A Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLX

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dc.contributor.authorYim, Soobinen_US
dc.contributor.authorYoon, Chanyoungen_US
dc.contributor.authorYoo, Sangbongen_US
dc.contributor.authorJang, Yunen_US
dc.contributor.editorKrone, Michaelen_US
dc.contributor.editorLenti, Simoneen_US
dc.contributor.editorSchmidt, Johannaen_US
dc.date.accessioned2022-06-02T15:29:07Z
dc.date.available2022-06-02T15:29:07Z
dc.date.issued2022
dc.description.abstractMental workload is a cognitive effort felt by users while solving tasks, and good visualizations tend to induce a low mental workload. For better visualizations, various visualization techniques have been evaluated through quantitative methods that compare the response accuracy and performance time for completing visualization tasks. However, accuracy and time do not always represent the mental workload of a subject. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The electroencephalogram (EEG) as biosignal is one of the indicators frequently utilized to measure mental workload. Since everyone judges and senses differently, EEG signals and mental workload differ from person to person. In this paper, we propose a mental workload personalized estimation model with EEG data specialized for each individual to evaluate visualizations. We use scatter plot, bar, line, and map visualizations and collect NASA-TLX scores as mental workload and EEG data. NASA-TLX and EEG data as training data are used for the mental workload estimation model.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2022 - Posters
dc.identifier.doi10.2312/evp.20221117
dc.identifier.isbn978-3-03868-185-4
dc.identifier.pages47-49
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20221117
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evp20221117
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Visualization design and evaluation methods; Computing methodologies --> Supervised learning by classification
dc.subjectHuman centered computing
dc.subjectVisualization design and evaluation methods
dc.subjectComputing methodologies
dc.subjectSupervised learning by classification
dc.titleA Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLXen_US
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