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.
Similar content being viewed by others
References
Bradshaw JM et al (2002) Adjustable autonomy and human-agent teamwork in practice: an interim report on space applications, Chapter 0, IEEE computer society foundation for intelligent physical agents (FIPA)
Backs RW, Boucsein W (eds) (2000) Engineering psychophysiology: issues and applications. Lawrence Erlbaum Associates, Mahwah
Bainbridge L (1983) Ironies of automation. Automatica 19(6):775–779
Chen Z, Cao J, Cao Y et al (2008) An empirical EEG analysis in brain death diagnosis for aults. Cogn Neurodyn 2:257–271
Colucci F (1995) Rotorcraft Pilot’s Associate update: the army’s largest science and technology program. Vertiflite, March/April 1995, 16–20
Fitts PM (1951) Some basic questions in designing an air-navigation and air-traffic control system. In: Moray N (ed) Ergonomics major writings, vol 4. Taylor & Francis, London, pp 367–383
Gaillard AWK, Kramer AF (2000) Theoretical and methodological issues in psychophysiological research. In: Backs RW, Boucsein W (eds) Engineering psychophysiology: issues and applications. Lawrence Erlbaum Associates, Mahwah, pp 31–58
Gao J, Hu J, Tung W–W (2011) Complexity measures of brain wave dynamics. Cogn Neurodyn 5:171–182
Gevins A, Smith ME (1999) Detecting transient cognitive impairment with EEG pattern recognition methods. Aviat Space Environ Med 70:1018–1024
Gevins A, Smith ME, Leong H (1998) Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Hum Factors 40:79–91
Greene KA, Bauer KW, Wilson GF et al (2000) Selection of psychophysiological features for classifying air traffic controller workload in neural networks. Smart Eng Syst Des 2:315–330
Hammer JM, Small RL (1995) An intelligent interface in an associate system. In: Rouse WB (ed) Human/technology interaction in complex systems, vol 7. JAI Press, Greenwich, pp 1–44
Hampel FR, Rousseeuw PJ, Stahel WA (1986) Robust statistics—the approach based on influence functions. Wiley, New York
Hancock PA, Desmond PA (2001) Stress, workload and fatigue. Lawrence Erlbaum Associates, Mahwah
Hockey GRJ (1997) Compensatory control in the regulation of human performance under stress and high workload: a cognitive-energetical framework. Biol Psychol 45:73–93
Hockey GRJ (2003) Operator functional state: the assessment and prediction of human performance degradation in complex tasks. IOS Press, Amsterdam
Hockey GRJ, Wastell DG, Sauer J (1998) Effects of sleep deprivation and user-interface on complex performance: a multilevel analysis of compensatory control. Hum Factors 40:233–253
Hockey GRJ, Nickel P, Roberts AC, Roberts MH (2009) Sensitivity of candidate markers of psychophysiological strain to cyclical changes in manual control load during simulated process control. Appl Ergon 40:1011–1018
Hoyt R (ed) (2010) Real-time physiological and psycho-physiological status monitoring. NATO RTO publication RTO-TR-HFM-132, NATO Research and Technology Organization, Neuilly sur Seine, July 2010
Jorna PGAM (1993) Heart rate and workload variations in actual and simulated flight. Ergonomics 36(9):1043–1054
Kuriyagawa Y, Kageyama I (1999) A modeling of heart rate variability to estimate mental work load. In: Proceedings of IEEE international conference on systems, man, and cybernetics (SMC’99), vol 2, pp 294–299
Lee S-Y, Song H-A, Amari S-I (2012) A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech. Cogn Neurodyn 6:525–535
Nickel P, Roberts AC, Hockey GRJ (2005) Assessment of high risk operator functional state markers in dynamical systems–preliminary results and implications. In: Proceedings of human factors and ergonomics society Europe chapter annual meeting, Turin, 26–28 Oct 2005
Parasuraman R (2000) Designing automation for human use: empirical studies and quantitative models. Ergonomics 43:931–951
Parasuraman R, Sheridan TB, Wickens CD (2000) A model for types and levels of human interaction with automation. IEEE Trans SMC Part A 30(3):286–297
Pockett S, Whalen S, McPhail AVH, Freeman WJ (2007) Topography, independent component analysis and dipole source analysis of movement related potentials. Cogn Neurodyn 1:327–340
Prinzel LJ, Freeman FG, Scerbo MW, Mikulka PJ, Pope AT (2000) A closed-loop system for examining psychophysiological measures for adaptive task allocation. Int J Aviat Psychol 10:393–410
Qin P–P, Zhang J-H (2012) LSSVM regressive model based analysis of operator functional state in a human-machine system (in Chinese). Space Med Med Eng 25(1):35–41
Rouse WB (1976) Adaptive allocation of decision making responsibility between supervisor and computer. In: Sheridan TB, Johannsen G (eds) Monitoring behavior and supervisory control. Plenum Press, New York, pp 295–306
Rouse WB (1977) Human-computer interaction in multi-task situations. IEEE Trans SMC 7:384–392
Rousseeuw PJ, Leroy A (1987) Robust regression and outlier detection. Wiley, New York
Russell CA, Wilson GF (1998) Air traffic controller functional state classification using neural networks. In: Proceedings of the artificial neural networks in engineering conference, vol 8, pp 649–654
Scerbo MW, Freeman FG, Mikulka PJ (2000) A biocybernetic system for adaptive automation. In: Backs RW, Boucsein W (eds) Engineering psychophysiology: issues and applications. Lawrence Erlbaum Associates, Mahwah, pp 241–254
Scerbo MW, Freeman FG, Mikulka PJ, Parasurmann R, Di Nocero F, Prinzel LJ III (2001) The efficacy of psychophysiological measures for implementing adaptive technology, NASA/TP-2001-211018, June
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Suykens JAK, Lukas L, Vandewalle J (2000) Sparse least squares support vector machines classifiers. In: Proceedings of the European symposium on artificial neural networks (ESANN’2000), vol 4, Bruges, pp 37–42
Suykens JAK, De Brabanter J, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1–4):85–105
Trejo LJ, Wheeler KR, Jorgensen CC et al (2003) Multi-modal neuroelectric interface development. IEEE Trans Neural Syst Rehabil Eng 11(2):199–204
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Wang R, Zhang J, Zhang Y, Wang X (2012) Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model. Biomed Signal Process Control 7:490–498
Werner G (2012) From brain states to mental phenomena via phase space transitions and renormalization group transformation: proposal of a theory. Cogn Neurodyn 6:199–202
Wilson GF (2001) In-flight psychophysiological monitoring. In: Fahrenberg F, Myrtek M (eds) Progress in ambulatory monitoring. Hogrefe and Huber Publishers, Seattle, pp 435–454
Wilson GF (2002a) Psychophysiological test methods and procedures. In: Charlton SG, O’Brien TG (eds) Handbook of human factors testing and evaluation. Lawrence Erlbaum Associates, Inc, Mahwah, pp 157–180
Wilson GF (2002b) An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int J Aviat Psychol 12:3–18
Wilson GF (2002c) Adaptive aiding implemented by psychophysiologically determined operator functional state. In: Proceedings of the NATO RTO-HFM symposium on the roles of humans in intelligent and automated systems, Warsaw, 7–9 Oct 2002; Also published in NATO RTO-MP-088, pp 18-1–18-8, Oct 2003
Wilson GF, Eggemerier FT (1991) Physiological measures of workload in multi-task environments. In: Damos D (ed) Multiple-task performance, pp 329–360
Wilson GF, Fisher F (1991) The use of cardiac and eye blink measures to determine flight segment in F4 crews. Aviat Space Environ Med 62:959–961
Wilson GF, Fisher F (1995) Cognitive task classification based upon topographic EEG data. Biol Psychol 40:239–250
Wilson GF, Schlegel RE (eds) (2004) Operator functional state assessment, NATO RTO Publication RTO-TR-HFM-104, NATO Research and Technology Organization, Neuilly sur Seine, Feb 2004
Wilson GF, Lambert JD, Russell CA (2000) Performance enhancement with real-time physiologically controlled adaptive aiding. In: Proceedings of the IEA 2000/HFES 2000 congress, vol 3, pp 61–64
Zhang Q, Lee M (2012) Analyzing the dynamics of emotional scene sequence using recurrent neuro-fuzzy network. Cogn Neurodyn, published online: 17 Aug 2012. doi: 10.1007/s11571-012-9216-y
Zhang J-H, Nassef A, Mahfouf M et al (2006) Modeling and analysis of HRV under physical and mental workloads. In: Proceedings of the 6th IFAC symposium on modeling and control in biomedical systems, Reims, pp 189–194, 20–22 Sept 2006
Zhang J-H, Wang X-Y, Mahfouf M et al (2008) Use of heart rate variability analysis for quantitatively assessing operator’s mental workload. In: Proceedings of the international conference on biomedical engineering and informatics (BMEI), vol 1, Sanya, pp 668–672
Acknowledgments
The authors would also like to thank Prof. D Manzey, Technical University Berlin, Germany, for providing the AUTO-CAMS software used in our OFS data acquisition experiments. The work supported by the National Natural Science Foundation of China (under Grant No. 61075070 and Key Grant No. 11232005) and a Senior Research Fellowship from the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, JH., Qin, PP., Raisch, J. et al. Predictive modeling of human operator cognitive state via sparse and robust support vector machines. Cogn Neurodyn 7, 395–407 (2013). https://doi.org/10.1007/s11571-013-9242-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-013-9242-4