Zibandehpoor Mobina, Alizadehziri Fatemeh, Larki Arash Abbasi, Teymouri Sobhan, Delrobaei Mehdi
Mechatronics Department, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, 1631714191, Iran.
Sci Data. 2025 Apr 1;12(1):553. doi: 10.1038/s41597-025-04879-z.
In the quest for understanding human executive function, eye movements represent a unique insight into how we process and comprehend our environment. Eye movements reveal patterns in how we focus, navigate, and make decisions across various contexts. The proposed dataset includes electrooculography (EOG) signals from 27 healthy subjects, capturing both vertical and horizontal eye movements. The recorded signals were obtained during the video-watching stage of the Leiden Navigation Test, designed to assess spatial navigation abilities. In addition to other data, the dataset includes scores from the Mini-Mental State Examination and the Wayfinding Questionnaire. The dataset comprises carefully curated components, including relevant information, the Mini-Mental State Examination scores, and the Wayfinding Questionnaire scores, encompassing navigation, orientation, distance estimation, spatial anxiety, as well as raw and processed EOG signals. These assessments contribute more information about the participants' cognitive function and navigational abilities. This dataset can be valuable for researchers investigating spatial navigation abilities through EOG signal analysis.
在探索人类执行功能的过程中,眼球运动为我们理解如何处理和感知周围环境提供了独特视角。眼球运动揭示了我们在各种情境下聚焦、导航和做出决策的模式。所提议的数据集包含来自27名健康受试者的眼电图(EOG)信号,记录了垂直和水平方向的眼球运动。这些记录的信号是在莱顿导航测试的视频观看阶段获取的,该测试旨在评估空间导航能力。除其他数据外,该数据集还包括简易精神状态检查表和寻路问卷的得分。该数据集由精心策划的组件组成,包括相关信息、简易精神状态检查表得分和寻路问卷得分,涵盖导航、定向、距离估计、空间焦虑以及原始和处理后的EOG信号。这些评估提供了更多关于参与者认知功能和导航能力的信息。该数据集对于通过EOG信号分析研究空间导航能力的研究人员可能具有重要价值。