Comput Math Methods Med - Higher-order spectrum in understanding nonlinearity in EEG rhythms.

Tópicos

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Resumo

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.

Resumo Limpo

fundament natur brain electr activ record electroencephalogram eeg remain unknown linear stochast model spectral estim common method analysi eeg robust simplic interpret appar associ rhythmic behavior pattern natur paper extend use higherord spectrum order indic hidden characterist eeg signal simpli aris random process higherord spectrum extens fourier spectrum use higher moment spectral estim essenti nullifi gaussian random effect therefor can reveal nongaussian nonlinear characterist complex pattern eeg time seri paper demonstr distinguish featur bispectr analysi chaotic system filter nois normal background eeg activ bispectrum analysi detect nonlinear interact howev quantifi coupl strength squar bicoher nonredund region estim demonstr nonlinear coupl bicoher valu minim white gaussian nois wgns filter nois higher bicoher valu chaotic time seri normal background eeg activ indic nonlinear coupl system paper show util bispectr method analyt tool understand neural process under human eeg pattern

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