Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31340
Chaotic Properties of Hemodynamic Responsein Functional Near Infrared Spectroscopic Measurement of Brain Activity

Authors: Ni Ni Soe , Masahiro Nakagawa

Abstract:

Functional near infrared spectroscopy (fNIRS) is a practical non-invasive optical technique to detect characteristic of hemoglobin density dynamics response during functional activation of the cerebral cortex. In this paper, fNIRS measurements were made in the area of motor cortex from C4 position according to international 10-20 system. Three subjects, aged 23 - 30 years, were participated in the experiment. The aim of this paper was to evaluate the effects of different motor activation tasks of the hemoglobin density dynamics of fNIRS signal. The chaotic concept based on deterministic dynamics is an important feature in biological signal analysis. This paper employs the chaotic properties which is a novel method of nonlinear analysis, to analyze and to quantify the chaotic property in the time series of the hemoglobin dynamics of the various motor imagery tasks of fNIRS signal. Usually, hemoglobin density in the human brain cortex is found to change slowly in time. An inevitable noise caused by various factors is to be included in a signal. So, principle component analysis method (PCA) is utilized to remove high frequency component. The phase pace is reconstructed and evaluated the Lyapunov spectrum, and Lyapunov dimensions. From the experimental results, it can be conclude that the signals measured by fNIRS are chaotic.

Keywords: Chaos, hemoglobin, Lyapunov spectrum, motorimagery, near infrared spectroscopy (NIRS), principal componentanalysis (PCA).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084416

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1377

References:


[1] J. Decety, "The neurophysiological basis of motor imagery", Behavioral Brain Research, vol. 77(1-2), pp. 45-52, 1996.
[2] G. Pfurtscheller, C. Neuper, and N. Birbaumer," Human brain-computer interface". In E. Vaadia and A. Riehle (Eds.), Motor cortex in voluntary movements: A distributed system for distributed functions. Series: Methods and new frontiers in neuroscience, pp. 367-401, 2005. Boca Raton, FL: CRC Press.
[3] M. Nakagawa,"Chaos and fractals in engineering", World Scientific, Singapore, pp. 113, 1999.
[4] M. Phothisonothai and M. Nakagawa, "EEG-Based Fractal Analysis of Different Motor Imagery Tasks using Critical Exponent Method", International Journal of Biomedical Science, vol. 1, no.3, pp 1306-1216, 2006.
[5] Ni Ni Soe and M. Nakagawa, "Chaos and fractal analysis of EEG signals during different imaginary motor movement tasks", J. Phys. Soc. Jpn., (submitted for publication).
[6] G. Hori, K. Aihara, Y. Mizuno and Y. Okuma, "Blind source separation and chaotic analysis of EEG for judgment of brain death", Artif. Life Robotics., vol. 5, no. 1, pp. 10-14, 2001.
[7] V. Vuksanović and V. Gal, "Nonlinear and chaos characteristics of heart period time series: healthy aging and postural change", Autonomic Neuroscience: Basic and Clinical, vol. 121, pp. 94 - 100, 2005.
[8] H. Koga and M. Nakagawa," A Chaotic Synthesis Model of Vowels ", J. Phys. Soc. Jpn., vol. 72, no.3, pp. 751-761, 2003.
[9] T. J. Huppert, R. D. Hoge, S. G. Diamond, M. A. Franceschini, and D. A. Boas," A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult human", NeuroImage, vol. 29, pp. 368-382, 2006.
[10] V. Y. Toronov, X. Zhang and A. G. Webb," A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in human primary visual cortex", NeuroImage, vol. 34, pp. 1136-1148, 2007.
[11] Barbara Stuart, "Infrared Spectroscopy: Fundamental and applications", John Wiley and sons, 2004.
[12] C. B. Akg├╝l, B. Sankur and A. Akin, "Extraction of cognitive activity-related waveforms from functional near-infrared spectroscopy signals", Med. Bio. Eng. Comput., vol. 44, pp. 945-958, 2006
[13] R. N. Aslin, and J. Mehler," Near-infrared spectroscopy for functional studies of brain activity in human infants: promise, prospects, and challenges", Journal of Biomedical Optics, vol.10, no.1, pp. 011009, 2005.
[14] C. B. Akg├╝l, B. Sankur and A. Akin," Spectral analysis of event-related hemodynamic responses in functional near infrared spectroscopy", Journal of Computational Neuroscience, vol. 18, pp. 67-83, 2005.
[15] M. Okamoto and I. Dan, "Functional near-infrared spectroscopy for human brain mapping of tase-related cognitive functions", Journal of Bioscience and Bioengineering, vol. 103, no.3, pp. 207-215, 2007.
[16] R. Sitaram, et al., "Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain computer interface", NeuroImage, vol. 34, pp. 1416-1427, 2007.
[17] S. Coyle, T. Ward, and C. Markham," Brain-computer interface using a simplified functional near-infrared spectroscopy system", Journal of Neural Engineering, vol. 4, pp. 219-226, 2007.
[18] S. Coyle, T. Ward, C. Markham and G. McDarby," On the suitability of near-infrared (NIR) systems for next-generation brain computer interface", Physiological Measurement, vol. 25, pp. 815-822, 2004.
[19] D. Friedman, R. Leeb, A. Antley, M. Garau, C. Guger, C. Keinrath, A. Steed, G. Pfurtscheller, and M. Slater," Navigating virtual reality by thought: What is it like?", Presence: Teleoperators and Virtual Environments, vol.16 (1), pp.100-110, 2007.
[20] A. H. Nayfeh and B. Balachandran, "Applied nonlinear dynamics: analytical, computational, and experimental methods", New York, John Wiley and Sons, 1995.
[21] H.G. Schuster, "Deterministic chaos: an introduction", Weinheim, Wiley-VCH, 1989.
[22] E. Ott, "Chaos in dynamical systems", Cambridge, Cambridge University Press, 2002.
[23] S. H. Strogatz, "Nonlinear dynamics and chaos", Boston (MA), Addison-Wesley, 1994.
[24] D. T. Kaplan and L. Class,"Understanding nonlinear dynamics", New York, Springer, 1995.
[25] J. C. Sprott, "Chaos and Time-Series Analysis", Oxford University, New York, 2003.
[26] R. C. Hilborn, "Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers", Oxford University, New York, 2nd ed., pp. 323, 2000.
[27] E. Ott, T. Sauer and J. Yorke, "Coping with chaos", John Wiley and Sons, Inc. New York, 1994.
[28] "Handbook of chaos control",ed. H. G. Schustetr, Wiley-VCH, Weinheim, 1999.
[29] F. Takens. "Detecting strange attractors in turbulence", Lecture Notes in Mathematics of Dynamical Systems of Turbulence (Springer-Verlag, Berlin, 1981), Vol. 898, pp. 365-381.
[30] A. M. Fraser and H. L. Swinney," Independent coordinates for strange attractors from mutual information", Phys. Rev. A, vol. 33, pp. 1134-1140, 1986.
[31] M. B. Kennel, R. Brown and H. D. I. Abarbanel," Determining embedding dimension for phase-space reconstruction using a geometrical construction", Phys. Rev. A, vol. 45, pp. 3403-3411, 1992.
[32] M. T. Rosenstein, J. J. Collins and C. J. De Luca, "A practical method for calculating largest Lyapunov exponents from small data sets", Physica D, vol. 65, pp. 117-134, 1993.
[33] I. Shimada and T. Nagashima, "A numerical approach to ergodic problem of dissipative dynamical systems", Prog. Theor. Phys., Vol. 611, no.6, pp. 1605-1616, 1979.
[in Japanese].
[34] A.Wolf, J.B. Swift, H.L. Swinney, and J.A. Vastano, "Determining Lyapunov exponents from a time series" Physica D, vol. 16, pp. 285-317, 1985.
[35] M.Sano and Y. Sawada, "Measurement of the Lyapunov spectrum from a chaotic time series", Phys. Rev. Lett., vol. 55, no.10, pp. 1082-1085, 1985.
[36] S. Sato, M. Sano and Y. Sawada, "Practical methods of measuring the generalized dimension and the largest Lyapunov exponent in high dimensional chaotic systems", Prog. Theor. Phys., vol. 77, no.1, pp.1-5, 1987.
[37] P. Bryant, R. Brown, and H. D. I. Abarbanel, "Lyapunov exponents from observed time series", Phys. Rev. A, vol. 65, pp.1523-1526, 1990.
[38] R. Brown, P. Bryant, and H. D. I. Abarbanel," Computing the Lyapunov spectrum of a dynamical system from observed time series", Phys. Rev. A, vol. 43, pp. 2787-2806, 1991.
[39] P. Frederickson, J. Kaplan, E. Yorke and J. Yorke, "The Lyapunov dimension of Strange Attractors", J. Diff. Eqs., vol. 49, pp 185-207 ,1983.
[40] P. Gruber, et.at., "Denoising using local ICA and a generalized eigendecomposition with time-delayed signals", In LNCS 3195, Proc. ICA- 2004, pp 993-1000, Granada, 2004.