EEG-based drowsiness estimation for safety driving using independent component analysis
EEG-based drowsiness estimation for safety driving using independent component analysis
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DOI：
10.1109/tcsi.2005.857555
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发表时间：
2005-12-01
影响因子：
5.1
通讯作者：
Jung, TP
中科院分区：
文献类型：
--
作者：
Lin, CT;Wu, RC;Jung, TP
近年来，预防嗜睡引发的事故已成为主动安全驾驶的一大重点。它需要一种最优的技术来连续检测驾驶员的认知状态，这些状态与(近)实时的感知、识别和车辆控制能力有关。开发这类系统的主要挑战包括：1)缺乏有效的嗜睡检测指标；2)在现实和动态的驾驶环境中，复杂而普遍的噪声干扰。在基于虚拟现实(VR)的动态模拟器中，结合独立分量分析(ICA)、功率谱分析、相关评价和线性回归模型，开发了一种基于脑电(EEG)的昏昏欲睡估计系统来估计驾驶员驾驶汽车时的认知状态。驾驶误差定义为在车道保持驾驶任务中车辆中心与巡航车道中心之间的偏差。实验结果证明了基于ICA的多流脑电谱定量估计嗜睡程度的可行性。将ICA方法应用于ICA分量的功率谱分析，可以成功地(1)去除大部分脑电伪影，(2)提出一种最佳的脑电蒙太奇放置方式，并以驾驶性能衡量指标来估计驾驶员的嗜睡程度波动。最后，我们给出了一个基准研究，在该研究中，基于ICA组件的警报估计的准确性比基于头皮-EEG的警报估计的准确性更好。
Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.