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Wavelets in Neuroscience pp 211–251Cite as

Time–Frequency Analysis of EEG: From Theory to Practice

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Abstract

This chapter describes some specific results of time–frequency analysis of EEG using the continuous wavelet transform. In this chapter we pay special attention to technical and computational details of time–frequency analysis of neurophysiological signals, i.e., produced by electrical brain activity in animals and humans. This chapter also presents continuous wavelet analysis of hypersynchronous rhythmic activity in multichannel EEG, characterizing the onset of absence epilepsy in patients.

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Notes

  1. 1.

    To estimate the relation between the characteristics of SWD and precursor activity, we used the following statistics. First, Pearson’s correlation coefficients were used to analyze the relation between the characteristics (f δ , f θ and corresponding MD) of SWD precursor activity and the frequency f b. The means per subject were used. Second, Student’s paired t-tests were used to test differences between the characteristics of SWD and precursor activity in the cortex and thalamus. Third, differences between states were analyzed with an ANOVA followed by post-hoc tests, according to Duncan [8].

  2. 2.

    This can happen when the level of neuronal network synchronization becomes too high (hyper-synchronization), or when cortical neurons express too strong an excitation (hyper-excitation) in response to the thalamic input.

  3. 3.

    Wistar rats are an outbred strain of albino rats belonging to the species Rattus norvegicus. This strain was developed at the Wistar Institute in 1906 for use in biological and medical research.

  4. 4.

    www.medicom-mtd.com/eng/index.htm

  5. 5.

    Note that intermittency of spontaneously occurring synchronized EEG oscillation epochs has already been described in humans [77].

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Authors and Affiliations

  1. Research and Education Center ‘Nonlinear Dynamics of Complex Systems’, Saratov State Technical University, Saratov, Russia

    Alexander E. Hramov, Alexey A. Koronovskii & Alexey N. Pavlov

  2. Department of Nonlinear Processes, Saratov State University, Saratov, Russia

    Alexander E. Hramov & Alexey A. Koronovskii

  3. Department of Applied Mathematics, Complutense University, Madrid, Spain

    Valeri A. Makarov

  4. Physics Department, Saratov State University, Saratov, Russia

    Alexey N. Pavlov

  5. Institute for Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia

    Evgenia Sitnikova

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Hramov, A.E., Koronovskii, A.A., Makarov, V.A., Pavlov, A.N., Sitnikova, E. (2015). Time–Frequency Analysis of EEG: From Theory to Practice. In: Wavelets in Neuroscience. Springer Series in Synergetics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43850-3_6

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