Synchronization in brain (original) (raw)

2012, Hanieh Bakhshayesh, B.Eng. Honours Thesis, Flinders University

Abstract

In this project, well-known synchronization measures (coherence, correntropy, cross correlation, phase synchrony and GFS) were compared to understand which one is the best measure for applying to EEG signals (electrical activity recorded from the human scalp). EEG is a nonstationary and noisy signal therefore a good synchronization measure should be insensitive to noise, be able to detect both linear and nonlinear relationships, and be able to detect nonstationary relationships. Time resolution and frequency resolution are other important properties of synchronization measures. Different approaches were considered to achieve the above requirements. Synchronization measures were applied to two types of simulated data: noisy sinusoidal signals with linear relationships and unidirectional coupled Hénon maps with nonlinear relationships. Different measures have both good and bad features. No measure is the clear winner but correntropy and phase synchrony shows promising results. The most appropriate synchronization measure must be chosen with knowledge of the application in hand. Additionally, the synchronization measures were applied to real EEG data. The first application was to find a synchronization measure which may be able to identify a persistent effect of disease in the brain. It is hypothesised that there are changes in the synchronization of brain signals of a patient with certain diseases, which are persistent across tasks. Each synchronization measure was evaluated for different subjects across different tasks. Phase synchrony and then coherence have the smallest dispersion from the mean (lowest standard deviation) and can be considered as the measures which are the least sensitive to changes due to the task. As phase synchrony also performed well on simulated data, it may be an appropriate measure to detect a persistent effect of disease in the brain. Thinking was the second application considered with real EEG. The main feature of thinking is its short time scale; therefore it is required to find a measure which is able to detect rapid changes in brain activities. To achieve this, all synchronization measures were applied to short blocks of EEG, sliding the block one sample at a time. There was no evidence that this analysis is able to distinguish EEG from noise. In another experiment, synchronization measures were applied to blocks of data of decreasing size, to identify measures that can detect synchronisation over very short timescales (i.e. thoughts) where synchrony is not apparent at long time scales. None of the results was in agreement with the expected outcome. As phase synchrony performed well on simulated data and robustly meets the null hypothesis of real EEG, in overall it can be concluded that it is an appropriate synchronization measure for further experiment on brain’s signal .but still it should be noted, application and aim of research are important aspects that needs to be consider for choosing the preferable synchronization measure.

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References (38)

  1. Arfken, G. B. and H. J. Weber 1995, "Mathematical methods for physicists", Academic Press.
  2. Abdi. H., & Williams, L.J. 2010 "Principal component analysis.". Wiley Interdisciplinary Reviews: Computational Statistics, 2: 433-459.
  3. A. Delorme, T.Sejnowski, and S. Makeig, 2007, "Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis" , Neuroimage, Issue 4 vol. 34, pp. 1443- 1449, Feb.
  4. Aronszajn, N. 1950. "Theory of reproducing kernels. Transactions of the American Mathematical Society, 68(3), 337-404.
  5. Biocybernaut Institute, EEG-ElectroEncephaloGraph, accessed 2012, < http://biocybernaut.com/tutorial/eeg.html>
  6. Brain Research Group , accessed < http://brain.bio.msu.ru/papers/chp2000/4.htm> Buzsaki, G. 2006. Rhythms of the Brain. Oxford: Oxford University Press Cahn, B.R.; Polich, J. (2006). "Meditation states and traits: EEG, ERP, and neuroimaging studies". Psychological Bulletin 132 (2): 180-211
  7. Christoph S. Herrmann, Maren Grigutsch2 & Niko A. Busch, EEG oscillations and wavelet analysis.
  8. Ernest Niedermeyer , 2005, electroencephalography basic principle, clinical application and related fields, Fernando lopes Da silva page 1240
  9. Electroencephalogram ,accessed 2012 http://dragon.inha.ac.kr/\~neurolee/eeg/eeg\_eng.htm Ehman,1971; lehman& Koenig,1997
  10. Gunduz A. and J.C. Principe J.C., 2009," Correntropy as a novel measure for nonlinearity tests". Signal Processing 89(1): 14-23.]
  11. Frederick W. King (2009). Hilbert Transforms. Cambridge: Cambridge University Press.
  12. Herrmann C. S., Grigutsch M., and Busch N. A., 2005, EEG Oscillations and Wavelet Analysis. In: Handy, T. (ed.), Event-Related Potentials: a Methods Handbook, Cambridge, MIT Press, pp. 229-259
  13. Herrmann, Maren Grigutsch & Niko A. Busch, EEG oscillations and wavelet analysis Christoph S. I.Goncharova, D. J. McFarland, T. M. Vaughan, and J. R. Wolpaw, 2003 , "EMG contamination of EEG: spectral and topographical characteristics," Clin. Neurophysiol., vol. 114, pp. 1580-1593.
  14. J. G. Webster, 1998, "Medical Instrumentation", 3rd ed., John-Wiley J.Dauwels , F. Vialatte cT. Musha dA. Cichocki , A Comparative Study of Synchrony Measures for the Early Diagnosis of Alzheimer's Disease Based on EEG J. Jianwu Xu 2007, Nonlinear signal processing based on reproducing kernel Hilbert space, University of Florida Junghofer etal., 1999
  15. Junghofer, M., T. Elbert, D. M. Tucker and C. Braun 1999. "The polar average reference effect: A bias in estimating the head surface integral in EEG recording", Electroencephalography and Clinical Neurophysiology 110(6): 1149-1155.
  16. Junghofer, M., T. Elbert, D. M. Tucker and C. Braun,1999, "The polar average reference effect: A bias in estimating the head surface integral in EEG recording", Electroencephalography and Clinical Neurophysiology 110(6): 1149-1155.
  17. Karim Jerbi, 2010, " Assessing functional connectivity by EEG and MEG :from methodology to interpretation" , HBM MEG/EEG Course June 6th, Barcelona .
  18. Kamen, Gary 2004. Electromyographic Kinesiology. In Robertson, DGE et al. Research Methods in Biomechanics. Champaign, IL: Human Kinetics Publ.
  19. Koenig, T., Lehmann, D., Saito, N., Kuginuki, T., Kinoshita, T., Koukkou, M., 2001, "Decreased functional connectivity of EEG theta-frequency activity in first-episode", neuroleptic-naı¨ve patients with schizophrenia: preliminary.
  20. Lachaux J.-P., Rodriguez E., Martinerie J., and Varela F. J., 1999, "Measuring phase synchrony in brain signals", Human Brain Mapping 8, 194-208.
  21. Lyapunov Exponent, content, exorcize, accessed 30 April 2012 , < http://www.agnld.uni- potsdam.de/~marwan/matlab-tutorials/html/lyapunov.html> LifeHugger. Accessed 2012 < ECG-simplified. Aswini Kumar M.D>
  22. Matthews, Michael R. (2000), < Time for science education: how teaching the history and philosophy of pendulum motion can contribute to science literacy> M. Palus, V. Komarek, Z. Hrncir, and K. Sterbova, 2001, Phys. Rev. E 63, 046211.
  23. Marco Thiel, Jürgen Kurths, and Celso Grebogi ,M. Carmen Romano,"Estimation of the direction of the coupling by conditional probabilities of recurrence", 2007, published 21 September Masumi ishkawa kenji doya ,hiroyuki miyamoto and takeshi yamakawa Numeral information processing page 114
  24. Niedermeyer E. and da Silva F.L. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincot Williams & Wilkins Niedermeyer, E (1997). "Alpha rhythms as physiological and abnormal phenomena". Int J Psychophysiol 26 (1-3): 31-49
  25. Nunez PL and Srinivasan R. 2006, "Electric Fields of the Brain", The Neurophysics of EEG (2 nd Ed), New York: Oxford University Press.
  26. Oppenheim, A. V., and R. W. Schafer, 1999, "Discrete-Time Signal Processing, Upper SaddleRiver, NJ: Prentice-Hall".
  27. Pereda, E., R. Quian Quiroga, and J. Bhattacharya, 2005 , "Nonlinear Multivariate Analysis of Neurophysiological Signals," Prog. Neurobiol., Vol. 77, No. 1-2, , pp. 1-37.
  28. Perception's shadow: long-distance synchronization of human brain activity", Nature, 397, 430-433
  29. Pes da Silva, F. , E. Niedermeyer and F. Lopes da Silva, (eds.), 1993, Baltimore, MD: Lippincott ,Williams & Wilkins, Theory and Practice in Electroencephalography" BasicPrinciples, Clinical Applications and Related Fields, EEG Analysis
  30. Pfurtscheller G, Lopes da Silva FH 1999. "Event-related EEG/MEG synchronization and desynchronization: basic principles". Clin Neurophysiol 110 (11): 1842-185 PHYSICAL REVIEW E 76, 2007
  31. Prof. Paul L. Nunez , A Brief History of the EEG Surface Laplacian Background and History.
  32. Quian Quiroga, R. et al. 2002, "Performance of Different Synchronization Measures in Real Data: A Case Study on Electroencephalographic Signals," Phys. Rev. E, Vol. 65, No. 4, Quian Quiroga R. ,J. Arnhold, and P. Grassberger, 2000, "Learning Driver-Response Relationships from Synchronization Patterns," Phys. Rev. E, Vol. 61, No. 5, Pt. A, , pp. 5142-5148 .
  33. Revonsuo, A. and Newman, J. 1999 . Binding and Consciousness. Consciousness and Cognition 8, 123- 127
  34. Rieke, F., et al., 2001 Spikes: Exploring the Neural Code, Cambridge, MA: MIT Press,.
  35. Rodriguez, E., George, N., Lachaux, J.-P., Martinerie, J., Renault, B., & Varela, F.J. ,1999, " R. QuianQuiroga, J. Arnhold, and P. Grassberger, , Phys. Rev. E 61, 5142 .
  36. Samuel Johnson,2007, Neural networks in neuroscience: a brief overview S. Baillet, J. C. Mosher, R. M. Leahy, 2001 , "Electromagnetic Brain Mapping", IEEE Signal Processing, vol. 18, no. 6, pp. 14-30, November .
  37. Shawe-Taylor J. and Cristianini N., 2004, "Kernel Methods for Pattern Analysis", Cambridge University Press.
  38. SP Fitzgibbon, TW Lewis, DMW Powers, EW Whitham, JO Willoughby, KJ Pope, accepted IEEE Transactions on Biomedical Engineering, 2012, Surface Laplacian of Central Scalp Electrical signals is Insensitive to Muscle Contamination