title: Statistical modeling for visualization evaluation through data fusion.
abstract: There is a high demand of data visualization providing insights to users in various applications. However, a consistent, online visualization evaluation method to quantify mental workload or user preference is lacking, which leads to an inefficient visualization and user interface design process. Recently, the advancement of interactive and sensing technologies makes the electroencephalogram (EEG) signals, eye movements as well as visualization logs available in user-centered evaluation. This paper proposes a data fusion model and the application procedure for quantitative and online visualization evaluation. 15 participants joined the study based on three different visualization designs. The results provide a regularized regression model which can accurately predict the user's evaluation of task complexity, and indicate the significance of all three types of sensing data sets for visualization evaluation. This model can be widely applied to data visualization evaluation, and other user-centered designs evaluation and data analysis in human factors and ergonomics.
authors.affiliation: Laboratory of Data Science and Visualization, Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061-0001, USA.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Chen
authors.affiliation: Laboratory of Data Science and Visualization, Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061-0001, USA. Electronic address: jran5@vt.edu.
authors.forename: Ran
authors.initials: R
authors.lastname: Jin
mesh_terms: D003625:Data Collection / Q000379:methods*; D004569:Electroencephalography; D006804:Ergonomics / Q000379:methods*; D053483:Eye Movement Measurements; D005133:Eye Movements; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D013647:Task Performance and Analysis
keywords: Data fusion; Data visualization; Electroencephalogram (EEG); Eye tracking; User-centered designs; Visualization evaluation