Abstract
We investigate the correlation between temporal complexity of EEG signal and the underlining neural activities. Fractal geometry has been proved useful in quantifying complexities of dynamical signals. Temporal fractal dimension of EEG signals provides a new neurophysiological measure. In order to better understand what the complexity measure reveals about the underling brain process, a further exploration on the neuronal generators of fractal geometry characteristics of EEG is conducted in this study. Our investigation suggests that the temporal fractal measure of EEG signals can be related to the activity diversity of neuronal population activities. The complexity measure also gives an indication on the change in synchronization state under certain mental conditions. These assumptions are supported by experimental evidence from the visual cortex and sensorimotor cortex. This work helps give an interpretation of the obtained results of the temporal complexity analysis on EEG signals and may be useful in further investigating the covert steps of brain information processing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Epstein, C.M.: Introduction to EEG and Evoked Potentials. Lippincott Williams & Wilkins, Philadelphia (1983)
Takens, F.: Detecting Strange Attractors in Turbulence. In: Rand, D.A., Young, L.S. (eds.) Dynamical Systems and Turbulence, Warwick. LNM, vol. 898, pp. 361–381. Springer, Heidelberg (1981)
Faure, P., Korn, H.: Is There Chaos in the Brain? I. Concepts of Nonlinear Dynamics and Methods of Investigation. C. R. Acad. Sci. Paris, Ser. III 324, 773–793 (2001)
Korn, H., Faure, P.: Is There Chaos in the Brain? II. Experimental Evidence and Related Models. C. R. Biologies 326, 787–840 (2003)
Lutzenberger, W., Preissl, H., Pulvermiiller, F.: Fractal Dimension of Electroencephalographic Time Series and Underlying Brain Processes. Biological Cybernetics 73, 477–482 (1995)
Babloyantz, A., Salazar, J.M., Nicolis, G.: Evidence of Chaotic Dynamics of Brain Activity During the Sleep Cycle. Phys. Lett. A 111, 152–156 (1985)
Kannathal, N., Acharya, R.U., Alias, F., Tibelong, T., Sadasivan, P.K.: Nonlinear Analysis of EEG Signals at Different Mmental States. Biomedical Online Journal 16, 3–7 (2004)
Soe, N.N., Nakagawa, M.: Chaos and Fractal Analysis of Electroencephalogram Signals during Different Imaginary Motor Movement Tasks. Journal of the Physical Society of Japan 77, 1–8 (2008)
Jing, H., Takigawa, M., Benasich, A.A.: Relationship of Nonlinear Analysis, MRI and SPECT in the Lateralization of Temporal Lobe Epilepsy. European Neurology 48, 11–19 (2002)
Preissl, H., Aertsen, A.: Reconstruction and Characterization of Neuronal Dynamics: How Attractive is Chaos? In: Information Processing in the Cortex: Experiments and Theory, pp. 285–297. Springer, Heidelberg (1992)
Theiler, J., Rapp, P.E.: Re-examination of the Evidence for Low-dimensional Non-linear Structure in the Human Electroencephalogram EEG. Clin. Neurophysiol. 98, 213–222 (1996)
Mandell, A.J., Slez, K.A.: The Impact of Chaos on Science and Society. Hunan Science & Technology Press, Changsha (2001)
Fernandez, E., Jelinek, H.F.: Use of Fractal Theory in Neuroscience: Methods, Advantages, and Potential Problems. Methods 24, 309–321 (2001)
Heymans, O., Fissette, J., Vico, P., Blacher, S., Masset, D., Brouers, F.: Is Fractal Geometry Useful in Medicine and Biomedical Sciences? Med. Hypotheses 54, 360–366 (2000)
Bashashati, A., Ward, R.K., Birch, G.E., Hashemi, M.R., Khalilzadeh, M.A.: Fractal Dimension-Based EEG Biofeedback System. In: 25th Annual International Conference of the IEEE EMBS, pp. 17–23. IEEE Press, New York (2003)
Cabukovski, V., Rudolf, N.M., Mahmood, N.: Measuring the Fractal Dimension of EEG Signals: Selection and Adaptation of Method for Real-time Analysis. Transactions on Biomedicine and Health 1, 285–292 (1993)
Liu, J.Z., Yang, Q., Yao, B., Brown, R.W., Yue, G.H.: Linear Correlation between Fractal Dimension of EEG Signal and Handgrip Force. Biological Cybernetics 93, 131–140 (2005)
Mandelbrot, B.B.: The Fractal Geometry of Nature. W. H. Freeman, New York (1983)
Higuchi, T.: Approach to an Irregular Time Series on the Basis of Fractal Theory. Physica D 31, 277–283 (1988)
Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG Synchronization and Desynchronization: Basic Principles. Clinical Neurophysiology 110, 1842–1857 (1999)
Dewan, E.M.: Occipital Alpha Rhythm Eye Position and Lens Accommodation. Nature 214, 975–977 (1967)
Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Wiley, New York (1949)
Elul, R.: The Genesis of the EEG. International Review of Neurobiology 15, 227–272 (1972)
Dornhege, G., Blankertz, B., Curio, G., Müller, K.R.: Boosting Bit Rates in Non-invasive EEG Single-trial Classifications by Feature Combination and Multi-class Paradigms. IEEE Trans. Biomed. Eng. 51, 993–1002 (2004)
Zhang, D., Wang, Y.J., Gao, X.R., Hong, B., Gao, S.K.: An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface. Computational Intelligence and Neuroscience 2007, Article ID 39714, 9 (2007)
Chatrian, G.E., Magnus, C.P., Lazarte, J.A.: The Blocking of the Rolandic Wicket Rhythm and Some Central Changes Related to Movement. Electroencephalogr. Clin. Neurophysiol. 11, 497–510 (1959)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, X., Deng, Z., Zhang, J. (2009). Function of EEG Temporal Complexity Analysis in Neural Activities Measurement. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-01507-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
eBook Packages: Computer ScienceComputer Science (R0)