Browsing by Author "Lin, Fu-Chang"

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Brain Computer Interface-Based Smart Living Environmental Auto-Adjustment Control System in UPnP Home Networking
(2014-06-01)
Lin, Chin-Teng
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Lin, Bor-Shyh
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Lin, Fu-Chang
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Chang, Che-Jui
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影像與生醫光電研究所
A brain computer interface-based smart living environmental auto-adjustment control system (BSLEACS) is proposed in this paper. Recently, many environmental control systems have been proposed to improve human quality of life. However, little research has focused on environmental control directly using the human physiological state. Based on the advantage of our technique on brain computer interface (BCI), we integrated the BCI technique with universal plug and play (UPnP) home networking for smart house applications. BSLEACS mainly consists of a wireless physiological signal acquisition module, an embedded signal processing module, a simple control protocol/power line communication environmental controller, and a host system. Here, the physiological signal acquisition module and embedded signal processing module were designed for long-term electroencephalogram (EEG) monitoring and backend analysis, respectively. The advantages of low power consumption and small volume of the above modules are suitable for smart house applications in daily life. Moreover, different from other BCI systems, the property of using only a single EEG channel to monitor cognitive state also makes BSLEACS become more practicable. BSLEACS has been verified in a practical demo room, and the environmental adjustment can be automatically controlled by the change of the user\'s cognitive state. BSLEACS provides a novel system prototype for environmental control, and can be simply extended and integrated with the UPnP home networking for other applications.
EEG-based Brain-computer Interface for Smart Living Environment Auto-adjustment
(2010)
Lin, Chin-Teng
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Lin, Fu-Chang
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Chen, Shi-An
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Lu, Shao-Wei
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Chen, Te-Chi
An EEG-based smart living environmental control system to auto-adjust the living environment is proposed in this study. Many environmental control systems have been proposed to improve human life quality in recent years. However, there is little research focused on environment control by using a human's physiological state directly. Even though some studies have proposed brain computer interface-based (BCI-based) environmental control systems, most of them encountered signal quality decline during long-term physiological monitoring with conventional wet electrodes. Moreover, such BCI-based environmental control systems are actively controlled by users; less close-loop feedback capability can be provided between environment and user for automation. Based on the advance of our technique for BCI and the improvement of micro-electro-mechanical-system-based (MEMS-based) dry electrode sensors, we combined these techniques to demonstrate an auto-adjustable living environment control system, e. g., illumination of light and fan speed of air conditioner, depends on the physiological change of the user, even for long-term physiological monitoring. The system is structured with five units: a wireless portable EEG acquisition circuit unit, an interactive flow control unit with a real-time physiology signal processing unit, which is implemented on a dual-core processor, an environment controller unit and a host system for data storage and display. The proposed system has been verified in a simulated environment and the experimental results show that the air conditioner and the lights can be successfully and automatically adjusted in real-time based on the subject's physiological changes, which indicate the proposed system can be implemented and constructed in the practical smart living environment or for other applications.
EEG-based Cognitive State Monitoring and Predition by Using the Self-Constructing Neural Fuzzy System
(2010)
Lin, Fu-Chang
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Ko, Li-Wei
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Chen, Shi-An
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Chen, Ching-Fu
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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.
Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System
(2012-09-01)
Lin, Fu-Chang
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Ko, Li-Wei
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Chuang, Chun-Hsiang
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Su, Tung-Ping
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Lin, Chin-Teng
A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy 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 difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value <0.038). Besides, the proposed EEG-based Self-organizing 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.