Analysis of Electroencephalography (EEG) Signals Based on the Haar Wavelet Transformation

Y. Contoyiannis (), P. Papadopoulos (), S. M. Potirakis (), M. Kampitakis (), N. L. Matiadou () and E. Kosmidis ()
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Y. Contoyiannis: West Attica University (UNIWA)
P. Papadopoulos: West Attica University (UNIWA)
S. M. Potirakis: West Attica University (UNIWA)
M. Kampitakis: Network Major Installations Department
N. L. Matiadou: West Attica University (UNIWA)
E. Kosmidis: Aristotle University of Thessaloniki

A chapter in Approximation and Computation in Science and Engineering, 2022, pp 157-166 from Springer

Abstract: Abstract EEG recordings give extremely noisy signals that do not allow classical methods to clearly display such as the existence of power laws or even more so the critical state that is a signature of the normal operation of biological tissues (Contoyiannis et al., Phys Rev Lett 93:098101, 2004; Contoyiannis et al., Nat Hazards Earth Syst Sci 13:125–139, 2013; Kosmidis et al., Eur J Neurosci, 2018. https://doi.org/10.1111/ejn.14117 ). We have recently introduced a method, based on Haar wavelet transformation (Contoyiannis et al. Phys. Rev. E 101:052104, 2020), that completely ignores noise and thus can reveal the information of the power law in EEGs. It calculates the exponent of the power law and thus gives us the ability to determine whether the brain is in critical state in terms of physics, i.e., in a state of normal biological function. Pathological conditions, such as epilepsy, are quantified through this method so we can observe their evolution.

Keywords: EEG; Criticality; Power law; Intermittency; Haar wavelet transformation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-84122-5_10

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