| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 93 === Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal estimation system to online continuously detect drivers’ cognitive state related to abilities in perception, recognition and vehicle control. The propose of this thesis is to develop an adaptive drowsiness estimation system based on electroencephalogram (EEG) by combining with independent component analysis (ICA), time-frequency spectral analysis, correlation analysis and fuzzy neural network model to estimate a driver’s cognitive state in Virtual-Reality (VR) dynamic driving simulator. Moreover, the VR-based motion platform with EEG measured system is the innovation of brain and cognitive engineering researches.
Firstly, there is good evidence to show that the necessary of VR-based motion platform for brain research in driving simulation. This is an important fact to stress that the kinesthetic stimuli obviously influence the cognitive states and the phenomenon can be indicated by the EEG signals. Secondly, a single-trial event-related potential (ERP) is applied to recognize different brain potentials by the five degrees of drowsiness in driving. And we demonstrate a close relationship between the fluctuations in driving performance and the EEG signal log bandpower spectrum. Our Experimental results show that it is feasible to accurately estimate the driving performance. Then we observe that the brain source related to drowsiness is on cerebral cortex. Finally, the spiked dry electrodes and the corresponding movement artifact removal technology were designed to replace the regular wet electrode for the purpose of applications in the realistic driving or working environments.
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