Browsing by Author "Hung, Shao-Hang"

Now showing 1 - 6 of 6

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.
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.
FPGA Implementation of 4-Channel ICA for On-line EEG Signal Separation
(2008)
Huang, Wei-Chung
;
Hung, Shao-Hang
;
Chung, Jen-Feng
;
Chang, Meng-Hsiu
;
Van, Lan-Da
Blind source separation of independent sources from their mixtures is a common problem for multi-sensor applications in real world, for example, speech or biomedical signal processing. This paper presents an independent component analysis (ICA) method with information maximization (Infomax) update applied into 4-channel one-line EEG signal separation. This can be implemented on FPGA with a fixed-point number representation, and then the separated signals are transmitted via Bluetooth. As experimental results, the proposed design is faster 56 times than soft performance, and the correlation coefficients at least 80% with the absolute value are compared with off-line processing results. Finally, live demonstration is shown in the DE2 FPGA board, and the design is consisted of 16,605 logic elements.
Front-end amplifier of low-noise and tunable BW/Gain for portable biomedical signal acquisition
(2008)
Huang, Chun-Chieh
;
Hung, Shao-Hang
;
Chung, Jen-Feng
;
Van, Lan-Da
;
Lin, Chin-Teng
We proposed a novel analog circuit design which is suitable for various biomedical signal acquisitions. In addition to the consideration of low power and low noise, the analog front-end integrated Circuit (AFEIC) is presented with design of high common-mode rejection ratio (CMRR) and high power supply ripple rejection ratio (PSRR). It has not only reduced the number of outer components, and enhances a better signal-to-noise ratio (SNR). The chip includes a current-balancing instrumentation amplifier, switched-capacitor filter, non-overlapping clock generator, and a programmable gain amplifier (PGA). It was fabricated by TSMC 0.35 mu m CMOS 2P4M standard process, with CMRR 155 dB CMRR, 131 dB of PSRR+, and 127 dB of PSRR- at 50 Hz. The power consumption is about 142.4 mu W under +/- 1.5V supply.
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.
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.