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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chen, Shi-AnSubject: search.filters.subject.EEG

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Now showing 1 - 4 of 4
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    EEG-based Cognitive State Monitoring and Predition by Using the Self-Constructing Neural Fuzzy System
    (2010)
    Lin, Fu-Chang
    ;
    Ko, Li-Wei
    ;
    Chen, Shi-An
    ;
    Chen, Ching-Fu
    ;
    Lin, Chin-Teng
    Driver's cognitive state monitoring has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. In our past studies, we found that the EEG power spectrum changes were highly correlated with the driver's driving behavior performance. In this study, we attempt to construct an EEG-based self-constructing neural fuzzy system to monitor and predict the driver's cognitive state. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. Our experimental results including correlation and prediction show that the performances of our proposed system are significantly higher than using the traditional neural networks. Besides, the proposed EEG-based self-constructing neural fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.
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    The Influence of Acute Stress on Brain Dynamics
    (2013)
    Lin, Chin-Teng
    ;
    Wang, Yu-Kai
    ;
    Fan, Jhe-Wei
    ;
    Chen, Shi-An
    ;
    資訊工程學系
    Living under high stress may be unhealthy. This study explores electroencephalography (EEG) correlated with stressful circumstances by using the task-switching paradigm with feedback information. According to the behavioral and physiological evidence, acute stress created by this paradigm affected the performance of participants. Under stress, the participants responded quickly and inaccurately. The EEG results correlated with acute stress were found in the frontal midline cortex, especially on the theta and alpha bands. These specific factors are considered importance features for detecting the influence of stress by applying various machine-learning methods and neuro-fuzzy systems. This comprehensive study can provide knowledge for studying stress and designing Brain-Computer Interface (BCI) systems in the future.
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    EEG-Based Motion Sickness Estimation Using Principal Component Regression
    (2011)
    Ko, Li-Wei
    ;
    Wei, Chun-Shu
    ;
    Chen, Shi-An
    ;
    Lin, Chin-Teng
    ;
    腦科學研究中心
    Driver's cognitive state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the limitation in developing this system is lack of a major indicator which can be applied to a realistic application. In our past studies, we investigated the physiological changes in the transition of driver's cognitive state by using EEG power spectrum analysis and found that the features in the occipital area were highly correlated with the driver's driving performance. In this study, we construct an EEG-based self-constructed neural fuzzy system to estimate the driver's cognitive state by using the EEG features from the occipital area. Experimental results show that the proposed system had the better performance than other neural networks. Moreover, the proposed system can not only be limited to apply to individual subjects but also sufficiently works in between subjects.
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    An EEG-Based Brain-Computer Interface for Dual Task Driving Detection
    (2011)
    Lin, Chin-Teng
    ;
    Wang, Yu-Kai
    ;
    Chen, Shi-An
    ;
    腦科學研究中心
    ;
    Brain Research Center
    A novel detective model for driver distraction was proposed in this study. Driver distraction is a significant cause of traffic accidents during these years. To study human cognition under a specific driving task, one virtual reality (VR)-based simulation was built. Unexpected car deviations and mathematics questions with stimulus onset asynchrony (SOA) were designed. Electroencephalography (EEG) is a good index for the distraction level to monitor the effects of the dual tasks. Power changing in Frontal and Motor cortex were extracted for the detective model by independent component analysis (ICA). All distracting and non-distracting EEG epochs could be revealed the existence by self-organizing map (SOM). The results presented that this system approached about 90% accuracy to recognize the EEG epochs of non-distracting driving, and might be practicable for daily life.