Development of the EEG of school-age children and adolescents. I. Analysis of band power

doi:10.1016/0013-4694(88)90204-0

Electroencephalography and Clinical Neurophysiology

Volume 69, Issue 2, February 1988, Pages 91-99

https://doi.org/10.1016/0013-4694(88)90204-0 Get rights and content

Abstract

Development in quantitative EEG parameters is studied for a sample of 158 normal children and adolescents aged 6–17 years. This is of interest both for increasing basic knowledge of human neurophysiology and for obtaining age standardized norms, useful in clinical research and applications.

After selecting an appropriate epoch and correcting for EOG artifacts, the EEG at 8 derivations was submitted to spectral analysis in order to extract broad-band parameters in absolute and relative power. Change in EEG band power across age was quantified by polynomial regression analysis. This opened automatically the possibility to obtain age-standardized EEG norms. Development was for most EEG parameters non-linear, with more pronounced changes for absolute than for relative power. No sex differences and no pubertal spurt could be identified in contrast to most somatic quantities. A detailed statistical analysis revealed, however, that this might be due to using cross-sectional data. All bands except for alpha₂ decreased in absolute power, whereas the fast bands increased and the slow bands decreased in relative power. Strong evidence was found for a substituting process between theta activity and fast alpha activity.

References (18)

C. Benninger et al.
EEG development of healthy boys and girls. Results of a longitudinal study
Electroenceph. clin. Neurophysiol.
(1984)
R.E. Dustman et al.
Power spectral analysis and cortical coupling of EEG for young and old normal adults
Neurobiol. Aging
(1985)
Th. Gasser et al.
Transformations towards the normal distribution of broadband spectral parameters of the EEG
Electroenceph. clin. Neurophysiol.
(1982)
Th. Gasser et al.
The transfer of EOG activity into the EEG for eyes open and closed
Electroenceph. clin. Neurophysiol.
(1985)
Th. Gasser et al.
Development of the EEG of school-age children and adolescents. II. Topography
Electroenceph. clin. Neurophysiol.
(1988)
A. Katada et al.
Developmental characteristics of normal and mentally retarded children's EEGs
Electroenceph. clin. Neurophysiol.
(1981)
M. Matsuura et al.
Age development and sex differences of various EEG elements in healthy children and adults — quantification by a computerized wave form recognition method
Electroenceph. clin. Neurophysiol.
(1985)
P. Matthis et al.
Changes in the background activity of the electroencephalogram according to age
Electroenceph. clin. Neurophysiol.
(1980)
J. Möcks et al.
How to select epochs of the EEG at rest for quantitative analysis
Electroenceph. clin. Neurophysiol.
(1984)

There are more references available in the full text version of this article.

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^☆: This work has been performed as part of the research program of the Sonderforschungsbereich 116 (Project M2) and the Sonderforschungsbereich 123 (Project B1), both at the University of Heidelberg, and was made possible by financial support from the Deutsche Forschungsgemeinschaft.

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Main articleDevelopment of the EEG of school-age children and adolescents. I. Analysis of band power☆

Abstract

Electroenceph. clin. Neurophysiol.

Neurobiol. Aging

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Main article
Development of the EEG of school-age children and adolescents. I. Analysis of band power☆