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Now showing 1 - 9 of 9

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels
(2014-09-04)
Huang, Chih-Sheng
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Lin, Chun-Ling
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Ko, Li-Wei
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Liu, Shen-Yi
;
Su, Tung-Ping
Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician\'s expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 +/- 4.0 (SD) % [average kappa 0.68 +/- 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.
Developing an EEG-based on-line closed-loop lapse detection and mitigation system
(2014-10-13)
Wang, Yu-Te
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Huang, Kuan-Chih
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Wei, Chun-Shu
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Huang, Teng-Yi
;
Ke, Li-Wei
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects\' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.
A Real-Time Wireless Brain-Computer Interface System for Drowsiness Detection
(2010-08-01)
Lin, Chin-Teng
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Chang, Che-Jui
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Lin, Bor-Shyh
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Hung, Shao-Hang
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Chao, Chih-Feng
A real-time wireless electroencephalogram (EEG)-based brain-computer interface (BCI) system for drowsiness detection has been proposed. Drowsy driving has been implicated as a causal factor in many accidents. Therefore, real-time drowsiness monitoring can prevent traffic accidents effectively. However, current BCI systems are usually large and have to transmit an EEG signal to a back-end personal computer to process the EEG signal. In this study, a novel BCI system was developed to monitor the human cognitive state and provide biofeedback to the driver when drowsy state occurs. The proposed system consists of a wireless physiological signal-acquisition module and an embedded signal-processing module. Here, the physiological signal-acquisition module and embedded signal-processing module were designed for long-term EEG monitoring and real-time drowsiness detection, respectively. The advantages of low ower consumption and small volume of the proposed system are suitable for car applications. Moreover, a real-time drowsiness detection algorithm was also developed and implemented in this system. The experiment results demonstrated the feasibility of our proposed BCI system in a practical driving application.
Mixed Bio-Signal Lossless Data Compressor for Portable Brain-Heart Monitoring Systems
(2011-02-01)
Chua, Ericson
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Fang, Wai-Chi
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
This paper presents a highly integrated VLSI implementation of a mixed bio-signal lossless data compressor capable of handling multichannel electroencephalogram (EEG), electrocardiogram (ECG) and diffuse optical tomography (DOT) bio-signal data for reduced storage and communication bandwidth requirements in portable, wireless brain-heart monitoring systems used in hospital or home care settings. The compressor integrated in a multiprocessor brain-heart monitoring IC comprises 15k gates and 12kbits of RAM, occupying a total area of 58k mu m(2) in 65nm CMOS technology. Results demonstrate an average compression ratio (CR) of 2.05, and a simulated power consumption of 170 mu W at an operating condition of 24MHz clock and 1.0V core voltage. Nominal power savings of 43% and 47% at the transmitter can be achieved when employing Bluetooth and Zigbee transceivers, respectively(1).
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.
Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades
(2012-05-01)
Liao, Lun-De
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Lin, Chin-Teng
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McDowell, Kaleb
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Wickenden, Alma E.
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Gramann, Klaus
The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of 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.
Adaptive EEG-based alertness estimation system by using ICA-based fuzzy neural networks
(2006-11-01)
Lin, Chin-Teng
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Ko, Li-Wei
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Chung, I-Fang
;
Huang, Teng-Yi
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Chen, Yu-Chieh
Drivers' fatigue has been implicated as a causal factor in many accidents. The development of human cognitive state monitoring system for the drivers to prevent accidents behind the steering wheel has become a major focus in the field of safety driving. It requires a technique that can continuously monitor and estimate the alertness level of drivers. 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. An adaptive alertness estimation methodology based on electroencephalogram, power spectrum analysis, independent component analysis (ICA), and fuzzy neural network (FNNs) models is proposed in this paper or continuously monitoring driver's drowsiness level with concurrent changes in the alertness level. A novel adaptive feature selection mechanism is developed for automatically selecting effective frequency bands of ICA components for realizing an on-line alertness monitoring system based on the, correlation analysis between the time-frequency power spectra of ICA components and the driving errors defined as the deviation between the center of the vehicle and the cruising lane in the virtual-reality driving environment. The mechanism also provides effective and efficient features that can be fed into ICA-mixture-model-based self-constructing FNN to indirectly estimate driver's drowsiness level expressed by approximately and predicting the driving error.
Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning
(2008-05-01)
Lin, Chin-Teng
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Chen, Yu-Chieh
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Huang, Teng-Yi
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Chiu, Tien-Ting
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Ko, Li-Wei
Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

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