應用空域與時域腦動態分析於駕駛安全之雙重任務探討

Abstract

於行車中的駕駛者之分心發生已經被證實是造成車禍發生的重大原因之一。因此，本論文建立虛擬實境技術之動態駕車裝置，來模擬真實之駕車環境，透過分心場景的設計，結合腦電波(Electroencephalogram, EEG)分析來探討駕車行為下人類分心效應的腦部認知功能與反應變化。在此實驗中，我們建立雙重任務促使駕駛者造成分心效應，此雙重任務分別為非預期性的車子偏移與數學問題的出現。為了研究車子偏移與數學任務的交互作用與影響，我們建立了五個不同時間間隔(Stimulus Onset Asynchrony, SOA)的雙重任務實驗，並分析此雙重任務於不同五個時間間隔所反應出的行為表現與腦電波動態變化。腦電波訊號分析採用獨立成份分析演算法(Independent Component Analysis, ICA)來分離出不同獨立成份為獨立之訊號源，再將特定獨立訊號源套用事件相關頻譜擾動分析(Event Related Spectral Perturbation, ERSP)來觀察時域與頻域響應，藉此了解並比較不同時間上腦電波的頻譜差異。從我們的研究成果發現，額葉區(Frontal Lobe)之Theta頻帶與Beta頻帶的能量增強與駕駛者分心效應有正向關係，另外在運動皮質區(Motor Area)也觀察到alpha頻帶與beta頻帶能量抑制的發生，此上述成果是由整合15位受測者的腦電波資料，也進行統計檢定分析。於行為表現上，我們觀察到不同時間間隔下反應時間的趨勢，與腦電波能量變化的趨勢是一致的，這說明了我們所發現的額葉區腦電波的能量增強現象，與行車中駕駛者分心效應發生有高度相關，而且能量增強越高，分心效應越強。

Driver distraction is a significant cause of traffic accidents. The aim of this study is to investigate Electroencephalography (EEG) dynamics in relation to distraction during driving. To study human cognition under a specific driving task, simulated real driving using virtual reality (VR) -based simulation and designed dual-task events are built, which include unexpected car deviations and mathematics questions. We designed five cases with different stimulus onset asynchrony (SOA) to investigate the distraction effects between the deviations and equations. The EEG channel signals are first converted into separated brain sources by independent component analysis (ICA). Then, event-related spectral perturbation (ERSP) changes of the EEG power spectrum are used to evaluate brain dynamics in time-frequency domains. Power increases in the theta and beta bands are observed in relation with distraction effects in the frontal cortex. In the motor area, alpha and beta power suppressions are also observed. All of the above results are consistently observed across 15 subjects. Additionally, further analysis demonstrates that response time and multiple cortical EEG power both changed significantly with different SOA. This study suggests that theta power increases in the frontal area is related to driver distraction and represents the strength of distraction in real-life situations.