Yim, SoobinYoon, ChanyoungYoo, SangbongJang, YunKrone, MichaelLenti, SimoneSchmidt, Johanna2022-06-022022-06-022022978-3-03868-185-4https://doi.org/10.2312/evp.20221117https://diglib.eg.org:443/handle/10.2312/evp20221117Mental 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.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visualization design and evaluation methods; Computing methodologies --> Supervised learning by classificationHuman centered computingVisualization design and evaluation methodsComputing methodologiesSupervised learning by classification10.2312/evp.2022111747-493 pages