Skip to main content

EEG Signal Processing: Theory and Applications

  • Chapter
  • First Online:
Neural Engineering

Abstract

The electroencephalogram or EEG is introduced in this chapter. Properties of the EEG time series are discussed as well. These include individual frequency band descriptions, and their critical functional properties are discussed. A variety of measurement tools are introduced to assist in the frequency-based intensity measure. These include the traditional strategies of power spectrum and time-domain analysis for continuous EEG signals, and other strategies for capturing the power frequency information about the sporadic events through the Teager energy operator (TEO). For the analysis of wave features, we also consider additional time-frequency methodologies, particularly wavelets. Lastly, apparent randomness of the EEG signals lends itself to entropy or information-theoretic analysis. We discuss an entropy-based model known as information quantity or IQ which is shown to reflect the changes in EEG from healthy, to injury, to recovering states. As a case study, we examine the use of EEG signal processing methods as a diagnostic tool in the recovery of the brain after cardiac arrest which causes global ischemic brain injury. The corresponding experiments demonstrate the importance of spectral methods to analyze the EEG frequency and amplitude variability assessed through the IQ measure and TEO as a tool to detect the burst suppression events in the experimental models of cardiac arrest. Our review of the EEG methods and the principled discoveries coming out of our experiments provide a general introduction to the basic properties of the EEG data interpretation and clinical translation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. L. Maillard, G. Ramantani, New recommendations of the IFCN: From scalp EEG to electrical brain imaging. Clin. Neurophysiol. 128, 2068–2069 (2017)

    Google Scholar 

  2. M. Seeck, L. Koessler, T. Bast, F. Leijten, C. Michel, C. Baumgartner, B. He, S. Beniczky, The standardized EEG electrode array of the IFCN. Clin. Neurophysiol. 128, 2070–2077 (2017)

    Google Scholar 

  3. T. Cerrahoglu Sirin, P. Bekdik Sirinocak, B.N. Arkali, T. Akinci, S.N. Yeni, Electroencephalographic features associated with intermittent rhythmic delta activity. Neurophysiol. Clin. 49, 227–234 (2019)

    Google Scholar 

  4. T. Lees, T. Chalmers, D. Burton, E. Zilberg, T. Penzel, S. Lal, S. Lal, Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers. Physiol. Meas. 39, 105012 (2018)

    Google Scholar 

  5. M. Li, H. Xu, X. Liu, S. Lu, Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol. Health Care 26, 509–519 (2018)

    Google Scholar 

  6. W. Yi, S. Qiu, K. Wang, H. Qi, F. He, P. Zhou, L. Zhang, D. Ming, EEG oscillatory patterns and classification of sequential compound limb motor imagery. J. Neuroeng. Rehabil. 13, 11 (2016)

    Google Scholar 

  7. D. Trubutschek, S. Marti, H. Ueberschar, S. Dehaene, Probing the limits of activity-silent non-conscious working memory. Proc. Natl. Acad. Sci. U. S. A. 116, 14358–14367 (2019)

    Google Scholar 

  8. D.R. Kramer, M.F. Barbaro, M. Lee, T. Peng, G. Nune, C.Y. Liu, S. Kellis, B. Lee, Electrocorticographic changes in field potentials following natural somatosensory percepts in humans. Exp. Brain Res. 237, 1155–1167 (2019)

    Google Scholar 

  9. M.E.M. Mashat, C.T. Lin, D. Zhang, Effects of task complexity on motor imagery-based brain-computer Interface. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 2178–2185 (2019)

    Google Scholar 

  10. N.R. Wilson, D. Sarma, J.D. Wander, K.E. Weaver, J.G. Ojemann, R.P.N. Rao, Cortical topography of error-related high-frequency potentials during erroneous control in a continuous control brain-computer Interface. Front. Neurosci. 13, 502 (2019)

    Google Scholar 

  11. B.J. Fisch, Fisch & Spehlmann’s EEG Primer: Basic Principles of Digital and Analog EEG (Elsevier, Amsterdam, 1999)

    Google Scholar 

  12. A.J. Rowan, E. Tolunsky, Primer of EEG (Butterworth-Heinemann, Philadelphia, 2002)

    Google Scholar 

  13. O. Bennett-Back, S. Uliel-Siboni, U. Kramer, The yield of video-EEG telemetry evaluation for non-surgical candidate children. Eur. J. Paediatr. Neurol. 20, 848–854 (2016)

    CAS  Google Scholar 

  14. D.K. Chen, S. Majmudar, A. Ram, H.C. Rutherford, M. Fadipe, C.B. Dunn, R.L. Collins, Change in illness perception is associated with short-term seizure burden outcome following video-EEG confirmation of psychogenic nonepileptic seizures. Epilepsy Behav. 83, 186–191 (2018)

    Google Scholar 

  15. H. Chen, M.Z. Koubeissi, Electroencephalography in epilepsy evaluation. Continuum (Minneap Minn) 25, 431–453 (2019)

    Google Scholar 

  16. R.S. Fisher, H.E. Scharfman, M. deCurtis, How can we identify ictal and interictal abnormal activity? Adv. Exp. Med. Biol. 813, 3–23 (2014)

    Google Scholar 

  17. C. Liu, R. Zhang, G. Zhang, T. Yu, J. Tai, W. Du, L. Li, Y. Wang, High frequency oscillations for lateralizing suspected bitemporal epilepsy. Epilepsy Res. 127, 233–240 (2016)

    Google Scholar 

  18. S. Rose, J.S. Ebersole, Advances in spike localization with EEG dipole modeling. Clin. EEG Neurosci. 40, 281–287 (2009)

    Google Scholar 

  19. E.H. Smith, C.A. Schevon, Toward a mechanistic understanding of epileptic networks. Curr. Neurol. Neurosci. Rep. 16, 97 (2016)

    Google Scholar 

  20. D. Sherman, N. Zhang, S. Garg, N.V. Thakor, M.A. Mirski, M.A. White, M.J. Hinich, Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy. Int. J. Neural Syst. 21, 115–126 (2011)

    Google Scholar 

  21. A.D. Bhimani, A.N. Selner, D.R. Esfahani, R.G. Chiu, C.L. Rosinski, D. Rosenberg, A. Mudreac, R.J. Diamond, Z. Almadidy, A.I. Mehta, Intracranial electrode placement for seizures before temporal lobectomy: A risk-benefit analysis. World Neurosurg. 121, e215–e222 (2019)

    Google Scholar 

  22. P. Sharma, M. Scherg, L.H. Pinborg, M. Fabricius, G. Rubboli, B. Pedersen, A.M. Leffers, P. Uldall, B. Jespersen, J. Brennum, O.M. Henriksen, S. Beniczky, Ictal and interictal electric source imaging in pre-surgical evaluation: A prospective study. Eur. J. Neurol. 25, 1154–1160 (2018)

    CAS  Google Scholar 

  23. P. Nemtsas, G. Birot, F. Pittau, C.M. Michel, K. Schaller, S. Vulliemoz, V.K. Kimiskidis, M. Seeck, Source localization of ictal epileptic activity based on high-density scalp EEG data. Epilepsia 58, 1027–1036 (2017)

    Google Scholar 

  24. J. Zhang, W. Liu, H. Chen, H. Xia, Z. Zhou, S. Mei, Q. Liu, Y. Li, Multimodal neuroimaging in presurgical evaluation of drug-resistant epilepsy. Neuroimage Clin. 4, 35–44 (2014)

    Google Scholar 

  25. L. Martinkovic, H. Hecimovic, V. Sulc, R. Marecek, P. Marusic, Modern techniques of epileptic focus localization. Int. Rev. Neurobiol. 114, 245–278 (2014)

    Google Scholar 

  26. A. Aarabi, B. He, Seizure prediction in patients with focal hippocampal epilepsy. Clin. Neurophysiol. 128, 1299–1307 (2017)

    Google Scholar 

  27. A. Aarabi, B. He, Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clin. Neurophysiol. 125, 930–940 (2014)

    Google Scholar 

  28. B.S. Chang, D.L. Schomer, E. Niedermeyer, Normal EEG and sleep: Adults and elderly, in Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, ed. by D. L. Schomer, F. L. da SIlva, (Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia, 2011)

    Google Scholar 

  29. J.J. Riviello Jr., D.R. Nordli Jr., E. Niedermeyer, Normal EEG and sleep: Infants to adolescents, in Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, ed. by D. Schomer, F. L. da SIlva, (Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadephia, 2011)

    Google Scholar 

  30. K. Casimo, K.E. Weaver, J. Wander, J.G. Ojemann, BCI use and its relation to adaptation in cortical networks. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1697–1704 (2017)

    Google Scholar 

  31. M.J. Khan, U. Ghafoor, K.S. Hong, Early detection of hemodynamic responses using EEG: A hybrid EEG-fNIRS study. Front. Hum. Neurosci. 12, 479 (2018)

    Google Scholar 

  32. C. Guger, R. Spataro, B.Z. Allison, A. Heilinger, R. Ortner, W. Cho, V. La Bella, Complete locked-in and locked-in patients: Command following assessment and communication with vibro-tactile P300 and motor imagery brain-computer interface tools. Front. Neurosci. 11, 251 (2017)

    Google Scholar 

  33. C. Guger, R. Spataro, F. Pellas, B.Z. Allison, A. Heilinger, R. Ortner, W. Cho, R. Xu, V. La Bella, G. Edlinger, J. Annen, G. Mandala, C. Chatelle, S. Laureys, Assessing command-following and communication with vibro-tactile P300 brain-computer Interface tools in patients with unresponsive wakefulness syndrome. Front. Neurosci. 12, 423 (2018)

    Google Scholar 

  34. S. Marchesotti, M. Bassolino, A. Serino, H. Bleuler, O. Blanke, Quantifying the role of motor imagery in brain-machine interfaces. Sci. Rep. 6, 24076 (2016)

    CAS  Google Scholar 

  35. B.P. Shortal, L.B. Hickman, R.A. Mak-McCully, W. Wang, C. Brennan, H. Ung, B. Litt, V. Tarnal, E. Janke, P. Picton, S. Blain-Moraes, H.R. Maybrier, M.R. Muench, N. Lin, M.S. Avidan, G.A. Mashour, A.R. McKinstry-Wu, M.B. Kelz, B.J. Palanca, A. Proekt, C.S.G. Re, Duration of EEG suppression does not predict recovery time or degree of cognitive impairment after general anaesthesia in human volunteers. Br. J. Anaesth. 123, 206–218 (2019)

    CAS  Google Scholar 

  36. D.A. Turner, Enhanced functional outcome from traumatic brain injury with brain-machine interface neuromodulation: Neuroprosthetic scaling in relation to injury severity, in Translational Research in Traumatic Brain Injury, ed. by D. Laskowitz, G. Grant, (CRC Press, Boca Raton, 2016)

    Google Scholar 

  37. C. Wang, M.E. Costanzo, P.E. Rapp, D. Darmon, D.E. Nathan, K. Bashirelahi, D.L. Pham, M.J. Roy, D.O. Keyser, Disrupted gamma synchrony after mild traumatic brain injury and its correlation with white matter abnormality. Front. Neurol. 8, 571 (2017)

    Google Scholar 

  38. D. Akhmetshina, A. Nasretdinov, A. Zakharov, G. Valeeva, R. Khazipov, The nature of the sensory input to the neonatal rat barrel cortex. J. Neurosci. 36, 9922–9932 (2016)

    CAS  Google Scholar 

  39. S. van der Lely, M. Stefanovic, M.R. Schmidhalter, M. Pittavino, R. Furrer, M.D. Liechti, M. Schubert, T.M. Kessler, U. Mehnert, Protocol for a prospective, randomized study on neurophysiological assessment of lower urinary tract function in a healthy cohort. BMC Urol. 16, 69 (2016)

    Google Scholar 

  40. R. Arya, C. Roth, J.L. Leach, D. Middeler, J.A. Wilson, J. Vannest, L. Rozhkov, H.M. Greiner, J. Buroker, C. Scholle, H. Fujiwara, P.S. Horn, D.F. Rose, N.E. Crone, F.T. Mangano, A.W. Byars, K.D. Holland, Neuropsychological outcomes after resection of cortical sites with visual naming associated electrocorticographic high-gamma modulation. Epilepsy Res. 151, 17–23 (2019)

    Google Scholar 

  41. R. Arya, J.A. Wilson, H. Fujiwara, J. Vannest, A.W. Byars, L. Rozhkov, J.L. Leach, H.M. Greiner, J. Buroker, C. Scholle, P.S. Horn, N.E. Crone, D.F. Rose, F.T. Mangano, K.D. Holland, Electrocorticographic high-gamma modulation with passive listening paradigm for pediatric extraoperative language mapping. Epilepsia 59, 792–801 (2018)

    CAS  Google Scholar 

  42. N. Braun, S. Debener, A. Solle, C. Kranczioch, H. Hildebrandt, Biofeedback-based self-alert training reduces alpha activity and stabilizes accuracy in the sustained attention to response task. J. Clin. Exp. Neuropsychol. 37, 16–26 (2015)

    Google Scholar 

  43. I. Dziembowska, P. Izdebski, A. Rasmus, J. Brudny, M. Grzelczak, P. Cysewski, Effects of heart rate variability biofeedback on EEG alpha asymmetry and anxiety symptoms in male athletes: A pilot study. Appl. Psychophysiol. Biofeedback 41, 141–150 (2016)

    Google Scholar 

  44. L. Kranaster, C. Janke, C. Hoyer, A. Sartorius, Management of severe postictal agitation after electroconvulsive therapy with bispectrum electroencephalogram index monitoring: A case report. J. ECT 28, e9–e10 (2012)

    Google Scholar 

  45. E.A. Mukamel, K.F. Wong, M.J. Prerau, E.N. Brown, P.L. Purdon, Phase-based measures of cross-frequency coupling in brain electrical dynamics under general anesthesia. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 1981–1984 (2011)

    Google Scholar 

  46. W. Xie, B.M. Mallin, J.E. Richards, Development of brain functional connectivity and its relation to infant sustained attention in the first year of life. Dev. Sci. 22, e12703 (2019)

    Google Scholar 

  47. C.S. Ouyang, C.T. Chiang, R.C. Yang, R.C. Wu, H.C. Wu, L.C. Lin, Quantitative EEG findings and response to treatment with antiepileptic medications in children with epilepsy. Brain Dev. 40, 26–35 (2018)

    Google Scholar 

  48. S.L. Massey, H. Shou, R. Clancy, M. DiGiovine, M.P. Fitzgerald, F.W. Fung, J. Farrar, N.S. Abend, Interrater and Intrarater agreement in neonatal electroencephalogram background scoring. J. Clin. Neurophysiol. 36, 1–8 (2019)

    Google Scholar 

  49. F. Pisani, E. Pavlidis, The role of electroencephalogram in neonatal seizure detection. Expert. Rev. Neurother. 18, 95–100 (2018)

    CAS  Google Scholar 

  50. J.P. Fuentes, S. Villafaina, D. Collado-Mateo, R. de la Vega, N. Gusi, V.J. Clemente-Suarez, Use of biotechnological devices in the quantification of psychophysiological workload of professional chess players. J. Med. Syst. 42, 40 (2018)

    Google Scholar 

  51. I.A. Akbar, A.M. Rumagit, M. Utsunomiya, T. Morie, T. Igasaki, Three drowsiness categories assessment by electroencephalogram in driving simulator environment. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2017, 2904–2907 (2017)

    Google Scholar 

  52. L.J. Herrera, C.M. Fernandes, A.M. Mora, D. Migotina, R. Largo, A. Guillen, A.C. Rosa, Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. Int. J. Neural Syst. 23, 1350012 (2013)

    CAS  Google Scholar 

  53. T. Limpiti, B.D. Van Veen, H.T. Attias, S.S. Nagarajan, A spatiotemporal framework for estimating trial-to-trial amplitude variation in event-related MEG/EEG. I.E.E.E. Trans. Biomed. Eng. 56, 633–645 (2009)

    Google Scholar 

  54. D.T. Plante, M.R. Goldstein, J.D. Cook, R. Smith, B.A. Riedner, M.E. Rumble, L. Jelenchick, A. Roth, G. Tononi, R.M. Benca, M.J. Peterson, Effects of partial sleep deprivation on slow waves during non-rapid eye movement sleep: A high density EEG investigation. Clin. Neurophysiol. 127, 1436–1444 (2016)

    Google Scholar 

  55. J.C. Ehlen, F. Jefferson, A.J. Brager, M. Benveniste, K.N. Paul, Period-amplitude analysis reveals wake-dependent changes in the electroencephalogram during sleep deprivation. Sleep 36, 1723–1735 (2013)

    Google Scholar 

  56. L.L. Gustafsson, W.F. Ebling, E. Osaki, D.R. Stanski, Quantitation of depth of thiopental anesthesia in the rat. Anesthesiology 84, 415–427 (1996)

    CAS  Google Scholar 

  57. M. Jospin, P. Caminal, E.W. Jensen, H. Litvan, M. Vallverdu, M.M. Struys, H.E. Vereecke, D.T. Kaplan, Detrended fluctuation analysis of EEG as a measure of depth of anesthesia. I.E.E.E. Trans. Biomed. Eng. 54, 840–846 (2007)

    Google Scholar 

  58. A. Shalbaf, M. Saffar, J.W. Sleigh, R. Shalbaf, Monitoring the depth of anesthesia using a new adaptive Neurofuzzy system. IEEE J. Biomed. Health Inform. 22, 671–677 (2018)

    Google Scholar 

  59. J.S. Paul, C.B. Patel, H. Al-Nashash, N. Zhang, W.C. Ziai, M.A. Mirski, D.L. Sherman, Prediction of PTZ-induced seizures using wavelet-based residual entropy of cortical and subcortical field potentials. I.E.E.E. Trans. Biomed. Eng. 50, 640–648 (2003)

    Google Scholar 

  60. P. Maragos, J.F. Kaiser, T.F. Quatieri, Energy separation in signal modulations with application to speech analysis. IEEE Trans. Signal Process. 41, 3024–3051 (1993)

    Google Scholar 

  61. J.F. Kaiser, On a Simple Algorithm to Calculate the ‘Energy’ of a Signal (IEEE, 1990)

    Google Scholar 

  62. P. Maragos, A. Potamianos, Higher order differential energy operators. IEEE Signal Process. Lett. 2, 152–154 (1995)

    Google Scholar 

  63. J. Fang, L. Atlas, Quadratic detectors for energy estimation. IEEE Trans. Signal Process. 43, 2582–2594 (1995)

    Google Scholar 

  64. C.L. Nikias, A.P. Petropulu, Higher Order Spectral Analysis: A Nonlinear Signal Processing Framework (Prentice Hall, Englewood Cliffs, 1993)

    Google Scholar 

  65. E. Niedermeyer, D. Sherman, R. Geocadin, The burst suppression electroencephalogram. Clin. Electroencephalogr. 30, 99–105 (1999)

    CAS  Google Scholar 

  66. G. Chatrian, L. Bergamini, M. Dondey, D. Klass, M. Lennox-Butchthal, I. Petersen, A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr. Clin. Neurophysiol. 37, 538–548 (1974)

    Google Scholar 

  67. J. Fuzik, L. Gellert, G. Olah, J. Heredi, K. Kocsis, L. Knapp, D. Nagy, Z.T. Kincses, Z. Kis, T. Farkas, J. Toldi, Fundamental interstrain differences in cortical activity between Wistar and Sprague-Dawley rats during global ischemia. Neuroscience 228, 371–381 (2013)

    CAS  Google Scholar 

  68. Z. Liang, Y. Wang, Y. Ren, D. Li, L. Voss, J. Sleigh, X. Li, Detection of burst suppression patterns in EEG using recurrence rate. ScientificWorldJournal 2014, 295070 (2014)

    Google Scholar 

  69. M. Sarkela, S. Mustola, T. Seppanen, M. Koskinen, P. Lepola, K. Suominen, T. Juvonen, H. Tolvanen-Laakso, V. Jantti, Automatic analysis and monitoring of burst suppression in anesthesia. J. Clin. Monit. Comput. 17, 125–134 (2002)

    Google Scholar 

  70. F.L. da Silva, EEG analysis: Theory and practice, in Electroencephalography: Basic Principles, Clinical Applications and Related Fields, ed. by E. Niedermeyer, F. L. da Silva, (Williams & Wilkins, Baltimore, 2011)

    Google Scholar 

  71. S.L. Marple, Digital Spectral Analysis, 2nd edn. (Dover Publications, Inc., Mineola, 2019)

    Google Scholar 

  72. D.L. Sherman, M.K. Atit, R.G. Geocadin, S. Venkatesha, D.F. Hanley, A.L. Natarajan, N.V. Thakor, Diagnostic instrumentation for neural injury. IEEE Instrum. Meas. 5, 28–35 (2002)

    Google Scholar 

  73. J.P. Burg, Maximum entropy spectral analysis, 37th meeting of the Society of Exploration Geophysicists, 1967

    Google Scholar 

  74. V. Goel, A Novel Technique for EEG Analysis: Application to Neonatal Hypoxia-Asphyxia, BME (Johns Hopkins University, Baltimore, 1995)

    Google Scholar 

  75. S.M. Kay, Modern Spectral Estimation: Theory and Application (Prentice Hall, Englewood Cliffs, 1988)

    Google Scholar 

  76. V. Goel, A.M. Brambrink, A. Baykal, D.F. Hanley, N.V. Thakor, Dominant frequency analysis of EEG reveals brain’s response during injury and recovery. IEEE Trans. Biomed. Eng. 43, 1083–1092 (1996)

    CAS  Google Scholar 

  77. D.L. Sherman, A.M. Brambrink, R.N. Ichord, V.K. Dasika, R.C. Koehler, R.J. Traystman, D.F. Hanley, N.V. Thakor, Quantitative EEG during early recovery from hypoxic-ischemic injury in immature piglets: Burst occurrence and duration. Clin. Electroencephalogr. 30, 175–183 (1999)

    CAS  Google Scholar 

  78. V. Goel, A. Brambrink, D. Hanley, R. Koehler, N.V. Thakor, Dominant frequency analysis reveals Brain's response to injury and recovery. IEEE Trans. Biomed. Eng. 43, 1083–1092 (1996)

    CAS  Google Scholar 

  79. R.O. Schmidt, Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propagation AP-34, 276–280 (1986)

    Google Scholar 

  80. N. Makela, M. Stenroos, J. Sarvas, R.J. Ilmoniemi, Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization. NeuroImage 167, 73–83 (2018)

    Google Scholar 

  81. K.G. Mideksa, A. Singh, N. Hoogenboom, H. Hellriegel, H. Krause, A. Schnitzler, G. Deuschl, J. Raethjen, G. Schmidt, M. Muthuraman, Comparison of imaging modalities and source-localization algorithms in locating the induced activity during deep brain stimulation of the STN. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 105–108 (2016)

    CAS  Google Scholar 

  82. R. Kronland-Martinet, J. Morlet, A. Grossmann, Analysis of sound patterns through wavelet transforms. Intern. J. Pattern Rec. Artificial Intell. 1, 273–302 (1987)

    Google Scholar 

  83. C.S. Burrus, R.A. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer (Prentice Hall, Upper Saddle River, 1998)

    Google Scholar 

  84. J. Semmlow, B. Griffel, Wavelet Analysis Biosignal and Medical Image Processing (CRC Press, Boca Raton, 2014), pp. 217–246

    Google Scholar 

  85. F. Hlawatsch, G.F. Boudreaux-Bartels, Linear and quadratic time-frequency signal representations. IEEE Signal Proc. Mag. 9, 21–67 (1992)

    Google Scholar 

  86. O. Meste, H. Rix, P. Jane, P. Caminal, N.V. Thakor, Detection of late potentials by means of wavelet transform. I.E.E.E. Trans. Biomed. Eng. 41, 625–634 (1994)

    CAS  Google Scholar 

  87. O. Rioul, M. Vetterli, Wavelet theory: Mapping signal to a time-scale plane. IEEE Signal Process. Mag. 8, 14–39 (1991)

    Google Scholar 

  88. M. Holschneider, Wavelets: An Analysis Tool (Clarendon Press, Oxford, 1995)

    Google Scholar 

  89. M. Holschneider, R. Kronland-Martinet, P. Tchamitchian, A real-time algorithm for signal analysis with the help of the wavelet transform, in Wavelets: Time-Frequency Methods and Phase Space, ed. by J. Combes, A. Grossmann, P. Tchamitchian, (Springer, New York, 1989)

    Google Scholar 

  90. A.H. Najmi, J. Sadowsky, The continuous wavelet transform and variable resolution time-frequency analysis. Johns Hopkins APL Technical Digest 18, 134–140 (1994)

    Google Scholar 

  91. J. Sadowsky, The continuous wavelet transform: A tool for signal investigation and understanding. Johns Hopkins APL Technical Digest 15, 306–318 (1994)

    Google Scholar 

  92. H.R. Modi, Q. Wang, S. Gd, D. Sherman, E. Greenwald, A.V. Savonenko, R.G. Geocadin, N.V. Thakor, Intranasal post-cardiac arrest treatment with orexin-A facilitates arousal from coma and ameliorates neuroinflammation. PLoS One 12, e0182707 (2017)

    Google Scholar 

  93. D.L. Jones, R.G. Baraniuk, Efficient approximation of continuous wavelet transforms. Electron. Letters 27, 748–750 (1991)

    Google Scholar 

  94. M. Vetterli, J. Kovacevic, Wavelets and Subband Coding (Prentice Hall, Englewood Cliffs, 1995)

    Google Scholar 

  95. P. Goupillaud, A. Grossmann, J. Morlet, Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23, 85–102 (1984)

    Google Scholar 

  96. A.V. Oppenheim, R.W. Schaffer, Discrete Time Signal Processing (Prentice Hall, Englewood Cliffs, 1989)

    Google Scholar 

  97. F. Hlawatsch, G. Boudreaux-Bartels, Linear and quadratic time-frequency signal representations. IEEE Signal Process. Mag. 9, 21–62 (1992)

    Google Scholar 

  98. N.V. Thakor, D. Sherman, Wavelet (time-scale) analysis in biomedical signal processing, in Biomedical Engineering Handbook, ed. by J. D. Bronzino, (CRC Press, Boca Raton, 1995)

    Google Scholar 

  99. C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948)

    Google Scholar 

  100. A. Bezerianos, S. Tong, N.V. Thakor, Time-dependent entropy estimation of EEG rhythm changes following brain ischemia Ann. Biomed. Eng. 31, 221–232 (2003)

    CAS  Google Scholar 

  101. H.C. Shin, S. Tong, S. Yamashita, X. Jia, R.G. Geocadin, N.V. Thakor, Quantitative EEG and effect of hypothermia on brain recovery after cardiac arrest. I.E.E.E. Trans. Biomed. Eng. 53, 1016–1023 (2006)

    Google Scholar 

  102. M.H. Weil, L. Becker, T. Budinger, K. Kern, G. Nichol, I. Shechter, R. Traystman, H. Wiedemann, R. Wise, M. Weisfeldt, G. Sopko, Workshop executive summary report: Post-resuscitative and initial utility in life saving efforts (PULSE): June 29-30, 2000; Lansdowne Resort and Conference Center; Leesburg, VA. Circulation 103, 1182–1184 (2001)

    CAS  Google Scholar 

  103. P.A. Meaney, B.J. Bobrow, M.E. Mancini, J. Christenson, A.R. de Caen, F. Bhanji, B.S. Abella, M.E. Kleinman, D.P. Edelson, R.A. Berg, T.P. Aufderheide, V. Menon, M. Leary, the American Heart Association Emergency Cardiovascular Care Committee, Cpr Quality Summit Investigators, C.C.P. the Council on Cardiopulmonary, Resuscitation, Cardiopulmonary resuscitation quality: [corrected] improving cardiac resuscitation outcomes both inside and outside the hospital: a consensus statement from the American Heart Association. Circulation 128, 417–435 (2013)

    Google Scholar 

  104. M.S. Eisenberg, T.J. Mengert, Cardiac resuscitation. N. Engl. J. Med. 344, 1304–1313 (2001)

    CAS  Google Scholar 

  105. P. Safar, Cerebral resuscitation after cardiac arrest: A review. Circulation 74, IV138–IV153 (1986)

    CAS  Google Scholar 

  106. P. Vaagenes, M. Ginsberg, U. Ebmeyer, L. Ernster, M. Fischer, S.E. Gisvold, A. Gurvitch, K.A. Hossmann, E.M. Nemoto, A. Radovsky, J.W. Severinghaus, P. Safar, R. Schlichtig, F. Sterz, T. Tonnessen, R.J. White, F. Xiao, Y. Zhou, Cerebral resuscitation from cardiac arrest: Pathophysiologic mechanisms. Crit. Care Med. 24, S57–S68 (1996)

    CAS  Google Scholar 

  107. K. Berek, M. Jeschow, F. Aichner, The prognostication of cerebral hypoxia after out of hospital cardiac arrest in adults. Eur. Neurol. 37, 135–145 (1997)

    CAS  Google Scholar 

  108. I.G. Stiell, G.A. Wells, B.J. Field, D.W. Spaite, V.J. De Maio, R. Ward, D.P. Munkley, M.B. Lyver, L.G. Luinstra, T. Campeau, J. Maloney, E. Dagnone, Improved out-of-hospital cardiac arrest survival through the inexpensive optimization of an existing defibrillation program: OPALS study phase II. Ontario Prehospital Advanced Life Support. JAMA 281, 1175–1181 (1999)

    CAS  Google Scholar 

  109. N.R. Grubb, Managing out-of-hospital cardiac arrest survivors: 1. Neurological perspective. Heart 85, 6–8 (2001)

    CAS  Google Scholar 

  110. S.A. Mills, Risk factors for cerebral injury and cardiac surgery. Ann. Thorac. Surg. 59, 1296–1299 (1995)

    CAS  Google Scholar 

  111. B.C. White, L.I. Grossman, B.J. O'Neil, D.J. DeGarcia, R.W. Neumar, J.A. Raafols, G.S. Krause, Global brain ischemia and reperfusion. Ann. Emerg. Med. 27, 588–594 (1996)

    CAS  Google Scholar 

  112. W. Longstreth, T. Inui, L. Cobb, M. Copass, Neurologic recovery after out of hospital cardiac arrest. Ann. Int. Med. 98, 588–592 (1983)

    Google Scholar 

  113. D. Levy, D. Bate, J. Carrona, et al., Prognosis in nontraumatic coma. Ann. Int. Med. 94, 293–301 (1981)

    CAS  Google Scholar 

  114. AHA, Guidelines 2000 for Cardiopulmonary Resuscitation and Emergancy Cardiovascular Care. Circulation 102(suppl I), I-1-I-384 (2000)

    Google Scholar 

  115. L.B. Becker, M.L. Weisfeldt, M.H. Weil, T. Budinger, J. Carrico, K. Kern, G. Nichol, I. Shechter, R. Traystman, C. Webb, H. Wiedemann, R. Wise, G. Sopko, The PULSE initiative: Scientific priorities and strategic planning for resuscitation research and life saving therapies. Circulation 105, 2562–2570 (2002)

    Google Scholar 

  116. L. Arvidsson, S. Lindgren, L. Martinell, S. Lundin, C. Rylander, Target temperature 34 vs. 36 degrees C after out-of-hospital cardiac arrest – A retrospective observational study. Acta Anaesthesiol. Scand. 61, 1176–1183 (2017)

    CAS  Google Scholar 

  117. P.Y.K. Pang, G.H.L. Wee, M.J. Huang, A.E.E. Hoo, I.M. Tahir Sheriff, S.L. Lim, T.E. Tan, Y.J. Loh, V.T.T. Chao, J.L. Soon, K.L. Kerk, Z.H. Abdul Salam, Y.K. Sin, C.H. Lim, Therapeutic hypothermia may improve neurological outcomes in extracorporeal life support for adult cardiac arrest. Heart Lung Circ. 26, 817–824 (2017)

    Google Scholar 

  118. B.R. Scholefield, F.S. Silverstein, R. Telford, R. Holubkov, B.S. Slomine, K.L. Meert, J.R. Christensen, V.M. Nadkarni, J.M. Dean, F.W. Moler, Therapeutic hypothermia after paediatric cardiac arrest: Pooled randomized controlled trials. Resuscitation 133, 101–107 (2018)

    Google Scholar 

  119. J.P. Nolan, P.T. Morley, T.L. Hoek, R.W. Hickey, Therapeutic hypothermia after cardiac arrest. An advisory statement by the advancement life support task force of the international liaison committee on resuscitation. Resuscitation 57, 231–235 (2003)

    Google Scholar 

  120. X. Jia, M.A. Koenig, R. Nickl, G. Zhen, N.V. Thakor, R.G. Geocadin, Early electrophysiologic markers predict functional outcome associated with temperature manipulation after cardiac arrest in rats. Crit. Care Med. 36, 1909–1916 (2008)

    Google Scholar 

  121. D. Sherman, A. Brambrink, R. Ichord, V. Dasika, R. Koehler, R. Traystman, D. Hanley, N. Thakor, Quantitative EEG during early recovery from hypoxic-ischemia injury in immature piglets: Burst occurence and duration. Clin. Electroenceph, Accepted (1999)

    Google Scholar 

  122. D.L. Sherman, M.J. Hinich, N.V. Thakor, The higher order statistics of energy operators with applications to neurological signals, 1998 IEEE symposum on time-frequency and time-scale, Pittsburgh, 1998

    Google Scholar 

  123. H.A. Al-Nashash, J.S. Paul, W.C. Ziai, D.F. Hanley, N.V. Thakor, Wavelet entropy for subband segmentation of EEG during injury and recovery. Ann. Biomed. Eng. 31, 653–658 (2003)

    Google Scholar 

  124. H.A. Al-Nashash, N.V. Thakor, Monitoring of global cerebral ischemia using wavelet entropy rate of change. I.E.E.E. Trans. Biomed. Eng. 52, 2119–2122 (2005)

    CAS  Google Scholar 

  125. X. Kang, X. Jia, R.G. Geocadin, N.V. Thakor, A. Maybhate, Multiscale entropy analysis of EEG for assessment of post-cardiac arrest neurological recovery under hypothermia in rats. I.E.E.E. Trans. Biomed. Eng. 56, 1023–1031 (2009)

    Google Scholar 

  126. H.C. Shin, S. Tong, S. Yamashita, X. Jia, R.G. Geocadin, N.V. Thakor, Quantitative EEG Assessment of Brain Injury and Hypothermic Neuroprotection after Cardiac Arrest, EMBS Annual Intl. Conference, IEEE, New York City, 2006, pp. 6229–6232

    Google Scholar 

  127. X. Jia, M.A. Koenig, H.C. Shin, G. Zhen, S. Yamashita, N.V. Thakor, R.G. Geocadin, Quantitative EEG and neurological recovery with therapeutic hypothermia after asphyxial cardiac arrest in rats. Brain Res. 1111, 166–175 (2006)

    CAS  Google Scholar 

  128. T. Nakamachi, S. Endo, H. Ohtaki, L. Yin, D. Kenji, Y. Kudo, H. Funahashi, K. Matsuda, S. Shioda, Orexin-1 receptor expression after global ischemia in mice. Regul. Pept. 126, 49–54 (2005)

    CAS  Google Scholar 

  129. E.A. Irving, D.C. Harrison, A.J. Babbs, A.C. Mayes, C.A. Campbell, A.J. Hunter, N. Upton, A.A. Parsons, Increased cortical expression of the orexin-1 receptor following permanent middle cerebral artery occlusion in the rat. Neurosci. Lett. 324, 53–56 (2002)

    CAS  Google Scholar 

  130. Y. Yasuda, A. Takeda, S. Fukuda, H. Suzuki, M. Ishimoto, Y. Mori, H. Eguchi, R. Saitoh, H. Fujihara, K. Honda, T. Higuchi, Orexin a elicits arousal electroencephalography without sympathetic cardiovascular activation in isoflurane-anesthetized rats. Anesth. Analg. 97, 1663–1666 (2003)

    CAS  Google Scholar 

  131. I. Sato-Suzuki, I. Kita, Y. Seki, M. Oguri, H. Arita, Cortical arousal induced by microinjection of orexins into the paraventricular nucleus of the rat. Behav. Brain Res. 128, 169–177 (2002)

    CAS  Google Scholar 

  132. H.L. Dong, S. Fukuda, E. Murata, Z. Zhu, T. Higuchi, Orexins increase cortical acetylcholine release and electroencephalographic activation through orexin-1 receptor in the rat basal forebrain during isoflurane anesthesia. Anesthesiology 104, 1023–1032 (2006)

    CAS  Google Scholar 

  133. M.A. Koenig, X. Jia, X. Kang, A. Velasquez, N.V. Thakor, R.G. Geocadin, Intraventricular orexin-A improves arousal and early EEG entropy in rats after cardiac arrest. Brain Res. 1255, 153–161 (2009)

    CAS  Google Scholar 

  134. ASET 59th annual conference proceedings. Neurodiagn. J. 58, 235–256 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Homework

Homework

  1. 1.

    What are the chief uses of the EEG? Divide these into clinical/diagnostic as well as purely functional or cognitive and behavioral.

  2. 2.

    What are the different bands of the EEG and what are their primary uses? Why is the power spectrum such an important tool for discovering the state of the EEG at any one time?

  3. 3.

    Why is the EEG so effective for detecting epileptic seizures in living beings? What is the primary characteristic of the seizure signal that we measure in different anatomically connected areas of the brain?

  4. 4.

    What are the differences between parametric (model based, e.g., autoregressive) and nonparametric (e.g., FFT-based) spectral methods?

  5. 5.

    What are the chief characteristics of the MUSIC method of spectral analysis?

  6. 6.

    Define the normalized separation of the EEG? What spectral method is used to calculate it? How is it calculated? What is the optimum normalized separation?

  7. 7.

    Define IQ or the information quantity? How does IQ reflect the total entropy in the EEG? How does IQ magnitude prognosticate outcome after cardiac arrest in rats?

  8. 8.

    What are uses of the measurement index that we call wavelets? Is there any advantage that we glean from using wavelets from independent sinusoidal signals?

  9. 9.

    Why is it so important to monitor the brain after cardiac arrest occurs? Does the EEG offer any benefits for monitoring the brain after cardiac arrest?

  10. 10.

    What are primary effects of the stimulant and neuropeptide, orexin, on the EEG? What measures have been utilized to categorize the EEG after orexin treatment?

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sherman, D.L., Thakor, N.V. (2020). EEG Signal Processing: Theory and Applications. In: He, B. (eds) Neural Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-43395-6_3

Download citation

Publish with us

Policies and ethics