Wavelet analysis in neurodynamics

Павлов, А. Н.; Hramov, Aleksandr E.; Короновский, А. А.; Sitnikova, Evgenia; Makarov, Valerii A.; Ovchinnikov, A. A.

doi:10.3367/ufnr.0182.201209a.0905

Cited by 81 publications

(11 citation statements)

References 131 publications

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“…8,10,11 The disadvantage of this approach is that the threshold is set up arbitrarily and, therefore, its selection depends on the researcher's experience. …”

Section: Patterns Recognitionmentioning

confidence: 99%

“…Maximum of R I is associated with the best selection of the parameters ρ and f of the Morlet-function (8).…”

Section: R-functional: Estimation From Experimental Datamentioning

confidence: 99%

“…Among them, wavelet-based methods are related to the most powerful tools. [6][7][8][9] In order to provide recognition procedure with wavelets, one needs: 1) to select an appropriate basis constructed from a single soliton-like function; 2) to select appropriate scale and translation parameters associated with the most informative wavelet-coefficients.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

et al. 2013

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The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

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“…8,10,11 The disadvantage of this approach is that the threshold is set up arbitrarily and, therefore, its selection depends on the researcher's experience. …”

Section: Patterns Recognitionmentioning

confidence: 99%

“…Maximum of R I is associated with the best selection of the parameters ρ and f of the Morlet-function (8).…”

Section: R-functional: Estimation From Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

et al. 2013

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“…3,4 The presence of artifacts blocks the possibility to use the most widespread methods for routine and scientific analysis of time-frequency structure of EEG signals such as Fourier and Radon transform, wavelet analysis, Hilbert-Huang transform, etc. [6][7][8]21 Thus, artifact removing became a standard procedure in modern electroencephalographic studies and development of new efficient methods for artifact filtration is an important problem in EEG analysis.…”

Section: Introductionmentioning

confidence: 99%

Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

et al. 2017

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In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

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“…Traditionally such research demands to analyze the structure of point processes, i.e. the time-frequency dynamics analysis of neural reviews [1]- [6], in which information carriers are times of pulses (spikes) generating, but not their form [7]. The mechanisms that lead to the spikes generation are partially known [8].…”

Section: Introductionmentioning

confidence: 99%

Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model

Lytvyn¹,

Vysotska²,

Peleshchak³

et al. 2018

IJISA

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Abstract-Time-frequency and time dependence of the output signal morphology of nonlinear oscillator neuron based on Van der Pol model using analytical and numerical methods were investigated. Threshold effect neuron, when it is exposed to external non-stationary signals that vary in shape, frequency and amplitude was considered.Index Terms-Nonlinear oscillator neuron, frequency modulation, morphology of the information signal, resonance effect, encoding and decoding of information.

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Wavelet analysis in neurodynamics

Cited by 81 publications

References 131 publications

Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model

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