The effects of day-to-day variability of physiological data on operator functional state classification

NeuroImage

Volumn 59, Issue 1, 2012, Pages 57-63

  Christensen, James C ^a   Estepp, Justin R ^a   Wilson, Glenn F ^b   Russell, Christopher A ^c  

^a AIR FORCE RESEARCH LABORATORY   (United States)

^b Physiometrex Inc   (United States)

^c Archinoetics LLC   (United States)

Author keywords

EEG; Interday variability; Pattern classification; Workload

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BRAIN FUNCTION; CLASSIFICATION; CLASSIFIER; COGNITION; DAILY LIFE ACTIVITY; ELECTROCARDIOGRAPHY; ELECTROENCEPHALOGRAPHY; ELECTROOCULOGRAPHY; FEMALE; FUNCTIONAL STATUS; HUMAN; HUMAN EXPERIMENT; MALE; NERVOUS SYSTEM ELECTROPHYSIOLOGY; NORMAL HUMAN; OPERATOR; PATTERN CLASSIFICATION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; VOLUNTEER;

ELECTROENCEPHALOGRAPHY; FEMALE; HUMANS; MALE; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; TASK PERFORMANCE AND ANALYSIS; USER-COMPUTER INTERFACE; WORKLOAD; YOUNG ADULT;

EID: 80054095042     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.07.091     Document Type: Article

References (42)

1. Berka C., Levendowski D.J., Cvetinovic M.M., Petrovic M.M., Davis G., Lumicao M.N., Zivkovic V.T., Popovic M.V., Olmstead R. Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. International Journal of Human Computer Interaction 2004, 17:151-170.
2. Birbaumer N. Breaking the silence: brain-computer-interfaces (BCI) for communication and motor control. Psychophysiology 2006, 43:517-532.
3. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer, New York.
4. Blanco J.A., Stead M., Krieger A., Viventi J., Marsh W.R., Lee K.H., Worrell G.A., Litt B. Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients. Journal of Neurophysiology 2010, 104(5):2900-2912.
5. Burgress A., Gruzelier J. Individual reliability of amplitude distribution in topographical mapping of EEG. Electroencephalography and Clinical Neurophysiology 1993, 86:210-223.
6. Byrne E.A., Parasuraman R. Psychophysiology and adaptive automation. Biological Psychology 1996, 42:249-268.
7. Cauwenberghs G., Poggio T. Incremental and decremental support vector machine learning. Advances in Neural Information Processing Systems 13 2001, 409-415. MIT Press, Cambridge, MA. T.K. Leen, T.G. Dietterich, V. Tresp (Eds.).
8. Coburn K.L., Lauterbach E.C., Boutros N.N., Black K.J., Arciniegas D.B., Coffey C.E. The value of quantitative electroencephalography in clinical psychiatry: a report by the Committee on Research of the American Neuropsychiatric Association. The Journal of Neuropsychiatry and Clinical Neurosciences 2006, 18:460-500.
9. Comstock J.R., Arnegard R.J. The multi-attribute task battery for human operator workload and strategic behavior research. NASA Technical Memorandum No. 104174 1992.
10. De Martino F., Gentile F., Esposito F., Balsic M., Di Sallea F., Goebel R., Formisano E. Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers. NeuroImage 2007, 34(1):177-194.
11. Freeman F.G., Mikulka P.J., Prinzel L.J., Scerbo M.W. Evaluation of an adaptive automation system using three EEG indices with a visual tracking task. Biological Psychology 1999, 50:61-76.
12. Garrett D., Peterson D.A., Anderson C.W., Thaut M.H. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2003, 11(2):141-144.
13. Gevins A., Smith M.E., Leong H., McEvoy L.K., Whitfield S., Du R., Rush G. Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors 1998, 40(1):79-91.
14. Haynes J.-D., Rees G. Decoding mental states from brain activity in humans. Nature Reviews. Neuroscience 2006, 7:523-534.
15. Huang Y., Erdogmus D., Pavel M., Mathan S., Hild K.E. A framework for rapid visual image search using single-trial brain evoked responses. Neurocomputing 2011, 74:2041-2051.
16. Jasper H.H. Report of the committee on methods of clinical examination. Electroencephalography and Clinical Neurophysiology 1958, 10:370-375.
17. Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain. Nature Neuroscience 2005, 8(5):679-685.
18. Klöppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Jack C.R., Ashburner J., Frackowiak R.S.J. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 131:681-689.
19. Kong X., Wilson G.F. A new EOG-based eyeblink detection algorithm. Behavior Research Methods 1998, 30(4):713-719.
20. Kriegeskorte N., Simmons W.K., Bellgowan P.S., Baker C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience 2009, 12:535-540.
21. Krusienski D.J., Grosse-Wentrup M., Galan F., Coyle D., Miller K.J., Forney E., Anderson C. Critical issues in state-of-the-art brain-computer interface signal processing. Journal of Neural Engineering 2011, 8(2):1-8.
22. Lal T.N., Schroder M., Hinterberger T., Weston J., Bogdan M., Birbaumer N., Scholkopf B. Support vector channel selection in BCI. IEEE Transactions on Biomedical Engineering 2004, 51(6):1003-1010.
23. Lippmann, R.P. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22.
24. McEvoy L.K., Smith M.E., Gevins A. Test-retest reliability of cognitive EEG. Clinical Neurophysiology 2000, 111:457-463.
25. McGonigle D.J., Howseman A.M., Athwal B.S., Friston K.J., Frackowiak R.S.J., Holmes A.P. Variability in fMRI: an examination of intersession differences. NeuroImage 2000, 11:708-734.
26. McGuirl J.M., Sarter N.B. Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Human Factors 2006, 48(4):656-665.
27. Parasuraman R., Wilson G.F. Putting the brain to work: neuroergonomics past, present, and future. Human Factors 2008, 50(3):468-474.
28. Pleydell-Pearce C.W., Whitecross B.T., Dickson B.T. Multivariate analysis of EEG: predicting cognition basis of frequency decomposition, inter-electrode correlation, coherence, cross phase and cross power. IEEE Computer Society 2003, 131-141. The Printing House USA. R.H. Sprague (Ed.).
29. Poggio T., Rifkin R., Mukherjee S., Niyogi P. General conditions for predictivity in learning theory. Nature 2004, 428:419-422.
30. Pollock V.E., Schneider L.S., Lyness S.A. Reliability of topographic quantitative EEG amplitude in health late-middle-aged and elderly subjects. Electroencephalography and Clinical Neurophysiology 1991, 79:0-26.
31. Refinetti R. Circadian Physiology 1999, CRC, Boca Raton.
32. Salinsky M.C., Oken B.S., Morehead L. Test-retest reliability in EEG frequency analysis. Electroencephalography and Clinical Neurophysiology 1991, 79:383-392.
33. Satti A., Guan C., Prasad G., Coyle D. A covariate shift minimisation method to alleviate non-stationarity effects for an adaptive brain-computer interface. Proceedings of the 20th International. Conference on Pattern Recognition 2010, 105-108.
34. Shelley J., Backs R.W. Categorizing EEG waveform length in simulated driving and working memory dual-tasks using feed-forward neural networks. Foundations of Augmented Cognition 2006, 155-161. Strategic Analysis, Inc.
35. Smith S.M., Beckmann C.F., Ramnani N., Woolrich M.W., Bannister P.R., Jenkinson M., Matthews P.M., McGonigle D.J. Variability in fMRI: a re-examination of intersession differences. Human Brain Mapping 2005, 24:248-257.
36. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J. Least Squares Support Vector Machines 2002, World Scientific, Singapore.
37. Widrow B., Lehr M.A. 30years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE 1990, 78:1415-1442.
38. Wilson G.F., Fisher F. The use of cardiac and eye blink measures to determine flight segment in F4 crews. Aviation, Space, and Environmental Medicine 1991, 62:959-961.
39. Wilson G.F., Russell C.A. Operator functional state classification using psychophysiological features in an air traffic control task. Human Factors 2003, 45(3):381-389.
40. Wilson G.F., Russell C.A. Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Human Factors 2003, 45(4):635-643.
41. Wilson G.F., Russell C.A. Performance enhancement in a UAV task using psychophysiological determined adaptive aiding. Human Factors 2007, 49:1005-1019.
42. Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M. Brain-computer interfaces for communication and control. Clinical Neurophysiology 2002, 113:767-791.