Modeling mental workload using EEG features for intelligent systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6787 LNCS, Issue , 2011, Pages 50-61

  Chaouachi, Maher ^a   Jraidi, Imène ^a   Frasson, Claude ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

cognitive workload; EEG; ITS

Indexed keywords

COGNITIVE TASK; COGNITIVE WORKLOADS; COMMUNICATION AND INTERACTION; EDUCATIONAL CONTEXT; INTELLIGENT TUTORING SYSTEM; ITS; MENTAL STATE; MENTAL WORKLOAD; OPERATIONAL ENVIRONMENTS; SPECTRAL FEATURE;

COMPUTER AIDED INSTRUCTION; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; INTELLIGENT SYSTEMS; MATHEMATICAL MODELS;

COGNITIVE SYSTEMS;

EID: 79960333279     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22362-4_5     Document Type: Conference Paper

References (28)

1. Berka, C., Levendowski, D.J., Cvetinovic, M.M., et al.: Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset. International Journal of Human-Computer Interaction 17, 151-170 (2004)
2. Gevins, A., Smith, M.E.: Assessing fitness-for-duty and predicting performance with cognitive neurophysiologic measures. In: Caldwell, J.A., Wesensten, N.J. (eds.) Biomonitoring for Physiological and Cognitive Performance during Military Operations. Proceedings of SPIE, vol. 5797, 18, pp. 127-138 (2005)
3. Murata, A.: An Attempt to Evaluate Mental Workload Using Wavelet Transform of EEG. Human Factors: The Journal of the Human Factors and Ergonomics Society 47, 498-508 (2005)
4. Smith, M.E., Gevins, A.: Neurophysiologic monitoring of mental workload and fatigue during operation of a flight simulator. In: Caldwell, J.A., Wesensten, N.J. (eds.) Proceedings of SPIE Defense and Security Symposium, Biomonitoring for Physiological and Cognitive Performance during Military Operations, pp. 116-126. SPIE, Orlando (2005)
5. Wilson, G.: Operator functional state assessment for adaptive automation implementation. In: Caldwell, J.A., Wesensten, N.J. (eds.) Proceedings of SPIE Defense and Security Symposium, Biomonitoring for Physiological and Cognitive Performance during Military Operations, pp. 100-104. SPIE, Orlando (2005)
6. Parasuraman, R.: Neuroergonomics: the brain at work. Oxford University Press, New York (2005)
7. Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology 40, 187-195 (1995)
8. Prinzel, L.J., Freeman, F.G., Scerbo, M.W.: A Closed-Loop System for Examining Psychophysiological Measures for Adaptive Task Allocation. International Journal of Aviation Psychology 10, 393-410 (2000)
9. Sterman, M.B., Kaiser, D.A., Mann, C.A., et al.: Application of Quantitative EEG Analysis to Workload Assessment in an Advanced Aircraft Simulator. In: Human Factors and Ergonomics, Seattle, Washington, USA, pp. 118-121 (1993)
10. Hancock, P.A., Warm, J.S.: A Dynamic Model of Stress and Sustained Attention. Human Factors and Ergonomics Society 31, 519-537 (1989)
11. Arroyo, I., Woolf, B.: Inferring learning and attitudes from a Bayesian Network of log file data. In: Proceeding of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pp. 33-40. IOS Press, Amsterdam (2005)
12. Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting Student Misuse of Intelligent Tutoring Systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531-540. Springer, Heidelberg (2004)
13. Beck, J.E.: Engagement tracing: using response times to model student disengagement. In: Proceeding of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pp. 88-95. IOS Press, Amsterdam (2005)
14. Johns, J., Woolf, B.: A dynamic mixture model to detect student motivation and proficiency. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, pp. 163-168. AAAI Press, Boston (2006)
15. Arroyo, I., Cooper, D.G., Burleson, W., et al.: Emotion Sensors Go To School. In: Proceeding of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, pp. 17-24. IOS Press, Amsterdam (2009)
16. D'Mello, S., Craig, S., Witherspoon, A., et al.: Automatic detection of learner's affect from conversational cues. User Modeling and User-Adapted Interaction 18, 45-80 (2008)
17. Forbes-Riley, K., Rotaru, M., Litman, D.J.: The relative impact of student affect on performance models in a spoken dialogue tutoring system. User Modeling and User- Adapted Interaction 18, 11-43 (2008)
18. Stevens, R., Galloway, T., Berka, C.: Integrating EEG Models of Cognitive Load with Machine Learning Models of Scientific Problem Solving. In: Schmorrow, D., Stanney, K., Reeves, L. (eds.) Augmented Cognition: Past, Present and Future, pp. 55-65. Strategic Analysis, Inc., Arlington (2006)
19. Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple sychophysiological measures. Int. J. Aviat. Psychol. 12, 3-18 (2004)
20. Gevins, A., Smith, M.E.: Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomics Science 4, 113-131 (2003)
21. Kohlmorgen, J., Dornhege, G., Braun, M., et al.: Improving human performance in a real operating environment through real-time mental workload detection. In: Toward Brain- Computer Interfacing, pp. 409-422. MIT Press, Cambridge (2007)
22. Heger, D., Putze, F., Schultz, T.: Online Workload Recognition from EEG Data during Cognitive Tests and Human-Machine Interaction. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds.) KI 2010. LNCS, vol. 6359, pp. 410-417. Springer, Heidelberg (2010)
23. Honal, M., Schultz., T.: Determine task demand from brain activity. In: 3rd International Conference on Bio-inspired Systems and Signal Processing (2008)
24. Berka, C., Levendowski, D.J., Lumicao, M.N., et al.: EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks. Aviation, Space, and Environmental Medicine 78, B231-B244 (2007)
25. Stevens, R., Galloway, T., Berka, C.: EEG-Related Changes in Cognitive Workload, Engagement and Distraction as Students Acquire Problem Solving Skills. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 187-196. Springer, Heidelberg (2007)
26. Stevens, R.H., Galloway, T., Berka, C.: Integrating innovative neuro-educational technologies (I-Net) into K-12 science classrooms. In: Proceedings of the 3rd International Conference on Foundations of Augmented Cognition, Beijing, China, pp. 47-56 (2007)
27. Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In: Hancock, P.A., Meshkati, N. (eds.) Human Mental Workload, pp. 139-183. North-Holland, Amsterdam (1988)
28. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)