EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach
EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach
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DOI：
10.1155/2008/519480
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发表时间：
2008-01-01
影响因子：
1.9
通讯作者：
Lin, Chin-Teng
中科院分区：
文献类型：
--
作者：
Pal, Nikhil R.;Chuang, Chien-Yao;Lin, Chin-Teng
使用生理信号监测和预测人类认知状态的变化，如警觉和嗜睡，对于驾驶员的安全非常重要。通常，实时检测嗜睡的生理学研究通常对所有受试者使用相同的模型。然而，与警觉性丧失相关的脑电动力学中相对较大的个体变异性意味着，对许多受试者来说，群体统计数据可能无法准确预测认知状态的变化。研究人员试图根据他/她的试验数据建立依赖于受试者的模型，以解释个体的可变性。这些方法不能解释脑电动力学中的跨时段变异性，这可能会由于包括电极位移、环境噪声和皮肤电极阻抗在内的各种原因而引起问题。因此，在这项研究中，我们提出了一种无监督的独立于主体和会话的方法来检测偏离警觉性的内容。实验结果表明，阿尔法频段(以及θ频段)的脑电功率与受试者在驾驶过程中反映出的昏昏欲睡的认知状态的变化高度相关。这种方法是一种无监督和独立于会话的方法，可以用来开发一个有用的系统，用于在注意力关键环境中对人类操作员的认知状态进行非侵入性监测。版权所有(C)2008 Nikhil R.Pal等人。本文是根据知识共享署名许可证分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是正确引用原始作品。
Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for driver's safety. Typically, physiological studies on real-time detection of drowsiness usually 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. Hence, we propose an unsupervised subject- and session-independent approach for detection departure from alertness in this study. Experimental results showed that the EEG power in the alpha-band (as well as in the theta-band) is highly correlated with changes in the subject's cognitive state with respect to drowsiness as reflected through his driving performance. 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. Copyright (C) 2008 Nikhil R. Pal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.