Browsing by Author "Jang, Yun"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Gaze Attention and Flow Visualization using the Smudge Effect
    (The Eurographics Association, 2019) Yoo, Sangbong; Jeong, Seongmin; Kim, Seokyeon; Jang, Yun; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Many advanced gaze visualization techniques have been developed continuously based on the fundamental gaze visualizations such as scatter plots, attention map, and scanpath. However, it is not easy to locate challenging visualization techniques that resolve the limitations presented in the conventional gaze visualizations. Therefore, in this paper, we propose a novel visualization applying the smudge technique to the attention map. The proposed visualization intuitively shows the gaze flow and AoIs (Area of Interests) of an observer. Besides, it provides fixation, saccade, and micro-movement information, which allows us to respond to various analytical goals within a single visualization. Finally, we provide two case studies to show the effectiveness of our technique.
  • Thumbnail Image
    Item
    A Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLX
    (The Eurographics Association, 2022) Yim, Soobin; Yoon, Chanyoung; Yoo, Sangbong; Jang, Yun; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Mental 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.
  • Thumbnail Image
    Item
    Revisiting Visualization Evaluation Using EEG and Visualization Literacy Assessment Test
    (The Eurographics Association, 2023) Yim, Soobin; Jung, Chanyoung; Yoon, Chanyoung; Yoo, Sangbong; Choi, Seongwon; Jang, Yun; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Using 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.
  • Thumbnail Image
    Item
    Visualization System for Analyzing Congestion Pricing Policies
    (The Eurographics Association, 2023) Choi, SeokHwan; Seo, Seongbum; Yoo, Sangbong; Jang, Yun; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Traffic congestion, which increases every year, has a negative impact on environmental pollution and productivity. Congestion pricing policy has been shown to be effective in Singapore, London, and Stockholm as one of the ways to solve traffic congestion. Pricing policy has different effects depending on a target area, pricing scheme, and toll. In general, congestion pricing policy researchers conduct statistical analysis of simulation model predictions within a fixed region and time range. However, existing research techniques make analyzing all traffic data characteristics with spatiotemporal dependency difficult. In this paper, we propose a visualization system for analyzing the influence of congestion pricing policy using SUMO and TCI. Our system provides a district-level analysis process to explore the influence of pricing policy over time and area.