Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model.
Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model.
复制标题
DOI：
10.1016/j.biopsycho.2011.03.003
复制
发表时间：
2011-05
影响因子：
2.6
通讯作者：
Berka, Chris
中科院分区：
文献类型：
--
作者：
Johnson, Robin R.;Popovic, Djordje P.;Olmstead, Richard E.;Stikic, Maja;Levendowski, Daniel J.;Berka, Chris
上个世纪的大量研究集中在嗜睡/警觉检测上，因为疲劳相关的身体和认知障碍对公共健康和安全构成了严重威胁。现有的嗜睡/警觉检测解决方案不能令人满意，原因有很多：1)缺乏概括性，2)未能解决广义模型中的个体可变性，和/或3)它们缺乏可移植的、不受限制的应用程序。本研究旨在解决这些问题，并确定是否可以定义一种基于脑电(EEG)的个性化算法来跟踪与睡眠缺失相关的性能下降，因为这是开发可现场部署的嗜睡/警觉检测系统的第一步。结果表明，一种基于脑电的算法，通过一系列简短的“识别”任务进行个性化，能够有效地跟踪与睡眠剥夺相关的性能下降。未来的发展将解决该算法预测由于睡眠不足而导致的性能下降的需求，并提供现场适用性。
A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: 1) lack of generalizability, 2) failure to address individual variability in generalized models, and/or 3) they lack a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability.
登录
查看更多内容
DOI：
10.1016/0013-4694(90)90035-i
发表时间：
1990-10-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
--
作者：
GEVINS, AS;BRESSLER, SL;JEX, HR
通讯作者：
JEX, HR
DOI：
10.3758/bf03200977
发表时间：
1985-12-01
期刊：
BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS
影响因子：
--
作者：
DINGES, DF;POWELL, JW
通讯作者：
POWELL, JW
DOI：
10.1207/s15327590ijhc1702_3
发表时间：
2004-01-01
影响因子：
4.7
作者：
Berka, C;Levendowski, DJ;Olmstead, R
通讯作者：
Olmstead, R
影响因子：
4.7
作者：
Higuchi, S;Liu, Y;Motohashi, Y
通讯作者：
Motohashi, Y
影响因子：
5.9
作者：
Baulk, S. D.;Biggs, S. N.;Dawson, D.
通讯作者：
Dawson, D.