Lin Chin-Teng, Chung I-Fang, Ko Li-Wei, Chen Yu-Chieh, Liang Sheng-Fu, Duann Jeng-Ren
Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan.
IEEE Trans Biomed Eng. 2007 Jul;54(7):1349-52. doi: 10.1109/TBME.2007.891164.
Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.
在交通死亡事故中，由人类行为失误和故障导致的事故死亡率很高，已成为公共安全领域的一个重要问题。这些事故主要是由驾驶员未能察觉交通信号灯的变化或道路上意外发生的意外情况引起的。在本文中，我们通过在虚拟现实（VR）动态驾驶环境中的交通信号灯实验中，研究脑电图（EEG）脑动力学背后的神经生物学信息，设计了一种定量分析方法来评估驾驶员的认知反应。VR技术使受试者能够直接与移动的虚拟环境进行交互，而不是单调的听觉和视觉刺激，从而提供了交互式的现实任务，而没有在实际机器上操作的风险。独立成分分析（ICA）用于从多通道EEG信号中分离和提取无噪声的ERP信号。使用时间滤波器解决ERP特征的时间对齐问题，并使用主成分分析（PCA）来减少特征维度。然后将降维后的特征输入到自构建神经模糊推理网络（SONFIN）中，以识别由红/绿/黄交通事件刺激的不同脑电位，在这个视觉刺激ERP实验中，平均八名受试者的准确率可达87%。这证明了检测和分析代表操作员认知状态和对任务事件反应的多流ERP信号的可行性。
