標題: 利用腦波之獨立成份分析結合虛擬實境動態模擬系統開發駕駛員瞌睡偵測技術
EEG-Based Drowsiness Estimation Using Independent Component Analysis in Virtual-Reality Dynamic Driving Simulator
作者: 陳俞傑
Yu-Jie Chen
林進燈
Chin-Teng Lin
電控工程研究所
關鍵字: 瞌睡偵測;腦電波;虛擬實境;動態平台;認知狀態;事件相關電位;動覺刺激;獨立成分分析;乾式電極;Drowsiness;Electroencephalogram;Virtual Reality;Dynamic Platform;Cognitive State;Event-Related Potential;Kinesthetic Stimulus;ndependent Component Analysis;Dry Electrode
公開日期: 2004
摘要: 近年來,預防瞌睡所導致的交通意外,已經成為交通安全研究的重要課題,我們需要一個最理想的估測系統,可以即時連續的偵測駕駛員的精神認知狀態、知覺以及控制車輛的能力。本論文的目的在發展一套有效的駕駛精神認知狀態估測系統,利用腦電波訊號結合頻譜分析、獨立成分分析演算法、相關係數分析以及類神經網路模型,結合虛擬實境動態模擬駕駛系統,開發駕駛員的瞌睡偵測技術;此外在虛擬實境的環境中,結合腦波量測系統與動感平台,進行神經認知系統研究,在腦科學與認知工程領域上都是一項創新。 首先我們證明利用虛擬實境結合動感平台,以進行實用之認知工程研究是必要的,動態刺激會明顯的影響腦波訊號認知狀態。我們亦利用單一試驗的事件相關腦電位分析,去識別開車時不同瞌睡程度的腦電位變化,並且證明人類腦波特定的頻帶活動與開車行為表現之間的關係非常密切,並經由實驗結果顯示,利用腦波訊號分析以估測駕駛員行為表現是可行的。我們亦研究在大腦皮層上與發生瞌睡相關的區域,最後為了實際應用的可行性,我們利用乾式電極結合獨特之雜訊消除技術取代傳統電極,以期將本論文所開發的技術,未來應用於實際駕駛與工作環境中。
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.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009212556
http://hdl.handle.net/11536/68523
Appears in Collections:Thesis


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