Chih-Feng Chao

Simplification of fuzzy-neural systems using similarity analysis

This paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate redundant fuzzy logical rules, so that the number of rules in the resulting fuzzy inference system will be reduced. Moreover, a fuzzy similarity measure for fuzzy sets that indicates the degree to which two fuzzy sets are equal is applied to combine similar input linguistic term nodes. Thus we obtain a method for reducing the complexity of a fuzzy neural network. We also design a new and efficient on-line initialization method for choosing the initial parameters of the FNNS. A computer simulation is presented to illustrate the performance and applicability of the proposed FNNS. The result indicates that the FNNS still has desirable performance under fewer fuzzy logical rules and adjustable parameters.

A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection

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 owner 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.

EEG-based subject- and session-independent drowsiness detection: an unsupervised approach

Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for drivers 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 subjects 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.

Tonic and phasic EEG and behavioral changes induced by arousing feedback

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 subjects 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 30s 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.

Distraction-related EEG dynamics in virtual reality driving simulation

Driver distraction has been recognized as a significant cause of traffic incidents. Therefore, the aim of this study was to investigate electroencephalography (EEG) dynamics in response to distraction during driving. To study human cognition under specific driving task, we used virtual reality (VR) based driving simulation to simulate events including unexpected car deviations and mathematics questions (math) in real driving. For further assessing effects of the stimulus onset asynchrony (SOA) between the deviation onset and math presented on the EEG dynamics, we designed five cases with different SOA. The scalp-recorded EEG channel signals were first separated into independent brain sources by independent component analysis (ICA). Then, the event-related-spectral-perturbations (ERSP) measuring changes of EEG power spectra were used to evaluate the brain dynamics in time-frequency domains. Results showed that increases of theta band (5~7.8 Hz) and beta band (12.2~17 Hz) power were observed in the frontal cortex. Results demonstrated that reaction time and multiple cortical EEG sources responded to the driving deviations and math occurrences differentially in the stimulus onset asynchrony. Results also suggested that the theta band power increase in frontal area could be used as the distracted indexes for early detecting drivers inattention in the future.

VLSI implementation for Epileptic Seizure Prediction System based on wavelet and chaos theory

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.

Driving Style Classification by Analyzing EEG Responses to Unexpected Obstacle Dodging Tasks

Driving safely has received increasing attention of the publics due to the growing number of traffic accidents that the drivers driving style is highly correlated to many accidents. The purpose of this study is to investigate the relationship between drivers driving style and drivers 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.

Development of real-time wireless brain computer interface for drowsiness detection

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 drivers drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers 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.

Independent Component Analysis Hard-IP integration system on programmable chip (SOPC) platform

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.