EEG-Based Assessment of Driver Cognitive Responses and Its Application to Driving Safety

• Main Authors: Ruei-Cheng Wu, 吳瑞成
• Other Authors: Chin-Teng Lin
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/44586756091071127881

博士 === 國立交通大學 === 電機與控制工程系所 === 94 === In this thesis, we develop advanced biomedical signal-processing technologies that combine independent component analysis (ICA), power spectrum analysis, correlation analysis, and fuzzy neural network (FNN) models to assess the event-related transient brain dynamics and the level of alertness of drivers by investigating the neurobiological mechanisms underlying non-invasively recorded electroencephalographic (EEG) signals in the virtual-reality-based cognitive driving tasks. The developed techniques are then applied for dynamically quantifying driver’s cognitive responses related to perception, recognition, and vehicle control abilities with concurrent changes in the driving performance to maintain their maximum performance in order to prevent accidents caused by errors and failures for driving safety. We first propose a novel ICA-based temporal matching filter for analyzing the single-trial event-related brain potentials (ERP) without using conventional trial-averaging results as input features of the FNN classifiers and apply this method to recognize the different transient brain responses stimulated by red/green/amber traffic-light events. Experimental results demonstrate the feasibility for identifying multiple streams of EEG signals related to human cognitive states and responses to task events. Our proposed methods can dramatically increase the quantity and quality of momentary cognitive information and achieve high recognition rates. We also develop a new ICA-based adaptive feature-selecting mechanism to extract most effective bandpower from EEG power spectrum and build an individual FNN model for each subject to further examine the neural activities correlated with fluctuations in human alertness level accompanying changes in the driving performance in the lane-keeping driving tasks. Experimental results show a closed relationship between changes in EEG power spectrum and the subject’s driving performance. Our proposed models also can effectively remove most non-brain artifacts and locate optimal positions to wire EEG electrodes such that it is possible to accurately estimate/predict the continuous fluctuations in human alertness level indexed by measuring the driving performance quantitatively. The computational methods are well within the capabilities of modern digital signal processing hardware to perform in real time and thus might be used to construct and test on a portable embedded system for an online alertness monitoring system in the future.