Spectral analysis of the EEG: comparison of various spectral parameters

Electroencephalography and Clinical Neurophysiology
P MatthisC Benninger

Abstract

One of the main problems in spectral analysis of the EEG consists of finding relevant parameters which are capable of summarizing all information included in the power spectrum. The mathematical properties and the clinical applicability of 4 sets of parameters were compared: (1) percentage of power within the conventional frequency ranges, (2) mobility and complexity (Hjorth), (3) the slope parameter (Dumermuth), (4) the coefficients of the autoregressive model. They were applied to the EEG changes in a child with brain stem tumour during cerebral radiotherapy. The statistical analysis suggests: (I) there are only minor differences in describing the EEG course between the conventional frequency ranges, mobility and the slope parameter, (II) the complexity is less satisfactory, (III) the first autoregressive coefficient of the AR model yields the best results.

References

Jan 1, 1977·European Neurology·G DumermuthB Lange

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Citations

Jan 1, 1984·Electroencephalography and Clinical Neurophysiology·C BenningerD Scheffner
Sep 1, 1991·Electroencephalography and Clinical Neurophysiology·T InouyeM Hirano
May 1, 1987·Journal of Pharmacological Methods·L BeaniS Tanganelli
Mar 1, 1992·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·C W HatskevichV I Maloletnev
May 1, 1988·Toxicology·J KubátM Vanícková
May 5, 2001·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·A R ClarkeM Selikowitz
Feb 1, 2003·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Robert J BarryStuart J Johnstone
May 29, 2003·IEEE Transactions on Bio-medical Engineering·Scott V Notley, Stephen J Elliott
Dec 8, 2010·International Journal of Developmental Neuroscience : the Official Journal of the International Society for Developmental Neuroscience·Dennis GmehlinRieke Oelkers-Ax

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