Skip to main content
SpringerLink
Log in

EEG Spectral Analysis

  • Chapter
  • First Online:
Computational EEG Analysis

Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

Electroencephalogram (EEG) spectral analysis quantifies the amount of rhythmic (or oscillatory) activity of different frequency in EEGs. Based on numerous studies that reported significant relationship between the EEG spectrum and human behavior, cognitive state, or mental illnesses, EEG spectral analysis is now accepted as one of the principal analysis methods in the field of neuroscience. Despite the tremendous amount of research related to its usefulness, EEG spectral analysis still exhibits inconsistent results among studies. This might be partly because of the various methodological decisions the researchers have to make during EEG spectral analysis. Indeed, there is no standardized analysis procedure. In this chapter, we cover some background principles of spectral analysis and introduce important issues that researchers must consider during EEG spectral analysis.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
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

References

  1. L.I. Aftanas, S.A. Golocheikine, Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation. Neurosci. Lett. 310, 57–60 (2001). https://doi.org/10.1016/S0304-3940(01)02094-8

    Article  Google Scholar 

  2. American Clinical Neurophysiology, Guideline fourteen: guidelines for recording clinical EEG on digital media. J. Clin. Neurophysiol. 11, 114–115 (1994)

    Article  Google Scholar 

  3. American Clinical Neurophysiology, Guideline twelve: guidelines for long-term monitoring for epilepsy. J. Clin. Neurophysiol. 11, 88–110 (1994)

    Article  Google Scholar 

  4. J.S. Barlow, The Electroencephalogram: Its Patterns and Origins (MIT Press, Cambridge, 1993), pp. 213–220

    Google Scholar 

  5. R.J. Barry, A.R. Clarke, S.J. Johnstone, A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qual. Quant. Electroencephalogr. Clin. Neurophysiol. 114, 171–183 (2003). https://doi.org/10.1016/S1388-2457(02)00362-0

    Article  Google Scholar 

  6. H. Berger, Über das Elektrenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten 87, 527–570 (1929). https://doi.org/10.1007/bf01797193

    Article  Google Scholar 

  7. M.A. Brazier, Preliminary proposal for an EEG terminology by the Terminology Committee of the International Federation for Electroencephalography and Clinical Neurophysiology. Electroencephalogr. Clin. Neurophysiol. 13, 646–650 (1961)

    Article  Google Scholar 

  8. T.H. Budzynski, H.K. Budzynski, J.R. Evans, A. Abarbanel, Introduction to Quantitative EEG and Neurofeedback: Advanced Theory and Applications (Elsevier, 2009)

    Google Scholar 

  9. J. Carrier, S. Land, D.J. Buysse, D.J. Kupfer, T.H. Monk, The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old). Psychophysiology 38, 232–242 (2001). https://doi.org/10.1111/1469-8986.3820232

    Article  Google Scholar 

  10. J.W. Cooley, J.W. Tukey, An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19, 297–301 (1965). https://doi.org/10.2307/2003354

    Article  MathSciNet  MATH  Google Scholar 

  11. J. Dien, Issues in the application of the average reference: review, critiques, and recommendations. Behav. Res. Methods Instrum. Comput. 30, 34–43 (1998). https://doi.org/10.3758/bf03209414

    Article  Google Scholar 

  12. G. Dietsch, Fourier-analyse von Elektrencephalogrammen des Menschen Pflüger’s Archiv für die gesamte. Physiologie des Menschen und der Tiere 230, 106–112 (1932). https://doi.org/10.1007/bf01751972

    Article  Google Scholar 

  13. P. Djuric, S. Kay, Spectrum Estimation and Modeling, in The Digital Signal Processing Hanbook, 2nd edn., ed. by V. Madisetti (CRC Press, Boca Raton, 2017)

    Google Scholar 

  14. R.E. Dustman, D.E. Shearer, R.Y. Emmerson, Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency. Clin. Neurophysiol. 110, 1399–1409 (1999). https://doi.org/10.1016/S1388-2457(99)00102-9

    Article  Google Scholar 

  15. I. Feinberg, R.L. Koresko, N. Heller, EEG sleep patterns as a function of normal and pathological aging in man. J. Psychiatr. Res. 5, 107–144 (1967). https://doi.org/10.1016/0022-3956(67)90027-1

    Article  Google Scholar 

  16. I.H. Gotlib, EEG alpha asymmetry, depression, and cognitive functioning. Cogn. Emot. 12, 449–478 (1998). https://doi.org/10.1080/026999398379673

    Article  Google Scholar 

  17. S. Gudmundsson, T.P. Runarsson, S. Sigurdsson, G. Eiriksdottir, K. Johnsen, Reliability of quantitative EEG features. Clin. Neurophysiol. 118, 2162–2171 (2007). https://doi.org/10.1016/j.clinph.2007.06.018

    Article  Google Scholar 

  18. T.M. Itil, Qualitative and quantitative EEG findings in schizophrenia. Schizophr. Bull. 3, 61–79 (1977). https://doi.org/10.1093/schbul/3.1.61

    Article  Google Scholar 

  19. H. Jaseja, B. Jaseja, EEG spike versus EEG sharp wave: differential clinical significance in epilepsy. Epilepsy Behav. 25, 137 (2012). https://doi.org/10.1016/j.yebeh.2012.05.023

    Article  Google Scholar 

  20. O. Jensen, J. Kaiser, J.-P. Lachaux, Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 30, 317–324 (2007). https://doi.org/10.1016/j.tins.2007.05.001

    Article  Google Scholar 

  21. B.W. Jervis, M. Coelho, G.W. Morgan, Spectral analysis of EEG responses. Med. Biol. Eng. Compu. 27, 230 (1989). https://doi.org/10.1007/bf02441479

    Article  Google Scholar 

  22. D.A. Kaiser, QEEG: State of the art, or state of confusion. J. Neurother. 4, 57–75 (2000). https://doi.org/10.1300/J184v04n02_07

    Article  Google Scholar 

  23. D.A. Kaiser, Basic principles of quantitative EEG. J. Adult Dev. 12, 99–104 (2005). https://doi.org/10.1007/s10804-005-7025-9

    Article  Google Scholar 

  24. W. Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29, 169–195 (1999). https://doi.org/10.1016/S0165-0173(98)00056-3

    Article  Google Scholar 

  25. M.J. Levy, Effect of epoch length on power spectrum analysis of the EEG. Anesthesiology 66, 489–495 (1987)

    Article  Google Scholar 

  26. V. Madisetti, D.B. Williams, The Digital Signal Processing Handbook. The Electrical Engineering Handbook Series (CRC Press; IEEE Press, Boca Raton; New York, 1998)

    Google Scholar 

  27. S. Makeig, M. Inlow, Lapse in alertness: coherence of fluctuations in performance and EEG spectrum. Electroencephalogr. Clin. Neurophysiol. 86, 23–35 (1993). https://doi.org/10.1016/0013-4694(93)90064-3

    Article  Google Scholar 

  28. J. Muthuswamy, N.V. Thakor, Spectral analysis methods for neurological signals. J. Neurosci. Methods 83, 1–14 (1998). https://doi.org/10.1016/S0165-0270(98)00065-X

    Article  Google Scholar 

  29. M.R. Nuwer, D. Lehmann, F.L. da Silva, S. Matsuoka, W. Sutherling, J.F. Vibert, IFCN guidelines for topographic and frequency analysis of EEGs and EPs. The International Federation of Clinical Neurophysiology. Electroencephalogr. Clin. Neurophysiol. Suppl. 52, 15–20 (1999)

    Google Scholar 

  30. A. Oppenheim, R. Schafer, Discrete-Time Signal Processing, 3rd ed. (Prentice-Hall Signal Processing Series) (Prentice Hall, 2009). Doi: citeulike-article-id:10412772

    Google Scholar 

  31. J. Pardey, S. Roberts, L. Tarassenko, A review of parametric modelling techniques for EEG analysis. Med. Eng. Phys. 18, 2–11 (1996). https://doi.org/10.1016/1350-4533(95)00024-0

    Article  Google Scholar 

  32. B. Pesaran, Spectral analysis for neural signals, in P. Mitra (ed.) Neural Signal Processing: Quantitative Analysis of Neural Activity. Society for Neuroscience (2008), pp. 1–12

    Google Scholar 

  33. K.M.M. Prabhu, V.U. Reddy, J.P. Agrawal, Performance comparison of data windows. Electron. Lett. 13, 600–601 (1977). https://doi.org/10.1049/el:19770431

    Article  ADS  Google Scholar 

  34. J. Proakis, D. Manolakis, Digital Signal Processing, 4th ed. (Prentice Hall, 2006). Doi: citeulike-article-id:1806381

    Google Scholar 

  35. S. Sanei, J.A. Chambers, S. Sanei, J.A. Chambers, Fundamentals of EEG Signal Processing, in EEG Signal Processing. (Wiley, New York, 2007), pp. 35–125. https://doi.org/10.1002/9780470511923.ch2

  36. C.E. Shannon, Communication in the presence of noise. Proc. IRE 37, 10–21 (1949). https://doi.org/10.1109/jrproc.1949.232969

    Article  MathSciNet  Google Scholar 

  37. N.S. Singhal, J.E. Sullivan, Continuous spike-wave during slow wave sleep and related conditions ISRN. Neurology 2014, 619079 (2014). https://doi.org/10.1155/2014/619079

    Article  Google Scholar 

  38. S.J.M. Smith, EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry 76, ii2–ii7 (2005). https://doi.org/10.1136/jnnp.2005.069245

  39. T. Takahashi et al., Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. Int. J. Psychophysiol. 55, 199–207 (2005)

    Article  Google Scholar 

  40. D.J. Thomson, Spectrum estimation and harmonic analysis. Proc. IEEE 70, 1055–1096 (1982). https://doi.org/10.1109/PROC.1982.12433

    Article  ADS  Google Scholar 

  41. J. Tzyy-Ping, S. Makeig, M. Stensmo, T.J. Sejnowski, Estimating alertness from the EEG power spectrum. IEEE Trans. Biomed. Eng. 44, 60–69 (1997). https://doi.org/10.1109/10.553713

    Article  Google Scholar 

  42. P. Welch, The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15, 70–73 (1967). https://doi.org/10.1109/TAU.1967.1161901

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea

    Do-Won Kim

  2. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea

    Chang-Hwan Im

Authors
  1. Do-Won Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang-Hwan Im
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Do-Won Kim .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Hanyang University, Seongdong-gu, Seoul, Korea (Republic of)

    Chang-Hwan Im

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kim, DW., Im, CH. (2018). EEG Spectral Analysis. In: Im, CH. (eds) Computational EEG Analysis. Biological and Medical Physics, Biomedical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-0908-3_3

Download citation

Publish with us

Policies and ethics

Search

Navigation