An algorithm for online detection of temporal changes in operator cognitive state using real-time psychophysiological data

被引：17
作者：
Cannon, Jordan A.
Krokhmal, Pavlo A. [1 ]
Lenth, Russell V. [2 ]
Murphey, Robert [3 ]
机构：
[1] Univ Iowa, Dept Mech & Ind Engn, Seamans Ctr 3131, Iowa City, IA 52242 USA
[2] Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA
[3] USAF, Res Lab, Munit Directorate, Eglin AFB, FL 32542 USA
来源：
BIOMEDICAL SIGNAL PROCESSING AND CONTROL | 2010年 / 5卷 / 03期
基金：
美国国家科学基金会;
关键词：
Cognitive state; Psychophysiological data; EEG; EOG; Kullback-Leibler distance; Statistical analysis; SERIES PREDICTION; EEG; TASK; SYSTEM; INDEXES;
D O I：
10.1016/j.bspc.2010.03.005
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
We consider the problem of on-the-fly detection of temporal changes in the cognitive state of human subjects due to varying levels of difficulty of performed tasks using real-time EEG and EOG data. We construct the Cognitive State Indicator (CSI) as a function that projects the multidimensional EEG/EOG signals onto the interval [0,1] by maximizing the Kullback-Leibler distance between distributions of the signals, and whose values change continuously with variations in cognitive load. During offline testing (i.e., when evolution in time is disregarded) it was demonstrated that the CSI can serve as a statistically significant discriminator between states of different cognitive loads. In the online setting, a trend detection heuristic (TDH) has been proposed to detect real-time changes in the cognitive state by monitoring trends in the CSI. Our results support the application of the CSI and the TDH in future closed-loop control systems with human supervision. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：229 / 236
页数：8
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