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Psychophysiology, Cortical Arousal and Dynamical Complexity (DCX)

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Nonlinear Dynamics, Psychology, and Life Sciences

Abstract

Recent studies in brain dynamics have utilized a dependent variable calculated from the electroencephalogram (EEG) known as dimensional complexity (DC x ), where variables such as scalp locus, cognitive task difficulty, or cortical arousal, are manipulated to test quantitative hypotheses regarding brain-state changes. The technique has been criticised on technical and theoretical grounds, yet its application to experimental time series in many domains has succeeded in yielding information about cortical activity which either complements or surpasses spectral band analysis, and other linear-stochastic techniques. The aim of this paper is to provide a pedagogical review of the contribution of dimensional complexity studies in understanding the psychophysiology of cortical arousal by outlining strategies for the successful estimation of DC x as an empirical measure.

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REFERENCES

  • Ackerman, P. T., Dykman, R. A., Oglesby, D. M., & Newton, J. E. (1994). EEG power spectra of children with dyslexia, slow learners, and normally reading children with ADD during verbal processing. Journal of Learning Disabilities, 27, 619–630.

    Google Scholar 

  • Albano, A. M., Passamante, A., & Farrell, M. E. (1991). Using higher-order correlations to define an embedding window. Physica D, 54, 85–97.

    Google Scholar 

  • Anderson, K. (1994). Impulsivity, caffeine and task difficulty: A within-subjects test of the Yerkes-Dodson Law. Personality & Individual Differences, 16, 813–829.

    Google Scholar 

  • Babloyantz, A. & Destexhe, A. (1986). Low-dimensional chaos in an instance of epilepsy. Proceedings of the National Academy of Sciences USA, 83 3513–3517.

    Google Scholar 

  • Babloyantz, A., Salazar, J. M., & Nicolis, C. (1985). Evidence of chaotic dynamics of brain activity during the sleep cycle. Physics Letters, 111 152–156.

    Article  Google Scholar 

  • Berger, H. (1929). Über das elektrenkephalogramm des menchen. Archiv für Psychiatrie und Nervenkrankheitem 87, 527–570.

    Google Scholar 

  • Birbaumer, N., Lang, P. J., Cook, E., Elbert, T., Luetzenberger, W., & Rockstroh, H. (1988). Slow brain potentials, imagery and hemispheric differences. International Journal of Neuroscience, 39 101–116.

    Google Scholar 

  • Born, J., Malle, M., Marshall, L., Pietrowsky, R., & Lutzenberger, W. (1995) (Abstract). EEG during convergent and divergent thinking. Paper presented to the Thirty-Fifth Annual Meeting of the Society for Psychophysiological Research, Toronto, Canada.

  • Chen, P. (1988). Empirical and theoretical evidence of economic chaos. Systems Dynamics Review, 4 81–108.

    Google Scholar 

  • Corsi-Cabrera, M., Ramos, J., Guevara, M. A., & Arce, C. (1993). Gender differences in the EEG during cognitive activity. International Journal of Neuroscience, 73 257–264.

    Google Scholar 

  • Czobor, P. & Volavka, J. (1993). Quantitative electroencephalogram examination of effects of risperidone in schizophrenic patients. Journal of Clinical Psychopharmacology; 13 332–342.

    Google Scholar 

  • Davidson, R. J., Chapman, J. P., Chapman, L. J., & Henriques, J. B. (1990). Asymmetrical brain electrical activity discriminates between psychometrically-matched verbal and spatial cognitive tasks. Psychophysiology, 27 528–543.

    Google Scholar 

  • Eysenck, H. J. (1967). The Biological Basis of Personality. Springfield, IL: Thomas.

    Google Scholar 

  • Ferber, G. (1987). Treatment of some nonstationarities in the EEG. Neuropsychobiology, 17 100–104.

    Google Scholar 

  • Fink, M. (1968). EEG classification of psychoactive compounds in man: A review and theory of behavioral associations. In D. H. Efton (Ed.). Psychopharmacology: A Review of Progress 1957–1967. Washington: Public Health Service.

    Google Scholar 

  • Fink, M. (1969). EEG and human pharmacology. Annual Review of Pharmacology, 9 241–258.

    Google Scholar 

  • Frank, G. W., Lookman, T., Nerenberg, M. A. H., Essex, C., Lemieux, J., & Blume, W. (1990). Chaotic time series analysis of epileptic seizures. Physica D, 46 427–438.

    Google Scholar 

  • Fraser, A. M. (1989). Reconstructing attractors from scalar time series: A comparison of singular system and redundancy criteria. IEEE Transactions on Information Theory, 35 245–262.

    Article  Google Scholar 

  • Freeman, W. J. (1987). Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biological Cybernetics, 56 139–150.

    Article  Google Scholar 

  • Gale, A. (1973). The psychophysiology of individual differences: studies of extroversion and the EEG. In P. Kline (Ed.) New Approaches in Psychological Measurement Pp. 211–256. London: Wiley.

    Google Scholar 

  • Goldstein, S. & Ingersoll, B. (1993). Controversial treatments for children with ADHD and impulse disorders. In L. F. Koziol, C. E. Stout, & D. H. Ruben (Eds.) Handbook of Childhood Impulse Disorders and ADHD: Theory and practice. (pp. 144–160). Springfield, IL: Charles C Thomas.

    Google Scholar 

  • Goldberger, A. L. & Rigney, D. R. (1990). Sudden death is not chaos. In S. Krasner (Ed.), The Ubiquity of Chaos (pp. 23–34). Washington D.C: American Association for the Advancement of Science.

    Google Scholar 

  • Grassberger, P. & Procaccia, I. (1983). Measuring the strangeness of strange attractors. Physica D, 9 189–208.

    Google Scholar 

  • Gray, C. M., König, P., Engel, A., & Singer, W. (1989). Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization with reflects global stimulus properties. Nature, 338 334–337.

    Article  PubMed  Google Scholar 

  • Gregson, R. A. M., Campbell, E. A., & Gates, R. (1993). Cognitive load as a determinant of the dimensionality of the electroencephalogram: A replication study. Biological Psychology, 35 165–178.

    Article  Google Scholar 

  • Hayashi, H., Nakao, M., & Hirakawa, K. (1982). Chaos in the self-sustained oscillation of an excitable membrane under sinusoidal stimulation. Physics Letters, 88 265–266.

    Article  Google Scholar 

  • Heath, R. A. (1995, May). A methodology for cognitive research based on nonlinear system theory. Paper presented to the Third Meeting of the Australasian Cognitive Science Society, Brisbane, Australia.

  • Houlihan, M., Pritchard, W. S., Krieble, K. K., Robinson, J. H., & Duke, D. W. (in press). Effects of cigarette smoking on EEG spectral-band power, dimensional complexity, and nonlinearity during reaction-time task performance. Psychophysiology.

  • Intrilligator, J. & Polich, J. (1994). On the relationship between background EEG and the P300 event-related potential. Biological Psychology; 37 207–218.

    Article  Google Scholar 

  • Itil, T. M. (1977). Computer analysis of sleep EEG in psychiatry. Waking and Sleeping, 1 343–357.

    Google Scholar 

  • Ivanov, D. K., Posch, H., & Stumpf, C. (1996). Statistical measures derived from the correlation integrals of physiological time series. Chaos, 6 243–253.

    Article  Google Scholar 

  • Kantz, H. & Schreiber, T. (1995). Dimension estimates and physiological data. Chaos, 5 143–154.

    Article  Google Scholar 

  • Klimesch, W., Doppelmayr, M., Pachinger, T., & Russegger, H. (1997). Event-related desynchronization in the alpha band and the processing of semantic information. Brain Research: Cognitive Brain Research, 6 83–94.

    Article  Google Scholar 

  • Leocani, L., Toro, C., Manganotti, P., Zhuang, P., & Hallett, M. (1997). Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements. Electroencephalography and Clinical Neurophysiology, 104 199–206.

    Article  Google Scholar 

  • Lindsley, D. (1957). Psychophysiology of motivation. In M. Jones (Ed.) Nebraska Symposium on Motivation 1957 (pp. 44–105). Lincoln, NE: University of Nebraska Press.

    Google Scholar 

  • Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20 130–141.

    Article  Google Scholar 

  • Lubar, J. F. (1991). Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self-Regulation, 16 201–25.

    Google Scholar 

  • Lutzenberger, W., Flor, H., & Birbaumer, N. (1997). Enhanced dimensional complexity of the EEG during memory for personal pain in chronic pain patients. Neuroscience Letters, 226 167–170.

    Article  Google Scholar 

  • Mayer-Kress, G. & Layne, S. P. (1987). Dimensionality of the human electroencephalogram. In S. H. Koslow, A. J. Mandell, & M. F. Shlesinger (Eds.). Perspectives in Biological Dynamics and Theoretical Medicine (Annals of the New York Academy of Science, 504; pp. 62–87). New York: New York Academy of Science.

    Google Scholar 

  • Mecklinger, A., Kramer, A. F., & Strayer, D. L. (1992). Event related potentials and EEG components in a semantic memory search task. Psychophysiology, 29 104–119.

    Google Scholar 

  • Mitra, M. & Skinner, J. E. (1992). Low-dimensional chaos maps learning in a model neuropil (olfactory bulb). Integrative Physiological and Behavioral Science, 27 304–322.

    Google Scholar 

  • Möller, M., Lange, W., Mitschke, F., Abraham, N. B., & Hübner, U. (1989). Errors from digitizing and noise in estimating attractor dimension. Physics Letters, 138A 4518–4521.

    Google Scholar 

  • Molnár, M. & Skinner, J. E. (1992). Low-dimensional chaos in event-related potentials. International Journal of Neuroscience, 66 263–276.

    Google Scholar 

  • Morruzzi, G. & Magoun, H. (1949). Brain stem reticular formation and activation of the EEG. Electroencephalography and Clinical Neurophysiology, 1 455–473.

    Google Scholar 

  • Norman, D. (1984). Theories and models in cognitive psychology. In E. Donchin (Ed.). Cognitive psychophysiology: Event-Related Potentials and the Study of Cognition. The Carmel Conferences Volume 1 (pp. 119–138). Hillsdale: Lawrence-Erlbaum.

    Google Scholar 

  • Nunez, P. L. (1989). Generation of human EEG by a combination of long and short range neocortical interactions. Brain Topography, 1 199–215.

    Google Scholar 

  • Packard, N. H., Crutchfield, J. P., Farmer, J. D. & Shaw, R.S. (1980). Geometry from a time series. Physical Review Letters, 45 712–720.

    Article  Google Scholar 

  • Petsche, H. (1996). Approaches to verbal, visual and musical creativity by EEG coherence analysis. International Journal of Psychophysiology, 24 145–159.

    Article  Google Scholar 

  • Pfurtscheller, G. & Klimesch, W. (1990). Topographical display and interpretation of event-related desynchronization during a visual-verbal task. Brain Topography, 3 85–93.

    Google Scholar 

  • Priessel, H., Lutzenberger, W., Pulvermueller, F. & Birbaumer, N. (1995, November) (Abstract). Electrocortical distinction of nouns and verbs. Paper presented to the Thirty-Fifth Annual Meeting of the Society for Psychophysiological Research, Toronto, Canada.

  • Pritchard, W. S. (1986). Cognitive event-related potential correlates of schizophrenia. Psychological Bulletin, 100 43–66.

    Article  Google Scholar 

  • Pritchard, W. S. (1991). Electroencephalographic effects of cigarette smoking. Psychopharmacology, 104 485–90.

    Google Scholar 

  • Pritchard, W. S., Duke, D. W., Coburn, K. L., & Robinson, J. H. (1992). Nonlinear dynamical electroencephalographic analysis applied to nicotine psychopharmacology and Alzheimer's disease. In P. M. Lippiello, A. C. Collins, J. A. Gray & J. H. Robinson (Eds.). The Biology of Nicotine: Current Research Issues. (pp. 195–214). New York: Raven.

    Google Scholar 

  • Pritchard, W. S., Gilbert, D. G., & Duke, D. W. (1993). Flexible effects of quantified cigarette-smoke delivery on EEG dimensional complexity. Psychopharmacology, 113 95–102.

    Google Scholar 

  • Rapp, P. E. (1993). Chaos in the neurosciences: cautionary tales from the frontier. Biologist, 40 89–94.

    Google Scholar 

  • Rapp, P. E., Bashore, T. R., Martinerie, J. M., Albano, A. B., Zimmerman, I. D., & Mees, A. I. (1989). Dynamics of brain electrical activity. Brain Topography, 2 99–118.

    Google Scholar 

  • Ritzenberg, A. L., Adam, D. R., & Cohen, R. J. (1984). Period multupling—evidence for non-linear behaviour of the canine heart. Nature, 307 159–161.

    Google Scholar 

  • Roeschke, J. & Aldenhoff, J. B. (1992). A nonlinear approach to brain function: Deterministic chaos and sleep EEG. Sleep, 15 95–101.

    Google Scholar 

  • Rosenstein, M. (1993). MTRCHAOS. [Computer Software]. Canton, MA: Dynamical Research.

    Google Scholar 

  • Rosenstein, M., Collins, J. J., & De Luca, C. (in press). Reconstruction expansion as a geometry-based framework for choosing proper delay times. Physica D.

  • Ruelle, D. (1994). Where can one hope to profitably apply the ideas of chaos? Physics Today, 47 24–30.

    Google Scholar 

  • Schaffer, W. M. (1985). Can nonlinear dynamics elucidate mechanisms in ecology and epidemiology? IMA Journal of Mathematics Applied in Medicine & Biology, 2 221–252.

    Google Scholar 

  • Schellenberg, R. & Schwarz, A. (1993). EEG and EP mapping: Possible indicators for disturbed information processing in schizophrenia? Progress in Neuro-Psychopharmacology and Biological Psychiatry, 17 595–607.

    Article  Google Scholar 

  • Sergent, J. (1994). Brain-imaging studies of cognitive functions. Trends in Neuroscience, 17 221–227.

    Article  Google Scholar 

  • Shelhamer, M. (1997). On the correlation dimension of optokinetic nystagmus eye movements: Computational parameters, filtering, nonstationarity, and surrogate data. Biological Cybernetics, 76 237–250.

    Article  Google Scholar 

  • Skarda, C. A. & Freeman, W. J. (1987). How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences, 10 161–195.

    Google Scholar 

  • Skinner, J. E., Molnar, M., & Tomberg, C. (1994). The point correlation dimension: Performance with nonstationary surrogate data and noise. Integrative Physiology and Behavioral Science, 29 217–234.

    Google Scholar 

  • Stam, C. J., van Woerkom, T. C., & Pritchard, W. S. (1995). Use of non-linear EEG measures to characterize EEG changes during mental activity. Electroencephalography and Clinical Neurophysiology, 99 214–224.

    Google Scholar 

  • Strubelt, O. (1969). The influence of reserpine, propranolol and adrenal medullectomy on the hyperglycemic actions of theophylline and caffeine. Archives of Pharmacodynamics, 169 215–224.

    Google Scholar 

  • Sugihara, G., Greenfell, B., & May, R. M. (1990). Distinguishing error from chaos in ecological time series. Philosophical Transactions of the Royal Society London B, 330 235–251.

    Google Scholar 

  • Theiler, J. (1987). Efficient algorithm for estimating the correlation dimension from a set of discrete points. Physical Review A, 36 4456–4462.

    Article  Google Scholar 

  • Theiler, J. (1995). On the evidence for low-dimensional chaos in an epileptic electroencephalogram. Physics Letters A, 196 335–341.

    Article  Google Scholar 

  • Theiler, J. & Lookman, T. (1993). Statistical error in a chord estimator of correlation dimension: the “rule of five.” International Journal of Bifurcations and Chaos, 3 765–771.

    Google Scholar 

  • Watters, P. A. (in press, a). Surrogate data and differentiation analyses of nonlinearity in resting EEG. Applied Signal Processing.

  • Watters, P. A. (in press, b). Persistent temporal correlations in the electroencephalogram. Complexity International.

  • Watters, P. A., Ball, P. J., & Carr, S. C. (1996). Social processes as dynamical processes: Qualitative dynamical systems theory in social psychology. Current Research In Social Psychology, 1.

  • Watters, P. A. & Martin, F. (in press). Frequency-dependent scaling properties of the electroencephalogram. Biological Cybernetics.

  • Watters, P. A., Martin, F., & Schreter, Z. (1997). Caffeine and cognitive performance: The nonlinear Yerkes-Dodson Law. Human Psychopharmacology: Clinical and Experimental, 12 249–257.

    Article  Google Scholar 

  • Watters, P. A., Martin, F., & Schreter, Z. (1998). Quadratic dose-response relationship between caffeine (1-3-7, trimethylxanthine) and EEG correlation dimension. Psychopharmacology, 136 264–271.

    Article  Google Scholar 

  • Wright, J. J. (1997). EEG Simulation: Variation of Spectral Envelope, Pulse Synchrony and ~40 Hz Oscillation. Biological Cybernetics, 76, 181–194.

    Article  Google Scholar 

  • Wright, J. J. & Kydd, R. R. (1992). The electroencephalogram and cortical neural networks. Network, 3 341–362.

    Google Scholar 

  • Wright, J. J. & Liley, D. T. J. (1995). Simulation of electrocortical waves. Biological Cybernetics, 72 347–356.

    Article  Google Scholar 

  • Wright, J. J. & Liley, D. T. J. (1996). Dynamics of the brain at global and microscopic scales: Neural networks and the EEG. Behavioral and Brain Sciences, 19 285–309.

    Google Scholar 

  • Yerkes, R. M. & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18 459–482.

    Google Scholar 

  • Zschocke, S. & Speckmann, E. J. (1993). Basic Mechanisms of the EEG. Boston, MA.: Birkhauser.

    Google Scholar 

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  1. Department of Psychology, University of Newcastle, Callaghan, NSW 2308, AUSTRALIA

    Paul A. Watters

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Watters, P.A. Psychophysiology, Cortical Arousal and Dynamical Complexity (DCX). Nonlinear Dynamics Psychol Life Sci 3, 211–233 (1999). https://doi.org/10.1023/A:1021826816817

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