反应数量。在检测组间差异方面，对所有收集的数据应用高斯分布建模是有益的。该研究采用了独立分类方法。在此过程中调用了几种经典分类方法。总体分类准确率几乎达到了96%。此外，采用了基于逻辑回归的可解释机器学习模型来计算最有价值特征的排名，这使我们能够检验它们的重要性。
