Advertisement

Neural Signal Processing

  • Donna L. Hudson
  • Maurice E. Cohen
Part of the Bioelectric Engineering book series (BEEG)

Abstract

The major thrust of this chapter is on neural signal processing in the central nervous system (CNS). In order to establish the framework for this discussion, it is instructive to look at the biological foundations, from single neurons to the peripheral nervous systems, because these are important building blocks and provide input and output signals for the complex neuronal structure of the CNS. Section 6.2 gives an overview of biological structures and historical discoveries. Section 6.3 examines signal processing in the single neuron and how it contributes to the complex network of signals. Examination of the function of the central and peripheral nervous systems depends to a large extent on time series analysis. Basic techniques are summarized in Section 6.4. Section 6.5 describes the peripheral nervous system as input and output media for the CNS. Section 6.6 describes methods for analyzing signals emanating from the CNS. Section 6.7 discusses the use of signal analysis in the diagnosis and treatment of neurological disease. Because of the complexity of signal analysis, its use in diagnosis of disease is dependent on higher-order decision models, which are described in Section 6.8. Finally, Section 6.9 describes current frontiers of signal analysis and prospects for the future.

Keywords

Deep Brain Stimulation Neural Signal Central Tendency Measure Giant Squid Functional Neuromuscular Stimulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aakerlund, L., and Hemmingsen, R., 1998, Neural networks as models of psychopathology, Biol. Psychiatry 43(7):471–482.CrossRefGoogle Scholar
  2. Akay, M., 1995, Wavelets in biomedical engineering, Ann. Biomed. Eng. 23:531–542.CrossRefGoogle Scholar
  3. Akay, M., 2001, Merging engineering and neuroscience, Proc. IEEE 89(7):991–992.CrossRefGoogle Scholar
  4. Akay, M., and Daubenspeck, J. A., 1999, Spatial mapping of respiratory related evoked responses using wavelet transform method, EMBS/BMES Joint Proc. 21:962.Google Scholar
  5. Asanuma, H., and Wilson, V. J. (eds.), 1979, Integration in the Nervous System: A Symposium in Honor of David P. C. Lloyd and Rafael Lorente de No, The Rokefeller University, Tokyo, New York.Google Scholar
  6. Bendat, J. S., and Piersol, A. G., 1971, Random Data: Analysis and Measurement Procedures, Wiley-Interscience, New York.zbMATHGoogle Scholar
  7. Berube, J. L., Parker, P. A., Gander, R. E., and Dunfield, V. A., 1984, Digital myoelectric signal processor with adaptive decision boundaries, Med. Biol. Eng. Comput. 22(4):349–352.CrossRefGoogle Scholar
  8. Blanco, S., Kochen, S., Rosso, O. A., and Salgado, P., 1997, Applying time-frequency analysis to seizure EEG activity, IEEE EMBS Mag. 16(1):64–71.CrossRefGoogle Scholar
  9. Butter, C. M., 1968, Neuropsychology: The Study of Brain and Behavior, Brooks/Cole Publishing Co., Belmont, CA.Google Scholar
  10. Chialvo, R., and Jalife, J., 1987, Non-linear dynamics of cardiac excitation and impulse propagation, Nature 330:749–752.CrossRefGoogle Scholar
  11. Cohen, M. E., and Hudson, D. L., 1999, Chaos and time series analysis, in Encyclopedia of Electrical & Electronics Engineering (J. G. Webster, ed.), John Wiley & Sons, New York, pp. 218–226.Google Scholar
  12. Cohen, M. E., and Hudson, D. L., 2000a, New chaotic methods for biomedical signal analysis, IEEE EMBS Inform. Technol. Applicat. Biomed. 2000:117–122.Google Scholar
  13. Cohen, M. E., and Hudson, D. L., 2000b, Extension of chaotic techniques to electroencephalogram analysis, ISCA Comput. Applicat. Ind. Eng. 13:82–85.Google Scholar
  14. Cohen, M. E., and Hudson, D. L., 2001, EEG analysis based on chaotic evaluation of variability, IEEE Eng. Med. Biol. 23.Google Scholar
  15. Cohen, M. E., and Hudson, D. L., 2003a, Knowledge-based and data-based analysis of biomedical signals, ISCA Comput. Their Applicat. 18:67–70.Google Scholar
  16. Cohen, M. E., and Hudson, D. L., 2003b, Nonlinear analysis using continuous chaotic modeling, Biomint Seminar, World Academy of Biotechnology, UNESCO.Google Scholar
  17. Cohen, M. E., Hudson, D. L., and Anderson, M. F., 1992, The effect of vasoactive drugs on the chaotic nature of blood flow, MEDINFO 92:931–936.Google Scholar
  18. Cohen, M. E., Hudson, D. L., Anderson, M. F., and Deedwania, P. C., 1994, A conjecture to the solution of the continuous logistic equation, Int. J. Uncert. Fuzz. Knowl.-Based Syst. 2(4):445–461.CrossRefGoogle Scholar
  19. Cohen, M. E., Hudson, D. L., and Deedwania, P. C., 1998, The use of continuous chaotic modeling in differentiation of categories of heart disease, Inform. Process. Manage. Uncert. Knowl.-Based Syst. 7:548–554.Google Scholar
  20. Daubechies, I., 1988, Orthonormal bases of compactly supported wavelets, Commun. Pure Appl. Math. XLI:909–996.CrossRefMathSciNetGoogle Scholar
  21. DeFelipe, J., and Jones, E. G., eds., 1988, Cajal on the Cerebral Cortex: An Annotated Translation of the Complete Writings, Oxford University Press, New York.Google Scholar
  22. De Tomasso, M., De Carlo, F., Difruscolo, O., Massagra, R., Sciruicchio, V., and Bellotti, R., 2003, Detection of subclinical brain electrical activity changes in Huntington’s disease using artificial neural networks, Clin. Neurophysiol. 114:1237–1245.CrossRefGoogle Scholar
  23. Eberhart, R. C., 1989, Chaos theory for the biomedical engineer, IEEE EMB Mag. Sept., 41–45.Google Scholar
  24. Edlinger, G., Wach, P., and Pfurtscheller, G., 1998, On the realization of an analytic high-resolution EEG, IEEE Trans. Biomed. Eng. 45:736–745.CrossRefGoogle Scholar
  25. Evans, H. B., Pan, Z., Philip, A. P., and Scott, R. N., 1984, Signal processing for proportional myoelectric control, IEEE Trans. Biomed. Eng. 31:207–211.CrossRefGoogle Scholar
  26. Flint, R., Pederson, B., Guekht, A. B., Malmgren, K., Michelucci, R., Neville, B., Pinto, F., Stephani, U., and Ozkara, C., 2002, Guidelines for the use of EEG methodology in the diagnosis of epilepspy, Acta Neurol. Scand. 106:1–7.CrossRefGoogle Scholar
  27. Freeman, W. J., 1987, Simulation of chaotic EEG patterns with a dynamic model of the olfactory system, Biol. Cybernet. 56:139–150.CrossRefGoogle Scholar
  28. Frenger, P., 2002, Nanocontroller update: Building a better artificial neuron, Biomed. Sci. Instrum. 38:441–445.Google Scholar
  29. Garrett, D., Peterson, D. A., Anderson, C. W., and Thaut, M. H., 2003, Comparison of linear, nonlinear and feature selection methods for EEG signal classification, IEEE Trans. Neural Syst. Rehabil. Eng. 11:141–144.CrossRefGoogle Scholar
  30. Goldberger, A. L., 1989, Cardiac chaos, Science 243(2987):1419.CrossRefGoogle Scholar
  31. Golgi, C., 1886, Sulla fina anatomia degli organi centrali del sistema nervoso, Hoepli, Milano.Google Scholar
  32. Harley, T. A., 1998, Connectionist modeling of the recovery of language functions following brain damage, Brain Lang. 52(1):7–24.CrossRefMathSciNetGoogle Scholar
  33. He, B., Lian, J., and Li, G., 2001, High-resolution EEG: A new realistic geometry spline Lapacian estimation technique, Clin. Neurophysiol. 112:845–852.CrossRefGoogle Scholar
  34. He, B., Zhang, Z., Lian, J., Sasaki, H., Wu, S., and Towle, V. L., 2002, Boundary element method based on cortical potential imaging of somatosensory evoked potentials using subjects; magnetic resonance imaging, Neuroimage 16:564–576.CrossRefGoogle Scholar
  35. Hefftner, G., Zucchini, W., and Jaros, G. G., 1988, The electromyogram (EMG) as a control signal for functional neuromuscular stimulation—Part I: Autoregressive modeling as a means of EMG signature discrimination, IEEE Trans. Biomed. Eng. 35(4):230–237.CrossRefGoogle Scholar
  36. Hjorth, B., 1975, An on line transformation of EEG scalp potentials into orthogonal source derivations, Electroencephalogr. Clin. Neurophysiol. 39:526–530.CrossRefGoogle Scholar
  37. Hodgkin, A. L., 1964, The Conduction of the Nervous Impulse, Liverpool University Press.Google Scholar
  38. Hofmann, O., and Bodendorf, F., 2000, A framework for agent mediated electronic business, ISCA Comput. Applicat. Med. Care 15:120–123.Google Scholar
  39. Hsu, C., and Goldberg, H. S., 1999, Knowledge-mediated retrieval of laboratory observations, Proc. AMIA 1999:809–813.Google Scholar
  40. Hubel, D. H., and Wiesel, T. N., 1962, Receptive fields, binocular interaction, and functional architecture of the cat visual cortex, J. Physiol. 160(1):106–154.Google Scholar
  41. Hudson, D. L., and Cohen, M. E., 1988, An approach to management of uncertainty in an expert system, Int. J. Intell. Syst. 3(1):45–58.zbMATHCrossRefGoogle Scholar
  42. Hudson, D. L., and Cohen, M. E., 1999, Neural Networks and Artificial Intelligence for Biomedical Engineering, IEEE Press-Wiley.Google Scholar
  43. Hudson, D. L., and Cohen, M. E., 2001, Use of intelligent agents to include signal analysis data, IEEE Eng. Med. Biol. 23. [CD].Google Scholar
  44. Hudson, D. L., and Cohen, M. E., 2002, Pattern identification in electroencephalograms, ISCA Comput. Their Applicat. 17:315–318.Google Scholar
  45. Hudson, D. L., Cohen, M. E., and Deedwania, P. C., 1998, Chaotic ECG analysis using combined models, IEEE Eng. Med. Biol. 20:1553–1556.Google Scholar
  46. Ihl, R., Dierks, T., Martin, E. M., Frolich, L., and Maurer, K., 1992, Importance of the EEG in early and differential diagnosis of dementia of the Alzheimer type, Fortschritte der Neurologie-Psychiatrie 6(12):451–459.Google Scholar
  47. Inouye, T., Toi, S., and Matsumoto, Y., 1995, A new segmentation method of electroencephalograms by use of Akaike’s information criterion, Brain Res. 3(1):33–40.Google Scholar
  48. Jellinger, K. A., Seppi, K., Wenning, G. K., and Poewe, W., 2002, Impact of coexistent Alzheimer pathology on the natural history of Parkinson’s disease, J. Neural Transm. 109(3):329–339.CrossRefGoogle Scholar
  49. Kalayci, T., and Ozdamar, O., 1995, Wavelet preprocessing for automated neural network detection of EEG spikes, IEEE EMBS Mag. 14(2):160–166.CrossRefGoogle Scholar
  50. Kearfott, R. B., Sidman, R. D., Major, D. A., and Hill, C. D., 1991, Numerical tests of a method for simulating electric potentials on the cortical surface, IEEE Trans. Biomed. Eng. 38:294–299.CrossRefGoogle Scholar
  51. Kenny, R. A., Kalaria, R., and Ballard, C., 2002, Neurocardiovascular instability in cognitive impairment and dementia, Ann. N.Y. Acad. Sci. 977:183–195.CrossRefGoogle Scholar
  52. Kim, H., Kim, S., Go, H., and Kim, D., 2001, Synergetic analysis of spatio-temporal EEG patterns: Alzheimer’s disease, Biol. Cybernet. 85:1–17.CrossRefGoogle Scholar
  53. Kim, K. H., and Kim, S. J., 2003, A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio, IEEE Trans. Biomed. Eng. 50(8):999–1011.CrossRefGoogle Scholar
  54. Kohli-Seth, R., and Oropello, J. M., 2000, The future of bedside monitoring, Crit. Care Clin. 16(4):557–578.CrossRefGoogle Scholar
  55. Lanzola, G., Gatti, L., Falasconi, S., and Stefanelli, M., 1999, A framework for building cooperative software agents in medical applications, Artif. Intell. Med. 16:223–249.CrossRefGoogle Scholar
  56. Law, S. K., Nunez, P. L., and Wijesinghe, R. S., 1993, High-resolution EEG using spline generated surface laplacians on spherical and ellipsoidal surfaces, IEEE Trans. Biomed. Eng. 40:145–153.CrossRefGoogle Scholar
  57. Leuchter, A. F., Cook, I. A., Newton, T. F., and Weiner, H., 1993, Regional differences in brain electrical activity in dementia: Use of spectral power and spectral ratio measures, Electroencephalogr. Clin. Neurophysiol. 87(6):385–393.CrossRefGoogle Scholar
  58. Lopes da Silva, F., 1993, Dynamics of EEGs as signals of neuronal populations: models and theoretical considerations, in: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 3rd edn. (E. Niedermeyer and F. Lopes da Silva, eds.), Williams and Williams, Baltimore, pp. 63–77.Google Scholar
  59. MacGregor, R. J., 1987, Neural and Brain Modeling, Academic Press, San Diego.zbMATHGoogle Scholar
  60. Mack, S. J., Holstein, J., Kleber, K., and Grönemeyer, D. H., 2000, New aspects of image distribution and workflow in radiology, J. Digit. Imag. 13(2):17–21.CrossRefGoogle Scholar
  61. Miyauchi, T., Hagimoto, H., Ishii, M., Endo, S., Tanaka, K., Kajiwara, S., Endo, K., Kajiwara, A., and Kosaka, K., 1994, Quantitative EEG in patients with presenile and senile dementia of the Alzheimer type, Acta Neurol. Scand. 89(1):56–64.Google Scholar
  62. Nunez, P. L., Silberstein, R. B., Cadusch, P. J., Wijesinghe, R. S., Westdorp, A. F., and Srinivasan, R., 1994, A theoretical and experimental study of high resolution EEG based on surface laplacians and cortical imaging, Electroencephalogr. Clin. Neurophysiol. 90:40–57.CrossRefGoogle Scholar
  63. Petrosian, A. A., Prokhorov, D. V., Lahara-Nanson, W., and Schiffer, R. B., 2001, Recurrent neural network-based approach for early recognition of Alzheimer’s disease in EEG, Clin. Neurophysiol. 112(8):1378–1387.CrossRefGoogle Scholar
  64. Philipson, G., 1985, Adaptable myoelectric prosthetic control with functional visual feedback using microprocessor techniques, Med. Biol. Eng. Comput. 23:8–14.CrossRefGoogle Scholar
  65. Porcher, R., and Thomas, G., 2001, Estimating Lyapunov exponents in biomedical time series, Phys. Rev. E: Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 64(1–1):010902.Google Scholar
  66. Prichep, L. S., John, E. R., Ferris, S. H., Reisberg, B., Almas, M., Alper, K., and Cancro, R., 1994, Quantitative EEG correlates of cognitive deterioration in the elderly, Neurobiol. Aging 15(1):85–90.CrossRefGoogle Scholar
  67. Principe, J., Gala, S. K., and Chang, T. G., 1989, Sleep staging automaton based on the theory of evidence, IEEE Trans. Biomed. Eng. 36(5):503–509.CrossRefGoogle Scholar
  68. Priori, A., Foffani, G., Pesent, A., Bianchi, A., Chiesa, V., Baselli, G., Caputo, E, Tamma, F., Rampini, P., Egidi, M., Locatelli, M, Barbieri, S., and Scarlato, G., 2002, Movement-related modulation of neural activity in human basal ganglia and its L-DOPA dependency: recordings from deep brain stimulation in patients with Parkinson’s disease, Neurol. Sci. 23(Suppl. 2):S101–102.CrossRefGoogle Scholar
  69. Pritchard, W. S., Duke, D. W., Coburn, K. L., Moore, N. C., Tucker, K. A., Jann, W. S., and Hostetler, R. M., 1994, EEG-based, neural-net predictive classification of Alzheimer’s disease versus control subjects is augmented by non-linear EEG measures, Electroencephalor. Clin. Neurophysiol. 91(2):118–130.CrossRefGoogle Scholar
  70. Rialle, V., and Stip, E., 1994, Cognitive models in psychiatry: from symbolic models to parallel and distributed models, J. Psychiatry Neurosci. 19(3):178–192.Google Scholar
  71. Rogers, S. K., and Kabrisky, M., 1991, An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, SPIE Optical Engineering Press.Google Scholar
  72. Rosenblatt, F., 1962, Principles of Neurodynamics, Spartan Books, New York.zbMATHGoogle Scholar
  73. Ruppin, E., Reggia, J. A., and Horn, D., 1996, Pathogensis of schizophrenic delusions and hallucinations: A neural network model, Schizophren. Bull. 22(1):105–123.Google Scholar
  74. Santa Cruz, K. S., Tasaki, C. S., Kim, R. C., and Cotman, C. W., 2002, Brainstem and cortical Lewy bodies in patients presenting with Alzheimer’s disease, J. Alzheim. Dis. 4(1):11–17.Google Scholar
  75. Sarbadhikari, S. N., and Chakrabarty, K., 2001, Chaos in the brain: A short review alluding to epilepsy, depression, exercise and lateralization, Med. Eng. Phys. 23(7):445–455.CrossRefGoogle Scholar
  76. Saugstad, L. F., 1994, Deviation in cerebral excitability: Possible clinical implications, Int. J. Psychophysiol. 18(3):205–212.CrossRefGoogle Scholar
  77. Schreiter-Gasser, U., Gasser, T., and Ziegler, P., 1993, Quantitative EEG analysis in early onset Alzheimer’s disease: A controlled study, Electroencephalogr. Clin. Neurophysiol. 86(1):15–22.CrossRefGoogle Scholar
  78. Schreiter-Gasser, U., Gasser, T., and Ziegler, P., 1994, Quantitative EEG analysis in early onset Alzheimer’s disease: Correlations with severity, clinical characteristics, visual EEG and CCT, Electroencephalogr. Clin. Neurophysiol. 90(4):267–272.CrossRefGoogle Scholar
  79. Seiss, E., Praamstra, C., Hesse, C. W., and Rickards, H., 2003, Proprioceptive sensory function in Parkinson’s disease and Huntington’s disease: Evidence from proprioception-related EEG potentials, Exp. Brain Res. 148:308–319.Google Scholar
  80. Signorino, M., Pucci, E., Belardinelli, N., Nolfe, G., and Angeleri, F., 1995, EEG spectral analysis in vascular and Alzheimer dementia, Electroencephalogr. Clin. Neurophysiol. 94(5):313–325.CrossRefGoogle Scholar
  81. Silverman, B. G., 1998, The role of Web agents in medical knowledge management, MD Comput. 15(4):221–231.Google Scholar
  82. Soininen, H., and Riekkinen, P. J., Sr., 1992, EEG in diagnostics and follow-up of Alzheimer’s disease, Acta Neurol. Scand. 139(Suppl.):36–39.Google Scholar
  83. Storey, E., Slavin, M. J., and Kinsella, G. J., 2002, Patterns of cognitive impairment in Alzheimer’s disease: Assessment and differential diagnosis, Front. Biosci. 7:E155–E184.CrossRefGoogle Scholar
  84. Suzuki, M., Desmond, T. J., Albin, R. L., and Frey, K. A., 2002, Striatal monoamnergic terminals in Lewy body and Alzheimer’s dementias, Ann. Neurol. 51(6):767–771.CrossRefGoogle Scholar
  85. Swigger, K. M., and Ducksworth, L., 2000, Supporting computer-mediated collaboration through user-defined agents, ISCA CAINE 13:43–46.Google Scholar
  86. Tiraboschi, P., Hansen, L. A., Alford, M., et al., 2002, Early and widespread cholinergic losses differentiate dementia with Lewy bodies form Alzheimer disease, Arch. Gen. Psychiatry 59(10):946–951.CrossRefGoogle Scholar
  87. Varma, A. R., Laitt, R., Lloyd, J. J., Carson, K. J., Snowden, J. S., Neary, D., and Jackson, A., 2002, Diagnostic value of high signal abnormalities on T2 weighted MRI in the differentiation of Alzheimer’s, frontotemporal and vascular dementias, Acta Neurol. Scand. 105(5):355–364.CrossRefGoogle Scholar
  88. Visser, P. J., Verhey, F. R., Ponds, R. W., and Jolles, J., 2001, Diagnosis of preclinical Alzheimer’s disease in a clinical setting, Int. Psychogeriatr. 13(4):411–423.CrossRefGoogle Scholar
  89. Woyshville, M. J., and Calabrese, J. R., 1994, Quantification of occipital EEG changes in Alzheimer’s disease utilizing a new metric: the fractal dimension, Biol. Psychiatry 35(6):381–387.CrossRefGoogle Scholar
  90. Wszolek, Z. K., Herkes, G. K., Lagerlund, T. D., and Kokmen, E., 1992, Comparison of EEG back-ground frequency analysis, psychological test scores, short test of mental status, and quantitative SPECT in dementia, J. Geriatr. Psychiatry Neurol. 5(1):22–30.Google Scholar

Copyright information

© Kluwer Academic/Plenum Publishers 2005

Authors and Affiliations

  • Donna L. Hudson
    • 1
  • Maurice E. Cohen
    • 1
    • 2
  1. 1.University of California, San FranciscoFresno
  2. 2.California State UniversityFresno

Personalised recommendations