文献类型：期刊文献

英文题名：Pattern Classification of Instantaneous Cognitive Task-load Through GMM Clustering, Laplacian Eigenmap, and Ensemble SVMs

作者：Zhang, Jianhua[1];Yin, Zhong[2];Wang, Rubin[3]

机构：[1]East China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China;[2]Univ Shanghai Sci & Technol, Shanghai Key Lab Modern Opt Syst, Minist Educ, Engn Res Ctr Opt Instrument & Syst, Shanghai 200093, Peoples R China;[3]East China Univ Sci & Technol, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China

年份：2017

卷号：14

期号：4

起止页码：947

外文期刊名：IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

收录：;EI(收录号：20173804190340);WOS:【SSCI(收录号:WOS:000407464700017)，SCI-EXPANDED(收录号:WOS:000407464700017)】；

基金：The authors would like to thank the developers of the aCAMS software which was used in our experiments. The authors also thank Michael Goessel, Fachhochschule Lubeck, Germany, for his assistance on parameter selection of the LE algorithm. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005. The major revision of this paper was completed while the first author (J. Zhang) was a Visiting Professor in the Control Systems Group (Prof. J. Raisch), TU Berlin, Germany in 2015. Jianhua Zhang is a corresponding author.

语种：英文

外文关键词：Cognitive task-load; psychophysiological signals; manifold learning; ensemble learning; support vector machine

摘要：The identification of the temporal variations in human operator cognitive task-load (CTL) is crucial for preventing possible accidents in human-machine collaborative systems. Recent literature has shown that the change of discrete CTL level during human-machine system operations can be objectively recognized using neurophysiological data and supervised learning technique. The objective of this work is to design subject-specific multi-class CTL classifier to reveal the complex unknown relationship between the operator's task performance and neurophysiological features by combining target class labeling, physiological feature reduction and selection, and ensemble classification techniques. The psychophysiological data acquisition experiments were performed under multiple human-machine process control tasks. Four or five target classes of CTL were determined by using a Gaussian mixture model and three human performance variables. By using Laplacian eigenmap, a few salient EEG features were extracted, and heart rates were used as the input features of the CTL classifier. Then, multiple support vector machines were aggregated via majority voting to create an ensemble classifier for recognizing the CTL classes. Finally, the obtained CTL classification results were compared with those of several existing methods. The results showed that the proposed methods are capable of deriving a reasonable number of target classes and low-dimensional optimal EEG features for individual human operator subjects.