Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum

Alexey Pavlovich Kulaichev

doi:doi:10.11648/j.ijpbs.20160102.11

| Peer-Reviewed

Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum

Alexey Pavlovich Kulaichev

Published in International Journal of Psychological and Brain Sciences (Volume 1, Issue 2)

Received: 27 July 2016 Accepted: 9 August 2016 Published: 29 August 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This work performs the metrological comparison of two groups of indicators estimating the average level of EEG–potentials. The indirect spectral indicators (ISI) based on amplitude spectrum and power spectrum are contrasted with natural indicators (NI) based on period-amplitude analysis, on EEG absolute value and on EEG envelope. Five major results were obtained: 1) NI give almost equivalent estimates that differ from ISI significantly; 2) NI demonstrate smooth dynamics of their value change at successive epochs whereas ISI are subject to drastic and casual fluctuations; 3) ISI unlike NI do not possess the additivity property of statistical averaging, their estimates depending on number and length of averaged epochs can differ over 3 times in their values; 4) ISI at simulated signals with a known amplitude ratio give estimates that differ 1.4–1.55 times from true value whereas NI show the proper estimates; 5) ISI depending on differences between EEG spectral distribution give estimates which differ over 5 times in their ratios while NI show the same ratios which differ 1.38–3.7 times from ISI. The least reliable results in all comparisons are related to the power spectrum. These conclusions do not allow to qualify metrologically ISI as an analytical tool that is adequate for the nature and peculiarities of EEG potentials. Their use may lead to incompatibility of the results obtained by different researchers and clinicians.

Published in	International Journal of Psychological and Brain Sciences (Volume 1, Issue 2)
DOI	10.11648/j.ijpbs.20160102.11
Page(s)	21-28
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

EEG Amplitude, Amplitude Spectrum, Power Spectrum, Period-Amplitude Analysis, Envelope, Filtration, Metrology

References

[1]	Ivanov L. B. (2011) On informativeness of use of coherent analysis in clinical electro-encephalography. Zh Vyssh Nerv Deiat Im I P Pavlova. 61 (4): 499–512.
[2]	Kulaichev A. P. (2007) Computer electrophysiology and functional diagnostics, 4th edition. INFRAM, Moscow, 640 pp.
[3]	Kulaichev A. P. (2009) The informativeness of coherence analysis in EEG studies. Neurosci Behav Physiol. 59 (6): 757–767.
[4]	Kulaichev A. P. (2011) The method of correlation analysis of EEG synchronism and its possibilities. Zh Vyssh Nerv Deiat Im I P Pavlova. 61 (4): 485-498.
[5]	Kulaichev A. P. (2012) Comparative analysis of EEG correlation synchronism and EEG amplitude relationships in all-night sleep. Zh Vyssh Nerv Deiat Im I P Pavlova. 62 (1): 108-119.
[6]	Kulaichev A. P., Gorbachevskaya N. L. (2013) Differentiation of norm and disorders of schizophrenic spectrum by analysis of EEG correlation synchrony. J. Exp. Integr. Med. 3 (4): 267–278.
[7]	Kulaichev A. P., Iznak A. F., Iznak E., Kornilov V. V., Sorokin S, A. (2014) The Changes of EEG correlation synchrony at depressive deviations of advanced age. Zh Vyssh Nerv Deiat Im I P Pavlova. 64 (2): 1–9.
[8]	Deuschl G., Eisen A. (1999). Recommendation for the practice of clinical neurophysiology: Guidelines of the International Federation of clinical neurophysiology. Elsevier, Amsterdam, 304 pp.
[9]	Harris F. J. (1978) On the use of windows for harmonic analysis with the discrete Fourier transform. IEEE proceedings. (66) 1: 51–83.
[10]	Malik M., BiggerJ. T., Camm A. J., Kleiger R. E., Mallian i A., Moss A. J., Schwartz. P. J. (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J. 17 (3): 354–381.
[11]	Kayser J., Tenke C. E. (2010) In search of the Rosetta Stone for scalp EEG: Converging on reference–free techniques. Clin. Neurophysiol. 121: 1973–1975.
[12]	Ng S. C., Raveendran P. (2007) Comparison of different montages on to EEG classification. Biomed 06, IFMBE Proceedings 15, 365–368.
[13]	Orihovskaya K. B., Antonova-Rafi U. V. (2014) The effect of EEG spectrum leakage when using Barletta filter and at his absence. Int research J. (22) 3–2: 42–43.
[14]	Otnes R. K., Enochson L. (1987) Applied time series analisys, vol. 1: bsaic techniques. N. Y., 428 pp.
[15]	Randall R. B. (1987) Frequency Analysis, 3rd edition. Bruel & Kjaer, 344 рp.
[16]	Tong S., Thankor N. V. (2009) Quantitative EEG analysis methods and clinical applications. Artech House, 421 pp.

Cite This Article

Plain Text BibTeX RIS

APA Style

Alexey Pavlovich Kulaichev. (2016). Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum. International Journal of Psychological and Brain Sciences, 1(2), 21-28. https://doi.org/10.11648/j.ijpbs.20160102.11

Copy | Download

ACS Style

Alexey Pavlovich Kulaichev. Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum. Int. J. Psychol. Brain Sci. 2016, 1(2), 21-28. doi: 10.11648/j.ijpbs.20160102.11

Copy | Download

AMA Style

Alexey Pavlovich Kulaichev. Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum. Int J Psychol Brain Sci. 2016;1(2):21-28. doi: 10.11648/j.ijpbs.20160102.11

Copy | Download

@article{10.11648/j.ijpbs.20160102.11,
  author = {Alexey Pavlovich Kulaichev},
  title = {Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum},
  journal = {International Journal of Psychological and Brain Sciences},
  volume = {1},
  number = {2},
  pages = {21-28},
  doi = {10.11648/j.ijpbs.20160102.11},
  url = {https://doi.org/10.11648/j.ijpbs.20160102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijpbs.20160102.11},
  abstract = {This work performs the metrological comparison of two groups of indicators estimating the average level of EEG–potentials. The indirect spectral indicators (ISI) based on amplitude spectrum and power spectrum are contrasted with natural indicators (NI) based on period-amplitude analysis, on EEG absolute value and on EEG envelope. Five major results were obtained: 1) NI give almost equivalent estimates that differ from ISI significantly; 2) NI demonstrate smooth dynamics of their value change at successive epochs whereas ISI are subject to drastic and casual fluctuations; 3) ISI unlike NI do not possess the additivity property of statistical averaging, their estimates depending on number and length of averaged epochs can differ over 3 times in their values; 4) ISI at simulated signals with a known amplitude ratio give estimates that differ 1.4–1.55 times from true value whereas NI show the proper estimates; 5) ISI depending on differences between EEG spectral distribution give estimates which differ over 5 times in their ratios while NI show the same ratios which differ 1.38–3.7 times from ISI. The least reliable results in all comparisons are related to the power spectrum. These conclusions do not allow to qualify metrologically ISI as an analytical tool that is adequate for the nature and peculiarities of EEG potentials. Their use may lead to incompatibility of the results obtained by different researchers and clinicians.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum
AU  - Alexey Pavlovich Kulaichev
Y1  - 2016/08/29
PY  - 2016
N1  - https://doi.org/10.11648/j.ijpbs.20160102.11
DO  - 10.11648/j.ijpbs.20160102.11
T2  - International Journal of Psychological and Brain Sciences
JF  - International Journal of Psychological and Brain Sciences
JO  - International Journal of Psychological and Brain Sciences
SP  - 21
EP  - 28
PB  - Science Publishing Group
SN  - 2575-1573
UR  - https://doi.org/10.11648/j.ijpbs.20160102.11
AB  - This work performs the metrological comparison of two groups of indicators estimating the average level of EEG–potentials. The indirect spectral indicators (ISI) based on amplitude spectrum and power spectrum are contrasted with natural indicators (NI) based on period-amplitude analysis, on EEG absolute value and on EEG envelope. Five major results were obtained: 1) NI give almost equivalent estimates that differ from ISI significantly; 2) NI demonstrate smooth dynamics of their value change at successive epochs whereas ISI are subject to drastic and casual fluctuations; 3) ISI unlike NI do not possess the additivity property of statistical averaging, their estimates depending on number and length of averaged epochs can differ over 3 times in their values; 4) ISI at simulated signals with a known amplitude ratio give estimates that differ 1.4–1.55 times from true value whereas NI show the proper estimates; 5) ISI depending on differences between EEG spectral distribution give estimates which differ over 5 times in their ratios while NI show the same ratios which differ 1.38–3.7 times from ISI. The least reliable results in all comparisons are related to the power spectrum. These conclusions do not allow to qualify metrologically ISI as an analytical tool that is adequate for the nature and peculiarities of EEG potentials. Their use may lead to incompatibility of the results obtained by different researchers and clinicians.
VL  - 1
IS  - 2
ER  -

Copy | Download

Author Information

Alexey Pavlovich Kulaichev

Department of Biological Science, Moscow State University, Moscow, Russia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Alexey Pavlovich Kulaichev. (2016). Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum. International Journal of Psychological and Brain Sciences, 1(2), 21-28. https://doi.org/10.11648/j.ijpbs.20160102.11

Copy | Download

ACS Style

Alexey Pavlovich Kulaichev. Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum. Int. J. Psychol. Brain Sci. 2016, 1(2), 21-28. doi: 10.11648/j.ijpbs.20160102.11

Copy | Download

AMA Style

Alexey Pavlovich Kulaichev. Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum. Int J Psychol Brain Sci. 2016;1(2):21-28. doi: 10.11648/j.ijpbs.20160102.11

Copy | Download

@article{10.11648/j.ijpbs.20160102.11,
  author = {Alexey Pavlovich Kulaichev},
  title = {Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum},
  journal = {International Journal of Psychological and Brain Sciences},
  volume = {1},
  number = {2},
  pages = {21-28},
  doi = {10.11648/j.ijpbs.20160102.11},
  url = {https://doi.org/10.11648/j.ijpbs.20160102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijpbs.20160102.11},
  abstract = {This work performs the metrological comparison of two groups of indicators estimating the average level of EEG–potentials. The indirect spectral indicators (ISI) based on amplitude spectrum and power spectrum are contrasted with natural indicators (NI) based on period-amplitude analysis, on EEG absolute value and on EEG envelope. Five major results were obtained: 1) NI give almost equivalent estimates that differ from ISI significantly; 2) NI demonstrate smooth dynamics of their value change at successive epochs whereas ISI are subject to drastic and casual fluctuations; 3) ISI unlike NI do not possess the additivity property of statistical averaging, their estimates depending on number and length of averaged epochs can differ over 3 times in their values; 4) ISI at simulated signals with a known amplitude ratio give estimates that differ 1.4–1.55 times from true value whereas NI show the proper estimates; 5) ISI depending on differences between EEG spectral distribution give estimates which differ over 5 times in their ratios while NI show the same ratios which differ 1.38–3.7 times from ISI. The least reliable results in all comparisons are related to the power spectrum. These conclusions do not allow to qualify metrologically ISI as an analytical tool that is adequate for the nature and peculiarities of EEG potentials. Their use may lead to incompatibility of the results obtained by different researchers and clinicians.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum
AU  - Alexey Pavlovich Kulaichev
Y1  - 2016/08/29
PY  - 2016
N1  - https://doi.org/10.11648/j.ijpbs.20160102.11
DO  - 10.11648/j.ijpbs.20160102.11
T2  - International Journal of Psychological and Brain Sciences
JF  - International Journal of Psychological and Brain Sciences
JO  - International Journal of Psychological and Brain Sciences
SP  - 21
EP  - 28
PB  - Science Publishing Group
SN  - 2575-1573
UR  - https://doi.org/10.11648/j.ijpbs.20160102.11
AB  - This work performs the metrological comparison of two groups of indicators estimating the average level of EEG–potentials. The indirect spectral indicators (ISI) based on amplitude spectrum and power spectrum are contrasted with natural indicators (NI) based on period-amplitude analysis, on EEG absolute value and on EEG envelope. Five major results were obtained: 1) NI give almost equivalent estimates that differ from ISI significantly; 2) NI demonstrate smooth dynamics of their value change at successive epochs whereas ISI are subject to drastic and casual fluctuations; 3) ISI unlike NI do not possess the additivity property of statistical averaging, their estimates depending on number and length of averaged epochs can differ over 3 times in their values; 4) ISI at simulated signals with a known amplitude ratio give estimates that differ 1.4–1.55 times from true value whereas NI show the proper estimates; 5) ISI depending on differences between EEG spectral distribution give estimates which differ over 5 times in their ratios while NI show the same ratios which differ 1.38–3.7 times from ISI. The least reliable results in all comparisons are related to the power spectrum. These conclusions do not allow to qualify metrologically ISI as an analytical tool that is adequate for the nature and peculiarities of EEG potentials. Their use may lead to incompatibility of the results obtained by different researchers and clinicians.
VL  - 1
IS  - 2
ER  -

Copy | Download