Wavelet Transform and Bispectrum Applied to Acoustic Emission Signals from Adherence Scratch-Tests on Corroded Galvanized Coatings

The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG) remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs) and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

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Wavelet Transform and Bispectrum Applied to Acoustic Emission Signals from Adherence Scratch-Tests on Corroded Galvanized Coatings

Cited by 3 publications

References 9 publications

A novel Bayesian approach to acoustic emission data analysis

A novel Bayesian approach to acoustic emission data analysis

Use of higher order spectrum in characterizing nonlinear interactions in human brain signals

Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

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