Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG based methods to monitor task loading during naturalistic computer-based work.
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References
1.
Baker, S. C., Rogers, R. D., Owen, A. M., Frith, C. D., Dolan, R. J., Frackowiak, R. S., & Robbins, T. W. (1996). Neural systems engaged by planning: A PET study of the Tower of London task. Neuropsychologia, 34, 515-526.
2.
Bullock, T. H., & McClune, M. C. (1989). Lateral coherence of the electroencephalogram: A new measure of brain synchrony. Electroencephalography and Clinical Neurophysiology, 73, 479-498.
3.
Bunge, S. A., Klingberg, T., Jacobsen, R. B., & Gabrieli, J. D. (2000). A resource model of the neural basis of executive working memory. Proceedings of the National Academy of Sciences, USA, 97, 3573-3578.
4.
Byrne, E. A., & Parasuraman, R. (1996). Psychophysiology and adaptive information. Biological Psychology, 42, 249-268.
5.
Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of human-computer interaction.Mahwah, NJ: Erlbaum.
6.
Carpenter, P. A., Just, M. A., & Reichle, E. D. (2000). Working memory and executive function: Evidence from neuroimaging. Current Opinion in Neurobiology, 10, 195-199.
7.
Comstock, J. R., & Arnegard, R. J. (1992). The multi-attribute task battery for human operator workload and strategic behavior research (NASA Tech. Memorandum 104174). Hampton, VA: Langley Research Center.
8.
Du, W., Leong, H., & Gevins, A. (1994, June). Ocular artifact reduction by adaptive filtering. Presented at the 7th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, Quebec City, Canada.
9.
Elul, R. (1969). The physiological interpretation of amplitude histograms of the EEG. Electroencephalography and Clinical Neurophysiology, 27, 703-704.
10.
Fournier, L. R., Wilson, G. F., & Swain, C. R. (1999). Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: Manipulations of task difficulty and training. International Journal of Psychophysiology, 31, 129-145.
11.
Garavan, H., Ross, T. J., Li, S., & Stein, E. A. (2000). A parametric manipulation of central executive functioning. Cerebral Cortex, 10, 585-592.
12.
Gevins, A., & Smith, M. E. (1999). Detecting transient cognitive impairment with EEG pattern recognition methods. Aviation, Space and Environmental Medicine, 70, 1018-1024.
13.
Gevins, A., & Smith, M. E. (2000). Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cerebral Cortex, 10, 829-839.
14.
Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., & Rush, G. (1998). Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors, 40, 79-91.
15.
Gevins, A., Smith, M. E., McEvoy, L., & Yu, D. (1997). High-resolution EEG mapping of cortical activation related to working memory: Effects of task difficulty, type of processing, and practice. Cerebral Cortex, 7, 374-385.
16.
Gevins, A. S., & Schaffer, R. E. (1980). A critical review of electroencephalographic EEG correlates of higher cortical functions. CRC Critical Reviews in Bioengineering, 4, 113-164.
17.
Gevins, A. S., Zeitlin, G. M., Doyle, J. C., Schaffer, R. E., & Callaway, E. (1979). EEG patterns during "cognitive" tasks: II. Analysis of controlled tasks. Electroencephalography and Clinical Neurophysiology, 47, 704-710.
18.
Gevins, A. S., Zeitlin, G. M., Yingling, C. D., Doyle, J. C., Dedon, M. F., Schaffer, R. E., Roumasset, J. T., & Yeager, C. L. (1979). EEG patterns during "cognitive" tasks: I. Methodology and analysis of complex behaviors. Electroencephalography and Clinical Neurophysiology, 47, 693-703.
19.
Glass, A. (1966). Comparison of the effect of hard and easy mental arithmetic upon blocking of the occipital alpha rhythm. Quarterly Journal of Experimental Psychology, 8, 142-152.
20.
Gopher, D., Brickner, M., & Navon, D. (1982). Different difficulty manipulations interact differently with task emphasis: Evidence for multiple resources. Journal of Experimental Psychology: Human Perception and Performance, 8, 146-157.
21.
Gundel, A., & Wilson, G. F. (1992). Topographical changes in the ongoing EEG related to the difficulty of mental tasks. Brain Topography, 5, 17-25.
22.
Hart, S. G., & Staveland, L. E. (1988). Development of NASATLX (Task Load Index): Results of experimental and theoretical research. In P. A. Hancock, & N. Meshakati (Eds.), Human mental workload (pp. 139-183). Amsterdam: North-Holland.
23.
Humphrey, D., & Kramer, A. F. (1994). Toward a psychophysiological assessment of dynamic changes in mental workload. Human Factors, 36, 3-26.
24.
Inouye, T., Shinosaki, K., Iyama, A., Matsumoto, Y., Toi, S., & Ishihara, T. (1994). Potential flow of frontal midline theta activity during a mental task in the human electroencephalogram. Neuroscience Letters, 169, 145-148.
25.
Ishii, R., Shinosaki, K., Ukai, S., Inouye, T., Ishihara, T., Yoshimine, T., Hirabuki, N., Asada, H., Kihara, T., Robinson, S. E., & Takeda, M. (1999). Medial prefrontal cortex generates frontal midline theta rhythm. Neuroreport, 10, 675-679.
26.
Kendall, M. G. (1963). Rank order correlation methods (3rd ed.).London: Griffin.
27.
Klimesch, W., Schmike, H., & Pfurtscheller, G. (1993). Alpha frequency, cognitive load, and memory performance. Brain Topography, 5, 241-251.
28.
Larson, C. L., Davidson, R. J., Abercrombie, H. C., Ward, R. T., Schaefer, S. M., Jackson, D. C., Holden, J. E., & Perlman, S. B. (1998). Relations between PET-derived measures of thalamic glucose metabolism and EEG alpha power. Psychophysiology, 35, 162-169.
29.
Lopes da Silva, F. H. (1991). Neural mechanisms underlying brain waves: From neural membranes to networks. Electroencephalography and Clinical Neurophysiology, 79, 81-93.
30.
Lopes da Silva, F. H., & Storm van Leeuwen, W. (1978). The cortical alpha rhythm in dog: The depth and surface profile of phase. In M. A. B. Brazier, & H. Petsche (Eds.), Architectonics of the cerebral cortex (pp. 319-333). New York: Raven.
31.
Lopes da Silva, F. H., Vos, J. E., Mooibreck, J., & Van Rotterdam A. (1980). Relative contributions of intracortical and thalamocortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroencephalography and Clinical Neurophysiology, 50, 449-456.
32.
McCallum, W. C., Cooper, R., & Pocock, P. V. (1988). Brain slow potential and ERP changes associated with operator load in a visual tracking task. Electroencephalography and Clinical Neurophysiology, 69, 453-468.
33.
Miyata, Y., Tanaka, Y., & Hono, T. (1990). Long-term observation on Fm-theta during mental effort. Neuroscience, 16, 145-148.
34.
Molloy, R., & Parasuraman, R. (1996). Monitoring an automated system for a single failure: Vigilance and task complexity effects. Human Factors, 38, 311-322.
35.
Morrison, J. G., & Gluckman, J. P. (1994). Definitions and prospective guidelines for the application of automation. In M. Mouloua, & R. Parasuraman (Eds.), Human performance in automated systems: Current research and trends (pp. 256-263). Hillsdale, NJ: Erlbaum.
36.
Mulholland, T. (1995). Human EEG, behavioral stillness and biofeedback. International Journal of Psychology, 19, 263-279.
37.
Nygren, T. E. (1991). Psychometric properties of subjective workload measurement techniques: Implications for their use in the assessment of perceived mental workload. Human Factors, 33, 17-33.
38.
Parasuraman, R. (1990). Event-related brain potentials and human factors research. In J. W. Rohrbaugh, R. Parasuraman, & R. J. Johnson (Eds.), Event-related brain potentials (pp. 279-300). New York: Oxford University Press.
39.
Parasuraman, R., Molloy, R., & Singh, I. L. (1993). Performance consequences of automation-induced "complacency". International Journal of Aviation Psychology, 3, 1-23.
40.
Parasuraman, R., Mouloua, M., & Molloy, R. (1996). Effects of adaptive task allocation on monitoring of automated systems. Human Factors, 38, 665-679.
41.
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man and Cybernetics Part A: Systems and Humans, 30, 286-297.
42.
Paus, T., Koski, L., Caramanos, Z., & Westbury, C. (1998). Regional differences in the effects of task difficulty and motor output on blood flow response in the human anterior cingulate cortex: A review of 107 PET activation studies. Neuroreport, 9, R37-R47.
43.
Pellouchoud, E., Smith, M. E., McEvoy, L., & Gevins, A. (1999). Mental effort-related EEG modulation during video-game play: Comparison between juvenile subjects with epilepsy and normal control subjects. Epilepsia, 40(Suppl. 4), 38–43.
44.
Pfurtscheller, G., & Klimesch, W. (1992). Functional topography during a visuoverbal judgment task studied with event-related desynchronization mapping. Journal of Clinical Neurophysiology, 9, 120-131.
45.
Posner, M. E., & Peterson, S. E. (1990). The attentional system of the human brain. Annual Review of Neuroscience, 13, 25-42.
46.
Posner, M. I., & Rothbart, M. K. (1992). Attentional mechanisms and conscious experience. In A. D. Milner, & M. D. Rugg (Eds.), The neuropsychology of consciousness (pp. 91-111). San Diego: Academic.
47.
Raskin, J. (2000). Humane interface: New directions for designing interactive systems.Boston: Addison-Wesley.
48.
Sadato, N., Nakamura, S., Oohashi, T., Nishina, E., Fuwamoto, Y., Waki, A., & Yonekura, Y. (1998). Neural networks for generation and suppression of alpha rhythm: A PET study. Neuroreport, 30, 893-897.
49.
Sharbrough, F., Chatrian, G., Lesser, R. P., Luders, H., Nuwer, M., & Picton, T. W. (1990). Guidelines for standard electrode position nomenclature.Bloomfield, IL: American EEG Society.
50.
Smith, M. E., & Gevins, A. (1998, September). EEG-based monitoring of cognitive load during human-computer interaction. Presented at the Annual Meeting of the Society for Psychophysiological Research, Boulder, CO.
51.
Smith, M. E., McEvoy, L. K., & Gevins, A. (1999). Neurophysiological indices of strategy development and skill acquisition. Cognitive Brain Research, 7, 389-404.
52.
Sterman, M. B., Mann, C. A., Kaiser, D. A., & Suyenobu, B. Y. (1994). Multiband topographic analysis of a simulated visuomotor aviation task. International Journal of Psychophysiology, 16, 49-56.
53.
Tou, J. T., & Gonzalez, R. C. (1974). Pattern recognition principles. Reading, MA: Addison-Wesley.
54.
Wickens, C. D. (1991). Processing resources and attention. In D. L. Damos (Ed.), Multiple-task performance (pp. 1-34). London: Taylor & Francis.
55.
Yamamoto, S., & Matsuoka, S. (1990). Topographic EEG study of visual display terminal VDT performance with special reference to frontal midline theta waves. Brain Topography, 2, 257-267.
56.
Michael E.Smith is principal neuroscientist at SAM Technology, San Francisco, CA. He received his Ph.D. in cognitive psychology in 1986 from the University of California, Los Angeles.
57.
Alan Gevins is The founder and president of SAM Technology, San Francisco, CA. He received his B.S. in psychology and engineering in 1967 from the Massachusetts Institute of Technology.
58.
Halle Brown is an independent consultant. She received her Ph.D. in psychology in 1995 from Harvard University.
59.
Arati Karnik is pursuing premedical studies at Bryn Mawr. She received her B.S.E. in bioengineering in 1996 from the University of Pennsylvania.
60.
Robert Du is a digital signal processing engineer with the Sony Corporation. He received his Ph.D. in electrical engineering in 1992 from the University of Victoria, British Colombia.
