Browsing by Author "Lee, Yi-Chieh"

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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 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.
Exploration of User's Mental State Changes during Performing Brain-Computer Interface
(2020-06-01)
Ko, Li-Wei
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Chikara, Rupesh Kumar
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Lee, Yi-Chieh
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Lin, Wen-Chieh
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交大名義發表
Substantial developments have been established in the past few years for enhancing the performance of brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user's mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user's visual area. BCI user's cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users' physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user's cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1-4 Hz), theta (4-7 Hz), and beta (13-30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1-4 Hz), alpha (8-12 Hz), and beta (13-30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user's cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.
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.