title: Predictive modeling of human operator cognitive state via sparse and robust support vector machines.
abstract: The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human-machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.
authors.affiliation: Department of Automation, East China University of Science and Technology, Shanghai, 200237 People's Republic of China ; Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237 People's Republic of China.
authors.forename: Jian-Hua
authors.initials: JH
authors.lastname: Zhang
authors.affiliation: Department of Automation, East China University of Science and Technology, Shanghai, 200237 People's Republic of China.
authors.forename: Pan-Pan
authors.initials: PP
authors.lastname: Qin
authors.affiliation: Control Systems Group, Technische Universität Berlin, 10587 Berlin, Germany ; Systems and Control Theory Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Raisch
authors.affiliation: Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237 People's Republic of China.
authors.forename: Ru-Bin
authors.initials: RB
authors.lastname: Wang
keywords: Human operator cognitive state; Least squares support vector machine; Regressive model; Robustness; Sparseness