Browsing by Author "Chen, Yu-Chieh"

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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
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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 a Multisite, Closed-loop Neuromodulator for the Theranosis of Neural Degenerative Diseases
(2018-01-01)
Chen, Hsin
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Chang, Yen-Chung
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Yeh, Shih-Rung
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Hsieh, Chih-Cheng
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Tang, Kea-Tiong
Stimulating specific brain regions has been found useful for treating neural disorders, such as the Parkinson's disease, epilepsy, depression, etc. However, how electrical stimulation modulates neural activities remains not fully understood. As animal models provide the advantage of recording and stimulating different disease-related regions simultaneously, this paper introduces the latest development of a multisite, closed-loop-controlled microsystem for investigating novel treatments on neural degenerative diseases with freely-moving rats. The algorithms for recognizing pathological neural activities automatically are also developed and realized in hardware, so as to control the stimulation in a closed loop and in real time. The pilot studies on the efficacy of treating the Parkinson's disease with closed-loop-controlled stimulation will be presented and discussed. Finally, the feasibility of modelling and probing how neural dynamics and connectivity are modulated by stimulation will be an important topic for future research.
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.
EEG effects of motion sickness induced in a dynamic virtual reality environment
(2007)
Lin, Chin-Teng
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Chuang, Shang-Wen
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Chen, Yu-Chieh
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Ko, Li-Wei
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Liang, Sheng-Fu
The Electroencephalogram (EEG) dynamics which relate to motion sickness are studied in this paper. Instead of providing visual or motion stimuli to the subjects to induce motion sickness, we employed a dynamic virtual-reality (VR) environment in our research. The environment consisted of a 3D surrounding VR scene and a motion platform providing a realistic situation. This environment provided the advantages of safety, low cost, and the realistic stimuli to induce motion sickness. The Motion Sickness Questionnaire (MSQ) was used to assess the sickness level, and the EEG effects on the subjects with high sickness levels were investigated using the independent component analysis (ICA). The fake-epoch extraction was then applied to the nausea-related independent components. Finally we employed the Event-Related Spectral Perturbation (ERSP) technology on the fake-epochs in order to determine the EEG dynamics during motion sickness. The experimental results show that most subjects experienced an 8-10 Hz power increase to their motion sickness-related phenomena in the parietal and motor areas. Moreover, some subjects experienced an EEG power increase of 18-20 Hz in their synchronized responses recorded in the same areas. The motion sickness-related effects and regions can be successfully obtained from our experimental results.
EEG-Based assessment of driver cognitive responses in a dynamic virtual-reality driving environment
(2007-07-01)
Lin, Chin-Teng
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Chung, I-Fang
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Ko, Li-Wei
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Chen, Yu-Chieh
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Liang, Sheng-Fu
Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.
Motion-Sickness Related Brain Areas and EEG Power Activates
(2009)
Chen, Yu-Chieh
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Duann, Jeng-Ren
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Lin, Chun-Ling
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Chuang, Shang-Wen
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Jung, Tzyy-Ping
This study investigates electroencephalographic (EEG) correlates of motion sickness in a virtual-reality based driving simulator. The driving simulator comprised an actual automobile mounted on a Stewart motion platform with six degrees of freedom, providing both visual and vestibular stimulations to induce motion-sickness in a manner that is close to that in daily life. EEG data were acquired at a sampling rate of 500 Hz using a 32-channel EEG system. The acquired EEG signals were analyzed using independent component analysis (ICA) and time-frequency analysis to assess EEG correlates of motion sickness. Subject's degree of motion-sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop psychophysical references to the recorded EEG changes. Five Motion-sickness related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. These components exhibited distinct spectral suppressions or augmentation in motion sickness. The results of this study could lead to a practical human-machine interface for noninvasive monitoring of motion sickness of drivers or passengers in real-world environments.
Real-time pedestrian detection system with novel thermal features at night
(2014-01-01)
Lin, Chun-Fu
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Lin, Sheng-Fuu
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Hwang, Chi-Hung
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Chen, Yu-Chieh
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電控工程研究所
Since the need of pedestrian detection at night has gained more and more interest, real-time pedestrian detection system at night has begun to attract significant attention. However, the detection performance of traditional pedestrian detection system at night is still insufficient since region of interests(ROI) generation and feature extraction are designed separately and less relative. This study presents a novel pedestrian detection algorithm and the corresponding system architecture for the real-time pedestrian detection system at night. The presented pedestrian detection algorithm uses novel outline features which have strong representative for the forward direction of pedestrian. A ROI generation method which is closely related to the outline features is presented to improve the detection accuracy. A three-layer back-propagation feed-forward neural network is applied as the classifier. Since the system architecture is efficiently and the calculation of the outline feature values is simple, the presented pedestrian detection system can operate in real-time. Experimental results show that the presented outline features are significantly effective and the detection performance of pedestrian detection system at night is improved.
Spatial and temporal EEG dynamics of motion sickness
(2010-02-01)
Chen, Yu-Chieh
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Duann, Jeng-Ren
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Chuang, Shang-Wen
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Lin, Chun-Ling
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Ko, Li-Wei
This study investigates motion-sickness-related brain responses using a VR-based driving simulator oil a motion platform with six degrees of freedom, which provides both visual and vestibular stimulations to induce motion sickness in a manner that is close to that in daily life. Subjects' brain dynamics associated with motion sickness were measured using a 32-channel EEG system. Their degree of motion sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop behavioral references to the recorded EEG changes The acquired EEG signals were parsed by independent component analysis (ICA) into maximally independent processes. The decomposition enables the brain dynamics that are induced by the motion of the platform and motion sickness to be disassociated. Five MS-related brain processes with equivalent dipoles located in the left motor, the parietal. the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. The parietal and motor components exhibited significant alpha power suppression in response to vestibular stimuli, while the occipital components exhibited MS-related power augmentation in mainly theta and delta bands: the occipital midline components exhibited a broadband power increase Further, time series cross-correlation analysis was employed to evaluate relationships between the spectral changes associated with different brain processes and the degree of motion sickness According to our results, it is suggested both visual and vestibular stimulations should be used to induce motion sickness in brain dynamic studies (C) 2009 Elsevier Inc. All rights reserved.
Structure design criteria of dual-channel high mobility electron transistors
(2007-01-01)
Lin, Jia-Chuan
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Chen, Yu-Chieh
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Tsai, Wei-Chih
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Yang, Po-Yu
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光電工程學系
The design criteria of dual-channel high electron mobility transistor (DHEMT) are proposed in this study. 8-Doped In(0.52)Al(0.48)As/ In(0.53)Ga(0.47)As/InP material systems are concentrated in this article. The DHEMT structures are explored numerically and compared with conventional single-channel high electron mobility transistor (SHEMT) structures. Some criteria of doping concentration and layer structure design are proposed. The simulation results reveal that DHEMT has a larger voltage swing, a lower gate leakage current, a better carrier confinement, a higher density of two-dimensional electron gas (2DEG) and an excellent transconductance than SHENIT. (c) 2006 Elsevier Ltd. All rights reserved.