Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Various visualization design guides have been proposed and evaluated through quantitative methods that compare the response accuracy and time for completing visualization tasks. However, accuracy and time do not always represent the mental workload. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The EEG as biosignal is one of the indicators frequently utilized to measure mental workload. Nevertheless, many studies have not applied the EEG for mental workload measurement in the visualization evaluation. In this work, we study the EEG to measure mental workload for visualization evaluation. We examine whether there is a difference in mental workload for the visualization designs suggested by the previously proposed visualization design guides. Besides, we propose a mental workload estimation model using EEG data specialized for each individual to evaluate visualization designs.
Recommended Citation
Yim, Soobin; Yoon, Chanyong; Yoo, Sangbong; and Jang, Yun, "A Mental Workload Estimation Model for Visualization Using EEG" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/da/visual_analytics/2
A Mental Workload Estimation Model for Visualization Using EEG
Online
Various visualization design guides have been proposed and evaluated through quantitative methods that compare the response accuracy and time for completing visualization tasks. However, accuracy and time do not always represent the mental workload. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The EEG as biosignal is one of the indicators frequently utilized to measure mental workload. Nevertheless, many studies have not applied the EEG for mental workload measurement in the visualization evaluation. In this work, we study the EEG to measure mental workload for visualization evaluation. We examine whether there is a difference in mental workload for the visualization designs suggested by the previously proposed visualization design guides. Besides, we propose a mental workload estimation model using EEG data specialized for each individual to evaluate visualization designs.
https://aisel.aisnet.org/hicss-56/da/visual_analytics/2