標題: 利用虛擬實境模擬系統研究駕駛員從清醒至打瞌睡過程之腦波變化
Electroencephalographic Spectral Changes from Alertness to Drowsiness in a Driving Simulator
作者: 鄭仲良
Jong-Liang Jeng
林志生
Chih-Sheng Lin
生物科技學系
關鍵字: 腦電波;打瞌睡;獨立成分分析;獨立成分分群分析;認知狀態;Alpha 波;Theta 波;Electroencephalogram;Drowsiness;Independent Component Analysis;Component Cluster Analysis;Cognitive State;Alpha Wave;Theta Wave
公開日期: 2007
摘要: 打瞌睡是造成意外事故的主因之一,因此於各種工作環境中,一套可靠、即時的非侵入式打瞌睡警示系統的建立是有其必要性的。本論文的目標在於利用360度虛擬實境(Virtual-Reality: VR)模擬駕駛系統,藉由一小時將維持車輛在車道中心位置的長時駕駛工作,探討駕駛員由清醒到打瞌睡的連續腦波(Electroencephalogram: EEG)變化現象。十六位年齡在18到28歲間的受測者參與此駕駛模擬實驗,並以256Hz取樣頻率同步量測其32通道腦電波與駕駛行為資料。所量測的腦電波在排除雜訊後,利用獨立成分分析法、時頻分析法,獨立成分分群分析來瞭解人類與清醒到打瞌睡認知狀態改變相關的腦電波變化,並作為未來發展即時瞌睡警示系統的基礎。 實驗結果顯示,人類在不同打瞌睡的程度之下其腦電波的變化情形也不相同。精神狀態從清醒至極輕度和輕度瞌睡過程中,在bi-lateral occipital (BLO)、 occipital midline (OM)、 frontal central midline (FCM)、 central midline (CM)、 central parietal midline (CPM)、 left-central parietal (LCP) 與 right-central parietal (RCP)等所得到的獨立成分群中,α波(8-12Hz)強度會持續性的增強,而進入重度瞌睡時, α波強度則會輕微的降低。另外,精神狀態從輕度至重度瞌睡過程中,θ波(4-7Hz)強度則持續的增強。實驗結果亦顯示BLO和OM 的α波是一個較適合用於極輕度打瞌睡的偵測指標,而進入輕度打瞌睡時,BLO和OM的α和θ波是適合的偵測指標,而重度打瞌睡時,BLO和OM的θ波是一個較適合的偵測指標。
Many traffic accidents have resulted from loss of alertness, lack of attention, or poor decision-making of truck and auto drivers. Catastrophic errors can be caused by momentary lapses in alertness and attention during periods of relative inactivity. Therefore, accurate and non-intrusive real-time monitoring of operator alertness would thus be highly desirable in a variety of operational environments. The aim of this study is to investigate the continuous electroencephalogram (EEG) fluctuations from alertness to drowsiness in a realistic virtual-reality-based (VR) driving environment that comprises a 360° virtual reality scene and a driving simulator. Sixteen healthy subjects (aged between 18 and 28) performed 1-hour lane-keeping driving task while their 32-channel EEG signals and driving behavior data were simultaneously recorded at 256 Hz. EEG data, after artifact removal, were processed by independent component analysis (ICA), component cluster analysis and time-frequency analysis to assess EEG correlates of cognitive-state changes. The bi-lateral occipital (BLO), occipital midline (OM), frontal central midline (FCM), central midline (CM), central parietal midline (CPM), left-central parietal (LCP) and right-central parietal (RCP) component clusters exhibited monotonic alpha-band (8-12 Hz) power increase during the transition from alertness to very-slight and slight drowsiness, but remain constant or slight decrease during the extreme drowsiness period. On the other hand, the theta-band (4-7 Hz) power for BLO, OM, FCM, CM, CPM, LCP and RCP component clusters increased monotonically during the transition from slight to extreme drowsiness. Additionally, we compared the EEG between different component clusters diversity of EEG power changes with respect to the transition from alertness to drowsiness and found that alpha power of BLO and OM component were most stable and desirable EEG feature for very-slight and slight drowsiness detection. The theta power of BLO and OM component were the most stable and desirable EEG feature for slight and extreme drowsiness detection.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009428520
http://hdl.handle.net/11536/81499
Appears in Collections:Thesis


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