Browsing by Author "Cherng, Fu-Yin"

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A Visual Attention Monitor Based on Steady-State Visual Evoked Potential
(2016-03)
Lee, Yi-Chieh
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Lin, Wen-Chieh
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Cherng, Fu-Yin
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Ko, Li-Wei
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生物科技學系
Attention detection is important for many applications. Automatic determination of users\' visual attention state is challenging because attention involves numerous complex and internal human cognitive functions. Behavioral observations, such as eye gaze or response to external stimuli, can provide clues for users\' visual attention state; however, users\' cognitive state cannot be easily known. Conventional electroencephalography-based methods detect attention by observing the dynamic changes in the frontal lobe of the brain, especially in the anterior cingulate cortex (ACC). However, that area in the brain is associated with many functions, some of which correlate with conscious experience but are not directly related to attention. In this paper, we design an attention monitoring system to detect whether the brain experiences a visual stimulus consciously. Our experiments verified the feasibility of our design, and the average classification rate ranged from 72% to 82%.
An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance
(2015)
Cherng, Fu-Yin
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Lin, Wen-Chieh
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King, Jung-Tai
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Lee, Yi-Chen
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資訊工程學系
Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users\' interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users\' different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users\' EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants\' perception during these tasks, and identify some important implications for icon design.
An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance
(2016)
Cherng, Fu-Yin
;
Lin, Wen-Chieh
;
King, Jung-Tai
;
Lee, Yi-Chen
;
資訊工程學系
Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users\' interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users\' different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users\' EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants\' perception during these tasks, and identify some important implications for icon design.
An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control
(2014-01-01)
Kuo, Chih-En
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Liang, Sheng-Fu
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Lee, Yi-Chieh
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Cherng, Fu-Yin
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Lin, Wen-Chieh
Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOG-based sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83% accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.
Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask
(2015-11-01)
Liang, Sheng-Fu
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Kuo, Chin-En
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Lee, Yi-Chieh
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Lin, Wen-Chieh
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Liu, Yen-Chen
Health care researchers have recently developed several home-based sleep monitoring systems and mobile applications that support healthy sleep by monitoring a user\'s sleep environment, activities, or sleep quality. This study describes the design and evaluation of an electrooculogram (EOG)-based system for an automatic sleep monitoring. Compared with polysomnogram or electroencephalogram recordings, EOG has the advantage of easy placement and can be operated by the users themselves. We also design an intelligent eye mask that is user friendly for measuring sleep stage and quality. Two user experiments were carried out to demonstrate that the proposed system produces valid measurements of sleep stage and sleep quality and has good usability and reliability while not disturbing sleep significantly. These findings suggest that our system can also be applied to long-term sleep monitoring or sleep environment control to improve the user\'s sleep quality and make sleep more comfortable.
EEG-based Measures of Auditory Saliency in a Complex Context
(2019-01-01)
Huang, Xun-Yi
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Cherng, Fu-Yin
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King, Jung-Tai
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Lin, Wen-Chieh
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資訊工程學系
Auditory saliency is an important mechanism that helps humans extract relevant information from environments. Audio notifications of mobile devices with high saliency can increase users' receptivity, yet overly high saliency could cause annoyance. Accurately measuring auditory saliency of a notification is critical for evaluating its usability. Previous studies adopted behavioral methods. However, their results may not accurately reflect auditory saliency as humans' perception of auditory saliency often involves complicated cognitive processes. Thus, we propose an electroencephalography (EEG)-based approach that can complement behavioral studies to provide a more nuanced analysis of auditory saliency. We evaluated our method by conducting an EEG experiment that measured the mismatch negativity and P3a of the sounds in realistic scenarios. We also conducted a behavioral experiment to link the EEG-based method with the behavioral method. The results suggested that EEG can provide detailed information about how human perceive auditory saliency and complement the behavioral measures.
Embodying Historical Learners' Messages as Learning Companions in a VR Classroom
(2019-01-01)
Liao, Meng-Yun
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Sung, Ching-Ying
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Wang, Hao-Chuan
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Lin, Wen-Chieh
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Cherng, Fu-Yin
Online learning platforms such as MOOCs have been prevalent sources of self-paced learning to people nowadays. However, the lack of peer accompaniment and social interaction may increase learners' sense of isolation. Prior studies have shown the positive effects on visualizing peer students' appearances in VR learning environments. In this work, we propose to build virtual classmates, which were constructed by synthesizing previous learners' time-anchored messages. Configurations of virtual classmates and their behavioral features can be adjusted. To build the characteristics of virtual classmates, we developed a technique called comment mapping to aggregate prior online learners' comments to shape virtual classmates' behaviors. We evaluated the effects of the virtual classmates built with and without the comment mapping and the amount of virtual classmates rendered in VR.
Evaluating 2D Flow Visualization Using Eye Tracking
(2015-06-01)
Ho, Hsin-Yang
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Yeh, I-Cheng
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Lai, Yu-Chi
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Lin, Wen-Chieh
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Cherng, Fu-Yin
Flow visualization is recognized as an essential tool for many scientific research fields and different visualization approaches are proposed. Several studies are also conducted to evaluate their effectiveness but these studies rarely examine the performance from the perspective of visual perception. In this paper, we aim at exploring how users\' visual perception is influenced by different 2D flow visualization methods. An eye tracker is used to analyze users\' visual behaviors when they perform the free viewing, advection prediction, flow feature detection, and flow feature identification tasks on the flow field images generated by different visualizations methods. We evaluate the illustration capability of five representative visualization algorithms. Our results show that the eye-tracking-based evaluation provides more insights to quantitatively analyze the effectiveness of these visualization methods.
Exploring Online Learners' Interactive Dynamics by Visually Analyzing Their Time-anchored Comments
(2017-10-01)
Sung, Ching-Ying
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Huang, Xun-Yi
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Shen, Yicong
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Cherng, Fu-Yin
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Lin, Wen-Chieh
MOOCs (Massive Open Online Courses) are increasingly prevalent as an online educational resource open to everyone and have attracted hundreds of thousands learners enrolling these online courses. At such scale, there is potentially rich information of learners' behaviors embedded in the interactions between learners and videos that may help instructors and content producers adjust the instructions and refine the online courses. However, the lack of tools to visualize information from interactive data, including messages left to the videos at particular timestamps as well as the temporal variations of learners' online participation and perceived experience, has prevented people from gaining more insights from video-watching logs. In this paper, we focus on extracting and visualizing useful information from time-anchored comments that learners left to specific time points of the videos when watching them. Timestamps as a kind of metadata of messages can be useful to recover the interactive dynamics of learners occurring around the videos. Therefore, we present a visualization system to analyze and categorize time-anchored comments based on topics and content types. Our system integrates visualization methods of temporal text data, namely ToPIN and ThemeRiver, which can help people understand the quality and quantity of online learners' feedback and their states of learning. To evaluate the proposed system, we visualized time-anchored commenting data from two online course videos, and conducted two user studies participated by course instructors and third-party educational evaluators. The results validate the usefulness of the approach and show how the quantitative and qualitative visualizations can be used to gain interesting insights around learners' online learning behaviors.
Measuring the Influences of Musical Parameters on Cognitive and Behavioral Responses to Audio Notifications Using EEG and Large-scale Online Studies
(2019-01-01)
Cherng, Fu-Yin
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Lee, Yi-Chen
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King, Jung-Tai
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Lin, Wen-Chieh
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資訊工程學系
Prior studies have evaluated various designs for audio notifications. However, calls for more in-depth research on how such notifications work, especially at the level of users' cognitive states, have gone unanswered; and studies evaluating audio notifications with large numbers of participants in multiple environments have been rare. This study conducted an electroencephalography study (N=20) and an online study (N=967) to enhance understandings of how three musical parameters - melody (simple, complex), pitch (high, low), and tempo (fast, slow) - influenced users' cognition and behaviors. There are eight different notifications with different combinations of these parameters. The online study analyzed the effects of user-specific and environmental information on users' behaviors while they listened to these notifications. The results revealed that tempo and pitch have the main effect on the speed and strength (accuracy) of users' cognition and behaviors. The users' characteristics and environments influenced the effects of these musical parameters.
To Repeat or Not to Repeat? Redesigning Repeating Auditory Alarms Based on EEG Analysis
(2019-01-01)
Lee, Yi-Chen
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Cherng, Fu-Yin
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King, Jung-Tai
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Lin, Wen-Chieh
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資訊工程學系
Auditory alarms that repeatedly interrupt users until they react are common, especially in the context of alarms. However, when an alarm repeats, our brains habituate to it and perceive it less and less, with reductions in both perception and attention-shifting: a phenomenon known as the repetition-suppression effect (RS). To retain users' perception and attention, this paper proposes and tests the use of pitch-and intensity-modulated alarms. Its experimental findings suggest that the proposed modulated alarms can reduce RS, albeit in different patterns, depending on whether pitch or intensity is the focus of the modulation. Specifically, pitch-modulated alarms were found to reduce RS more when the number of repetitions was small, while intensity-modulated alarms reduced it more as the number of repetitions increased. Based on these results, we make several recommendations for the design of improved repeating alarms, based on which modulation approach should be adopted in various situations.
Using Time-Anchored Peer Comments to Enhance Social Interaction in Online Educational Videos
(2015-01-01)
Lee, Yi-Chieh
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Lin, Wen-Chieh
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Cherng, Fu-Yin
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Wang, Hao-Chuan
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Sung, Ching-Ying
Online learning is increasingly prevalent as an option for self-learning and as a resource for instructional design. Prerecorded video is currently the main medium of online education content delivery and instruction; this affords asynchronicity and flexibility, and enables the dissemination of lecture content in a distributed and scalable manner. However, the same properties may impede learners' engagement due to the lack of social interaction and peer support. In this paper, we propose a time-anchored commenting interface to allow online learners who watch the same video clips to exchange comments on them. Comments left by previous learners at specific time points of a video are displayed to new learners when they watch the same video and reach those time points. We investigated how the display of time-anchored comments (dynamic or static) and type of comments (content-related or social-oriented) influenced users' perceived engagement, perceived social interactivity, and learning outcomes. Our results show that dynamically displaying time-anchored comments can indeed enhance learners' perceived social interactivity. Moreover, the content of comments would further affect learners' intention of commenting. Based on our findings, we make various recommendations for the improvement of social interaction and learning experience in online education.