Browsing by Author "Tsai, Shu-Fang"

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EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
(2013-10-01)
Lin, Chin-Teng
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Tsai, Shu-Fang
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Ko, Li-Wei
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生物資訊及系統生物研究所
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資訊工程學系
Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of similar to 82%.
EEG-based Motion Sickness Classification system with Genetic Feature Selection
(2013)
Ko, Li-Wei
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Lee, Hua-Chin
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Tsai, Shu-Fang
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Shih, Tsung-Chin
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Chuang, Ya-Ting
People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VIZ-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects.
Motion Sickness Estimation System
(2012)
Lin, Chin-Teng
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Tsai, Shu-Fang
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Lee, Hua-Chin
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Huang, Hui-Lin
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Ho, Shinn-Ying
Motion sickness occurs when the brain receives conflicting sensory information from body, inner ear and eyes [1]. In some cases, a decreased ability to actively control the body's postural motion also causes motion sickness [2][3]. Many previous studies have indicated that motion sickness had negative effect on driving performance, and sometimes lead to serious traffic accidents due to self-control ability decline. Therefore motion sickness becomes a very important issue in our daily life especially considering driving safety. There are many attempts made by researchers to realize motion sickness, and detect motion sickness in the early stage. Although many motion-sickness-related biomarkers have been identified, estimating human motion sickness level (MSL) remains a challenge in operational environment. In our past studies, we found that features in the occipital area were highly correlated with the driver's driving performance. In this study, we designed a virtual-reality (VR) based driving environment with instinct-MSL-reporting mechanism. When a subject performed a driving task, his/her brain EEG was recorded simultaneously. From those EEG data, features associated with left motor brain area, parietal brain area and occipital midline brain area which predicted MSL were extracted by an optimal classifier implemented by an inheritable bi-objective combinatorial genetic algorithm (IBCGA) with support vector machine. Unlike traditional correlation-based method, IBCGA aims to select a small set of EEG features and maximize the prediction accuracy simultaneously in BCI applications. Once the optimal feature set predicting MSL is successfully found, a driver's cognitive state can be monitored.
Transcutaneous Electrical Nerve Stimulation System for Improvement of Flight Orientation in a VR-based Motion Environment
(2012)
Yang, Shiau-Ru
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Chen, Shi-An
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Tsai, Shu-Fang
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Lin, Chin-Teng
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電機工程學系
It is very important to reduce the possibility of spatial disorientation because spatial disorientation is a major cause of aircraft crashes. In this research, we assess the effect of transcutaneous electrical nerve stimulation (TENS) on spatial cognitive function by measuring physiological signals, including brain waves measured by electroencephalography (EEG). Through physiological signals such as brain waves, we can objectively understand a subject's cognitive state. Moreover, we use virtual reality technology to build 3D scenery that provides navigation clues in an environment with no visual reference frame. Using the virtual environment, we confirm that TENS lessens spatial disorientation. Across subjects and sessions, the parietal component of brain activity exhibited baseline elevation predominantly in the theta (4-7 Hz) and alpha (8-12 Hz) band as the subjects navigated. This study also found that subjects performed better after TENS in terms of behavioral and EEG data. The results facilitate understanding of the cognitive function of maintaining spatial orientation and development of a device for assisting pilots and reducing the occurrence of spatial disorientation.
Wireless and Wearable EEG System for Evaluating Driver Vigilance
(2014-04-01)
Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Huang, Chih-Sheng
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Tsai, Shu-Fang
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Lu, Shao-Wei
Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers\' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver\'s vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver\'s vigilance in real time.