Browsing by Author "Chao, Chih-Feng"

Now showing 1 - 8 of 8

A Real-Time Wireless Brain-Computer Interface System for Drowsiness Detection
(2010-08-01)
Lin, Chin-Teng
;
Chang, Che-Jui
;
Lin, Bor-Shyh
;
Hung, Shao-Hang
;
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.
ASSESSING EFFECTIVENESS OF VARIOUS AUDITORY WARNING SIGNALS IN MAINTAINING DRIVERS' ATTENTION IN VIRTUAL REALITY-BASED DRIVING ENVIRONMENTS
(2009-06-01)
Lin, Chin-Teng
;
Chiu, Tien-Ting
;
Huang, Teng-Yi
;
Chao, Chih-Feng
;
Liang, Wen-Chieh
Drivers' fatigue contributes to traffic accidents, so drivers must maintain adequate alertness. The effectiveness of audio alarms in maintaining driving performance and characteristics of alarms was studied in a virtural reality-based driving environment. Response time to the car's drifting was measured under seven conditions: with no warnings and with continuous warning tones (500 Hz, 1750 Hz, and 3000 Hz), and with tone bursts at 500 Hz, 1750 Hz, and 3000 Hz. Analyses showed the audio warning signals significantly improved driving. Further, the tones' spectral characteristics significantly influenced the effectiveness of the warning.
Development of Real-time Wireless Brain Computer Interface for Drowsiness Detection
(2010)
Hung, Shao-Hang
;
Chang, Che-Jui
;
Chao, Chih-Feng
;
Wang, I-Jan
;
Lin, Chin-Teng
In this study, a real-time wireless embedded EEG-based brain computer interface (BCI) system was developed for drowsiness detection in a realistic driving task. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Therefore, real-time drowsiness monitoring is important to avoid traffic accidents. In this study, an embedded EEG-based BCI system which includes a wireless physiological signal acquisition module and an embedded signal processing module was designed, and a real-time drowsiness detection algorithm based on our unsupervised approach was implemented in the embedded signal processing module. EEG signal would be monitored and analyzed by the embedded signal processing module, and the warning tone would be triggered to prevent traffic accidents when the drowsiness condition occurred.
Driving style classification by analyzing EEG responses to unexpected obstacle dodging tasks
(2006)
Lin, Chin-Teng
;
Liang, Sheng-Fu
;
Chao, Wen-Hung
;
Ko, Li-Wei
;
Chao, Chih-Feng
Driving safely has received increasing attention of the publics due to the growing number of traffic accidents that the driver's driving style is highly correlated to many accidents. The purpose of this study is to investigate the relationship between driver's driving style and driver's ERP response. In our research, a virtual reality (VR) driving environment is developed to provide stimuli to subjects. Independent component analysis (ICA) is used to decompose the electroencephalogram (EEG) data. The power spectrum analysis of ICA components and correlation analysis are employed to investigate the EEG activities related to driving style. Experimental results demonstrate that we may classify the drivers into aggressive or gentle styles based on the observed ERP difference corresponding to the proposed unexpected obstacle dodging tasks.
EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach
(2008)
Pal, Nikhil R.
;
Chuang, Chien-Yao
;
Ko, Li-Wei
;
Chao, Chih-Feng
;
Jung, Tzyy-Ping
Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for driver's safety. Typically, physiological studies on real-time detection of drowsiness usually use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects, group statistics may not be useful to accurately predict changes in cognitive states. Researchers have attempted to build subject-dependent models based on his/her pilot data to account for individual variability. Such approaches cannot account for the cross-session variability in EEG dynamics, which may cause problems due to various reasons including electrode displacements, environmental noises, and skin-electrode impedance. Hence, we propose an unsupervised subject- and session-independent approach for detection departure from alertness in this study. Experimental results showed that the EEG power in the alpha-band (as well as in the theta-band) is highly correlated with changes in the subject's cognitive state with respect to drowsiness as reflected through his driving performance. This approach being an unsupervised and session-independent one could be used to develop a useful system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings. Copyright (C) 2008 Nikhil R. Pal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Independent Component Analysis Hard-IP integration System on Programmable Chip (SOPC) Platform
(2010)
Hung, Shao-Hang
;
Chao, Chih-Feng
;
Yan, Yu-Chun
;
Lin, Bor-Shyh
;
Lin, Chin-Teng
This paper presented Independent Component Analysis (ICA) Hard-IP integration in System on Programmable Chip (SOPC) platform. The ICA component can discover the main component for original signal in multiple fetching signal sources, and it has been used in biomedical signal processing such as electroencephalogram (EEG) analysis. The proposed system consists of a programmable CPU, ICA processing units, system bus, communication, and display interface. The experimental results showed that the proposed design implemented on Altera DE2 FPGA development board, can achieve real-time signal separation and display at 100 MHz. The whole design consists of 29,640 logic elements.
Tonic and phasic EEG and behavioral changes induced by arousing feedback
(2010-08-15)
Lin, Chin-Teng
;
Huang, Kuan-Chih
;
Chao, Chih-Feng
;
Chen, Jian-Ann
;
Chiu, Tzai-Wen
This study investigates brain dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses during a sustained-attention task. Electroencephalographic (EEG) and behavioral data were simultaneously collected during virtual-reality (VR) based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel. 30-channel EEG data were analyzed by independent component analysis and the short-time Fourier transform. Across subjects and sessions, intermittent performance during drowsiness was accompanied by characteristic spectral augmentation or suppression in the alpha- and theta-band spectra of a bilateral occipital component, corresponding to brief periods of normal (wakeful) and hypnagogic (sleeping) awareness and behavior. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events, which immediately agitated subject's responses to the events. The improved behavioral performance was accompanied by concurrent spectral suppression in the theta- and alpha-bands of the bilateral occipital component. The effects of auditory feedback on spectral changes lasted 30 s or longer. The results of this study demonstrate the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges. (C) 2010 Elsevier Inc. All rights reserved.
VLSI implementation for Epileptic Seizure Prediction System based on Wavelet and Chaos Theory
(2010)
Hung, Shao-Hang
;
Chao, Chih-Feng
;
Wang, Shu-Kai
;
Lin, Bor-Shyh
;
Lin, Chin-Teng
This paper presents a very large scale integration (VLSI) circuit implementation for Epileptic Seizure Prediction System based combination of wavelet and chaos theory. The system consists with operation units of discrete wavelet transform (DWT), correlation dimension ( CD), and correlation coefficient. This work discovered by certain bandwidth of signal extraction with DWT, and the combination with Chaotic features analysis, it can achieve a higher accuracy of epileptic prediction. Furthermore, the correlation coefficient between two correlation dimensions with different embedding dimensions was proposed as a novel feature for epileptic seizure prediction in this study. The proposed system was evaluated with intracranial Electrocorticography (ECoG) recordings from a set of eleven patients with refractory temporal lobe epilepsy (TLE). The accuracy of experiment result for all subjects can achieve 87%, and a false prediction rate is 0.24/h. In average warning time occur about 27 min ahead the ictal.