标题: 利用虚拟实境模拟系统侦测驾驶员从清醒至打瞌睡过程之脑波变化
EEG-Based Subject- and Session-Independent Drowsiness Detection: An Unsupervised Approach
作者: 庄玠瑶
Jade
林进灯
多媒体工程研究所
关键字: 打瞌睡;虚拟实境;脑电波;认知狀态;瞌睡警示;特征值抽取;human cognition;Electroencephalogram (EEG);Alertness, Drowsiness;unsupervised;alpha band;theta band
公开日期: 2007
摘要: 打瞌睡是造成意外事故的主因之一,因此于各种工作环境中,一套可靠、即时的非侵入式打瞌睡警示系统的建立是有其必要性的。本論文的目标在于利用360 度虚拟实境(Virtual-Reality: VR)模拟驾驶系统,藉由一小时将维持車辆在車道中心位置的长时驾驶工作,侦测驾驶员由清醒到打瞌睡的連续脑波(Electroencephalogram: EEG)变化现象。十三位年龄在18到28岁间的受测者參与此驾驶模拟实验,并以250Hz 取样频率同步量测其28通道脑电波与驾驶行为资料。所量测的脑电波利用 unsupervised 演算法来侦测人類从清醒到打瞌睡认知狀态的改变。此应用可作为未來发展即时瞌睡警示系统的基础。实验结果显示,我们不需要事先资料的回馈并且使用更简洁的运算即可准确的侦测出受测者从清醒到打瞌睡的脑波状态。并发现人類在不同打瞌睡的程度之下其脑电波的变化情形也不相同。精神狀态从清醒至极轻度和轻度瞌睡过程中,有些的受测者可能使用α波来做特征值抽取会有比较好的表现结果,而有些受测者则可能使用θ波会有比较好的表现结果 所以我们结合α波和θ波来做为一个瞌睡指标的依据,以期能侦测出受测者从清醒到打瞌睡的变化来防止一些因打瞌睡而导致的意外产生。
Monitoring and prediction of changes in the human cognitive stages, such as alertness, drowsiness, using physiological signals such as Electroencephalogram (EEG) are very important for driver’s safety. Typically, psychophysiological studies on real time detection of drowsiness based on EEG data use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects, group statistics may not be useful to accurately predict changes in cognitive states. Researchers have attempted to build subject-dependent models based on his/her pilot data to account for individual variability. Such approaches cannot account for the cross-session variability in EEG dynamics, which may cause problems due to various reasons including electrode displacements, environmental noises, and skin-electrode impedance. Here we propose an unsupervised subject and session independent approach for detection departure from alertness. We first demonstrate that the EEG power in the alpha band (as well as in the theta band) is correlated with changes in the subject’s cognitive state with respect to drowsiness as reflected through his driving performance. We then make a few mild and realistic assumptions to derive models for the alert state of the driver using the EEG power in the alpha and theta bands. The deviations of the EEG power in the alpha and theta bands from the corresponding alert models are found to be correlated with the changes in the driving performance. Although, the alert state models derived using alpha band power and theta band power are quite effective in detecting drowsiness, for an improved performance, we also use a liner combination of deviations of the EEG power in the alpha band and theta band from the respective alert models. This approach being an unsupervised and session independent one could be used to develop a useful system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009557546
http://hdl.handle.net/11536/39698
显示于类别:Thesis


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