PMID- 24427214
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20140115
LR  - 20211021
IS  - 1871-4080 (Print)
IS  - 1871-4099 (Electronic)
IS  - 1871-4080 (Linking)
VI  - 7
IP  - 5
DP  - 2013 Oct
TI  - Predictive modeling of human operator cognitive state via sparse and robust 
      support vector machines.
PG  - 395-407
LID - 10.1007/s11571-013-9242-4 [doi]
AB  - 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.
FAU - Zhang, Jian-Hua
AU  - Zhang JH
AD  - 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.
FAU - Qin, Pan-Pan
AU  - Qin PP
AD  - Department of Automation, East China University of Science and Technology, 
      Shanghai, 200237 People's Republic of China.
FAU - Raisch, Jorg
AU  - Raisch J
AD  - Control Systems Group, Technische Universitat Berlin, 10587 Berlin, Germany ; 
      Systems and Control Theory Group, Max Planck Institute for Dynamics of Complex 
      Technical Systems, 39106 Magdeburg, Germany.
FAU - Wang, Ru-Bin
AU  - Wang RB
AD  - Institute of Cognitive Neurodynamics, East China University of Science and 
      Technology, Shanghai, 200237 People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20130120
PL  - Netherlands
TA  - Cogn Neurodyn
JT  - Cognitive neurodynamics
JID - 101306907
PMC - PMC3773327
OTO - NOTNLM
OT  - Human operator cognitive state
OT  - Least squares support vector machine
OT  - Regressive model
OT  - Robustness
OT  - Sparseness
EDAT- 2014/01/16 06:00
MHDA- 2014/01/16 06:01
PMCR- 2014/10/01
CRDT- 2014/01/16 06:00
PHST- 2012/08/27 00:00 [received]
PHST- 2012/12/11 00:00 [revised]
PHST- 2013/01/10 00:00 [accepted]
PHST- 2014/01/16 06:00 [entrez]
PHST- 2014/01/16 06:00 [pubmed]
PHST- 2014/01/16 06:01 [medline]
PHST- 2014/10/01 00:00 [pmc-release]
AID - 9242 [pii]
AID - 10.1007/s11571-013-9242-4 [doi]
PST - ppublish
SO  - Cogn Neurodyn. 2013 Oct;7(5):395-407. doi: 10.1007/s11571-013-9242-4. Epub 2013 
      Jan 20.