A Mental Workload Estimation Model for Visualization Using EEG

Simple item page
dc.contributor.authorYim, Soobin
dc.contributor.authorYoon, Chanyong
dc.contributor.authorYoo, Sangbong
dc.contributor.authorJang, Yun
dc.date.accessioned2022-12-27T18:57:36Z
dc.date.available2022-12-27T18:57:36Z
dc.date.issued2023-01-03
dc.description.abstractVarious 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.150
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.othercd5618dc-b7fd-4945-b066-55f5acd18145
dc.identifier.urihttps://hdl.handle.net/10125/102780
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectInteractive Visual Analytics for Knowledge Integration and Decision Intelligence
dc.subjectmachine learning techniques ; other topics and techniques ; guidelines
dc.titleA Mental Workload Estimation Model for Visualization Using EEG
dc.type.dcmitext
prism.startingpage1216

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0118.pdf
Size:
32.36 MB
Format:
Adobe Portable Document Format
Download

Collections

Interactive Visual Analytics for Knowledge Integration and Decision Intelligence