Yim, SoobinJung, ChanyoungYoon, ChanyoungYoo, SangbongChoi, SeongwonJang, YunChaine, RaphaëlleDeng, ZhigangKim, Min H.2023-10-092023-10-092023978-3-03868-234-9https://doi.org/10.2312/pg.20231289https://diglib.eg.org:443/handle/10.2312/pg20231289Using EEG signals, also known as Electroencephalogram, can provide a quantitative measure of human cognitive load, making it an effective tool for evaluating visualization. However, the suitability of EEG for visualization evaluation has not been verified in previous studies. This paper investigates the feasibility of utilizing EEG data in visualization evaluation by comparing previous experiments. We trained and estimated individual CNN models for each subject using the EEG data. Our study demonstrates that EEG-based visualization evaluation provides a more feasible estimate of the difficulties experienced by subjects during the visualization task compared to previous studies that used accuracy and response time.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing -> Visualization design and evaluation methodsHuman centered computingVisualization design and evaluation methods10.2312/pg.20231289127-1282 pages